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Publications
2017 |
Gujar, Sujit Prakash and Faltings, Boi Auction Based Mechanisms for Dynamic Task Assignments in Expert Crowdsourcing Book Chapter et al., Ceppi S (Ed.): pp. 50-65, Springer, 2017, ISBN: 978-3-319-54229-4. Abstract | Links | BibTeX | Citation @inbook{Gujar2017, title = {Auction Based Mechanisms for Dynamic Task Assignments in Expert Crowdsourcing}, author = {Sujit Prakash Gujar and Boi Faltings}, editor = {S. Ceppi et al.}, isbn = {978-3-319-54229-4}, year = {2017}, date = {2017-00-00}, journal = {Agent-Mediated Electronic Commerce}, pages = {50-65}, publisher = {Springer}, series = {LNBIP}, abstract = {Crowdsourcing marketplaces link large populations of workers to an even larger number of tasks. Thus, it is necessary to have mechanisms for matching workers with interesting and suitable tasks. Earlier work has addressed the problem of finding optimal workers for a given set of tasks. However, workers also have preferences and will stay with a platform only if it gives them interesting tasks. We therefore analyze several matching mechanisms that take into account workers’ preferences as well.We propose that the workers pay premiums to get preferred matches and auction-based models where preferences are expressed through variations of the payment for a task. We analyze the properties of two matching different mechanisms: Split Dynamic VCG (SDV) and e-Auction. We compare both the mechanisms with Arrival Priority Serial Dictatorship (APSD) empirically for efficiency.}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } Crowdsourcing marketplaces link large populations of workers to an even larger number of tasks. Thus, it is necessary to have mechanisms for matching workers with interesting and suitable tasks. Earlier work has addressed the problem of finding optimal workers for a given set of tasks. However, workers also have preferences and will stay with a platform only if it gives them interesting tasks. We therefore analyze several matching mechanisms that take into account workers’ preferences as well.We propose that the workers pay premiums to get preferred matches and auction-based models where preferences are expressed through variations of the payment for a task. We analyze the properties of two matching different mechanisms: Split Dynamic VCG (SDV) and e-Auction. We compare both the mechanisms with Arrival Priority Serial Dictatorship (APSD) empirically for efficiency. Gujar, Sujit Prakash and Faltings, Boi, Auction Based Mechanisms for Dynamic Task Assignments in Expert Crowdsourcing, et al., Ceppi S (Ed.): pp. 50-65, Springer, 2017, ISBN: 978-3-319-54229-4. |
2016 |
Radanovic, Goran; Faltings, Boi and Jurca, Radu Incentives for Effort in Crowdsourcing using the Peer Truth Serum Journal Article Acm Transactions on Intelligent Systems and Technology, 7 (4), pp. 48:1-28, 2016. Abstract | Links | BibTeX | Citation @article{Radanvoic 2016b, title = {Incentives for Effort in Crowdsourcing using the Peer Truth Serum}, author = {Goran Radanovic and Boi Faltings and Radu Jurca}, doi = {http://dx.doi.org/10.1145/2856102}, year = {2016}, date = {2016-00-00}, journal = {Acm Transactions on Intelligent Systems and Technology}, volume = {7}, number = {4}, pages = {48:1-28}, abstract = {Crowdsourcing is widely proposed as a method to solve large variety of judgement tasks, such as classifying website content, peer grading in online courses, or collecting real-world data. As the data reported by workers cannot be verified, there is a tendency to report random data without actually solving the task. This can be countered by making the reward for an answer depend on its consistency with answers given by other workers, an approach called peer consistency. However, it is obvious that the best strategy in such schemes is for all workers to report the same answer without solving the task. Dasgupta and Ghosh (WWW 2013) show that in some cases exerting high effort can be encouraged in the highest-paying equilibrium. In this paper we present a general mechanism that implements this idea and is applicable to most crowdsourcing settings. Furthermore, we experimentally test the novel mechanism, and validate its theoretical properties.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Crowdsourcing is widely proposed as a method to solve large variety of judgement tasks, such as classifying website content, peer grading in online courses, or collecting real-world data. As the data reported by workers cannot be verified, there is a tendency to report random data without actually solving the task. This can be countered by making the reward for an answer depend on its consistency with answers given by other workers, an approach called peer consistency. However, it is obvious that the best strategy in such schemes is for all workers to report the same answer without solving the task. Dasgupta and Ghosh (WWW 2013) show that in some cases exerting high effort can be encouraged in the highest-paying equilibrium. In this paper we present a general mechanism that implements this idea and is applicable to most crowdsourcing settings. Furthermore, we experimentally test the novel mechanism, and validate its theoretical properties. Radanovic, Goran; Faltings, Boi and Jurca, Radu, Incentives for Effort in Crowdsourcing using the Peer Truth Serum, Acm Transactions on Intelligent Systems and Technology, 7 (4), pp. 48:1-28, 2016. |
Chatzopoulos, Dimitris; Gujar, Sujit Prakash; Faltings, Boi and Hui, Pan LocalCoin: An ad-hoc payment scheme for areas with high connectivity: poster. Conference ACM MobiHoc 2016, ACM, 2016. Abstract | Links | BibTeX | Citation @conference{Faltings2016a, title = {LocalCoin: An ad-hoc payment scheme for areas with high connectivity: poster.}, author = {Dimitris Chatzopoulos and Sujit Prakash Gujar and Boi Faltings and Pan Hui}, year = {2016}, date = {2016-00-00}, booktitle = {ACM MobiHoc 2016}, publisher = {ACM}, abstract = {The popularity of digital currencies, especially cryptocurrencies, has been continuously growing since the appearance of Bitcoin. Bitcoin is a peer-to-peer (P2P) cryptocurrency protocol enabling transactions between individuals without the need of a trusted authority. Its network is formed from resources contributed by individuals known as miners. Users of Bitcoin currency create transactions that are stored in a specialised data structure called a block chain. Bitcoin's security lies in a proof-of-work scheme, which requires high computational resources at the miners. These miners have to be synchronised with any update in the network, which produces high data traffic rates. Despite advances in mobile technology, no cryptocurrencies have been proposed for mobile devices. This is largely due to the lower processing capabilities of mobile devices when compared with conventional computers and the poorer Internet connectivity to that of the wired networking. In this work, we propose LocalCoin, an alternative cryptocurrency that requires minimal computational resources, produces low data traffic and works with off-the-shelf mobile devices. LocalCoin replaces the computational hardness that is at the root of Bitcoin's security with the social hardness of ensuring that all witnesses to a transaction are colluders. It is based on opportunistic networking rather than relying on infrastructure and incorporates characteristics of mobile networks such as users' locations and their coverage radius in order to employ an alternative proof-of-work scheme. Localcoin features (i) a lightweight proof-of-work scheme and (ii) a distributed block chain.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } The popularity of digital currencies, especially cryptocurrencies, has been continuously growing since the appearance of Bitcoin. Bitcoin is a peer-to-peer (P2P) cryptocurrency protocol enabling transactions between individuals without the need of a trusted authority. Its network is formed from resources contributed by individuals known as miners. Users of Bitcoin currency create transactions that are stored in a specialised data structure called a block chain. Bitcoin's security lies in a proof-of-work scheme, which requires high computational resources at the miners. These miners have to be synchronised with any update in the network, which produces high data traffic rates. Despite advances in mobile technology, no cryptocurrencies have been proposed for mobile devices. This is largely due to the lower processing capabilities of mobile devices when compared with conventional computers and the poorer Internet connectivity to that of the wired networking. In this work, we propose LocalCoin, an alternative cryptocurrency that requires minimal computational resources, produces low data traffic and works with off-the-shelf mobile devices. LocalCoin replaces the computational hardness that is at the root of Bitcoin's security with the social hardness of ensuring that all witnesses to a transaction are colluders. It is based on opportunistic networking rather than relying on infrastructure and incorporates characteristics of mobile networks such as users' locations and their coverage radius in order to employ an alternative proof-of-work scheme. Localcoin features (i) a lightweight proof-of-work scheme and (ii) a distributed block chain. Chatzopoulos, Dimitris; Gujar, Sujit Prakash; Faltings, Boi and Hui, Pan, LocalCoin: An ad-hoc payment scheme for areas with high connectivity: poster., ACM MobiHoc 2016, ACM, 2016. |
Radanovic, Goran and Faltings, Boi Limiting the Influence of Low Quality Information in Community Sensing Conference Proceedings of the 15th AAMAS, 2016. Abstract | Links | BibTeX | Citation @conference{Radanovic2016a, title = {Limiting the Influence of Low Quality Information in Community Sensing}, author = {Goran Radanovic and Boi Faltings}, year = {2016}, date = {2016-00-00}, booktitle = {Proceedings of the 15th AAMAS}, pages = {873-881}, abstract = {We consider a community of private sensors that collect mea surements of a physical phenomenon, such as air pollution, and report it to a center. The center should be able to prevent low quality reports from degrading the quality of the aggregated information, as there are numerous reasons for operators to inject false sensor data. Hence, it is necessary to track the quality of the sensors over time in order to filter out low quality and malicious reports. To achieve this, we construct a reputation system with a guaranteed bounds on negative impact that malicious sensors can cause, and we evaluate its performance on a realistic dataset.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } We consider a community of private sensors that collect mea surements of a physical phenomenon, such as air pollution, and report it to a center. The center should be able to prevent low quality reports from degrading the quality of the aggregated information, as there are numerous reasons for operators to inject false sensor data. Hence, it is necessary to track the quality of the sensors over time in order to filter out low quality and malicious reports. To achieve this, we construct a reputation system with a guaranteed bounds on negative impact that malicious sensors can cause, and we evaluate its performance on a realistic dataset. Radanovic, Goran and Faltings, Boi, Limiting the Influence of Low Quality Information in Community Sensing, Proceedings of the 15th AAMAS, 2016. |
Radanovic, Goran and Faltings, Boi Learning to Scale Payments in Crowdsourcing with PropeRBoost Conference Proceedings of the Fourth AAAI conference on Conference on Human Computation and Crowdsourcing (HCOMP'16), 2016. Abstract | Links | BibTeX | Citation @conference{Radanovic2016c, title = {Learning to Scale Payments in Crowdsourcing with PropeRBoost}, author = {Goran Radanovic and Boi Faltings}, year = {2016}, date = {2016-00-00}, booktitle = {Proceedings of the Fourth AAAI conference on Conference on Human Computation and Crowdsourcing (HCOMP'16)}, abstract = {Motivating workers to provide significant effort has been recognized as an important issue in crowdsourcing. It is important not only to compensate worker effort, but also to discourage low-quality workers from participating. Several proper incentive schemes have been proposed for this purpose; they are either based on gold tasks or on peer consistency in individual tasks. As the rewards cannot become negative, these schemes have difficulty in achieving zero expected reward for random answers. We describe a novel boosting scheme, ProperRBoost, that improves the efficiency of existing incentive schemes by making a better separation between incentives for high and low quality work, and effectively discourages random answers by assigning them near minimal average rewards. We show the actual performance of the boosting scheme through simulations of various worker strategies.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Motivating workers to provide significant effort has been recognized as an important issue in crowdsourcing. It is important not only to compensate worker effort, but also to discourage low-quality workers from participating. Several proper incentive schemes have been proposed for this purpose; they are either based on gold tasks or on peer consistency in individual tasks. As the rewards cannot become negative, these schemes have difficulty in achieving zero expected reward for random answers. We describe a novel boosting scheme, ProperRBoost, that improves the efficiency of existing incentive schemes by making a better separation between incentives for high and low quality work, and effectively discourages random answers by assigning them near minimal average rewards. We show the actual performance of the boosting scheme through simulations of various worker strategies. Radanovic, Goran and Faltings, Boi, Learning to Scale Payments in Crowdsourcing with PropeRBoost, Proceedings of the Fourth AAAI conference on Conference on Human Computation and Crowdsourcing (HCOMP'16), 2016. |
Faltings, Boi and Radanovic, Goran Mechanismen zur Beschaffung korrekter Daten Journal Article Informatik Spektrum, 2016. Abstract | Links | BibTeX | Citation @article{Faltings2016b, title = {Mechanismen zur Beschaffung korrekter Daten}, author = {Boi Faltings and Goran Radanovic}, year = {2016}, date = {2016-00-00}, journal = {Informatik Spektrum}, abstract = {,Smart Cities“ brauchen als Grundlage für Entscheidungen grosse Mengen von Daten, die häufig nicht direkt, sondern über andere Agenten erhoben werden. Es ist daher wichtig, die Qualität durch geeignete Anreize oder Kontrolle sicherzustellen. Bei Ausnutzung der Spieltheorie ist dies erstaunlicherweise selbst dann möglich, wenn die Korrektheit der Daten gar nicht kontrolliert werden kann. Wir zeigen neuartige Mechanismen zur Qualitätssicherung für zwei Arten von Agenten. Für rational handelnde Agenten berechnen wir die Belohnung für die Daten so, dass nur korrekte Daten einen positiven Betrag erwarten können. Für fehlerhafte oder absichlich falsche Daten zeigen wir einen neuen Mechanismus, der den negativen Einfluss auf das erzeugte Modell begrenzt. }, keywords = {}, pubstate = {published}, tppubtype = {article} } ,Smart Cities“ brauchen als Grundlage für Entscheidungen grosse Mengen von Daten, die häufig nicht direkt, sondern über andere Agenten erhoben werden. Es ist daher wichtig, die Qualität durch geeignete Anreize oder Kontrolle sicherzustellen. Bei Ausnutzung der Spieltheorie ist dies erstaunlicherweise selbst dann möglich, wenn die Korrektheit der Daten gar nicht kontrolliert werden kann. Wir zeigen neuartige Mechanismen zur Qualitätssicherung für zwei Arten von Agenten. Für rational handelnde Agenten berechnen wir die Belohnung für die Daten so, dass nur korrekte Daten einen positiven Betrag erwarten können. Für fehlerhafte oder absichlich falsche Daten zeigen wir einen neuen Mechanismus, der den negativen Einfluss auf das erzeugte Modell begrenzt. Faltings, Boi and Radanovic, Goran, Mechanismen zur Beschaffung korrekter Daten, Informatik Spektrum, 2016. |
Gujar, Sujit Prakash and Faltings, Boi Online Auctions for Dynamic Assignment: Theory and Empirical Evaluation Conference Proceedings of the European Conference on Artificial Intelligence, 22 , EurAI 2016. Abstract | Links | BibTeX | Citation @conference{gujar2016a, title = {Online Auctions for Dynamic Assignment: Theory and Empirical Evaluation}, author = {Sujit Prakash Gujar and Boi Faltings}, year = {2016}, date = {2016-00-00}, booktitle = {Proceedings of the European Conference on Artificial Intelligence}, volume = {22}, pages = {1035-1043}, organization = {EurAI}, abstract = {Dynamic resource assignment is a common problem in multi-agent systems.We consider scenarios in which dynamic agents have preferences about assignments and the resources that can be assigned using online auctions.We study the trade-off between the following online auction properties: (i) truthfulness, (ii) expressiveness, (iii) efficiency, and (iv) average case performance. We theoretically and empirically compare four different online auctions: (i) Arrival Priority Serial Dictatorship, (ii) Split Dynamic VCG, (iii) e-Action, and (iv) Online Ranked Competition Auction. The latter is a novel design based on the competitive secretary problem. We show that, in addition to truthfulness and algorithmic efficiency, the degree of competition also plays an important role in selecting the best algorithm for a given context.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Dynamic resource assignment is a common problem in multi-agent systems.We consider scenarios in which dynamic agents have preferences about assignments and the resources that can be assigned using online auctions.We study the trade-off between the following online auction properties: (i) truthfulness, (ii) expressiveness, (iii) efficiency, and (iv) average case performance. We theoretically and empirically compare four different online auctions: (i) Arrival Priority Serial Dictatorship, (ii) Split Dynamic VCG, (iii) e-Action, and (iv) Online Ranked Competition Auction. The latter is a novel design based on the competitive secretary problem. We show that, in addition to truthfulness and algorithmic efficiency, the degree of competition also plays an important role in selecting the best algorithm for a given context. Gujar, Sujit Prakash and Faltings, Boi, Online Auctions for Dynamic Assignment: Theory and Empirical Evaluation, Proceedings of the European Conference on Artificial Intelligence, 22 , EurAI 2016. |
2015 |
Radanovic, Goran and Faltings, Boi Incentives for Subjective Evaluations with Private Beliefs Conference Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), 2015. @conference{Radanovic&Faltings:15a, title = {Incentives for Subjective Evaluations with Private Beliefs}, author = {Goran Radanovic and Boi Faltings}, year = {2015}, date = {2015-00-00}, booktitle = {Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15)}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Radanovic, Goran and Faltings, Boi, Incentives for Subjective Evaluations with Private Beliefs, Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), 2015. |
Radanovic, Goran and Faltings, Boi Incentive Schemes for Participatory Sensing Conference Proceedings of the 14th international conference on autonomous agents and multiagent systems (AAMAS'15), 2015. @conference{Radanovic&Faltings:15b, title = {Incentive Schemes for Participatory Sensing}, author = {Goran Radanovic and Boi Faltings}, year = {2015}, date = {2015-00-00}, booktitle = {Proceedings of the 14th international conference on autonomous agents and multiagent systems (AAMAS'15)}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Radanovic, Goran and Faltings, Boi, Incentive Schemes for Participatory Sensing, Proceedings of the 14th international conference on autonomous agents and multiagent systems (AAMAS'15), 2015. |
Bhat, Satyanath; Jain, Shweta; Gujar, Sujit and Narahari, Yadati An Optimal Bidimensional Multi-Armed Bandit Auction for Multi-unit Procurement Conference Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, Istanbul, Turkey, May 4-8, 2015, 2015. @conference{Sujit15a, title = {An Optimal Bidimensional Multi-Armed Bandit Auction for Multi-unit Procurement}, author = {Satyanath Bhat and Shweta Jain and Sujit Gujar and Yadati Narahari}, year = {2015}, date = {2015-00-00}, booktitle = {Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, Istanbul, Turkey, May 4-8, 2015}, pages = {1789--1790}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Bhat, Satyanath; Jain, Shweta; Gujar, Sujit and Narahari, Yadati, An Optimal Bidimensional Multi-Armed Bandit Auction for Multi-unit Procurement, Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, Istanbul, Turkey, May 4-8, 2015, 2015. |
Guajr, Sujit and Faltings, Boi Dynamic Task Assignments: An Online Two Sided Matching Approach Conference Proceedings of the 3rd International workshop on Matching Under Preferences, MATCHUP, 2015. @conference{Gujar15a, title = {Dynamic Task Assignments: An Online Two Sided Matching Approach}, author = {Sujit Guajr and Boi Faltings}, year = {2015}, date = {2015-00-00}, booktitle = {Proceedings of the 3rd International workshop on Matching Under Preferences, MATCHUP}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Guajr, Sujit and Faltings, Boi, Dynamic Task Assignments: An Online Two Sided Matching Approach, Proceedings of the 3rd International workshop on Matching Under Preferences, MATCHUP, 2015. |
Guajr, Sujit and Faltings, Boi Auction Based Mechanisms for Dynamic Task Assignments in Expert Crowdsourcing Conference Proceedings of the International workshop on Agent Mediated E-Commerce and Trading Agent Design and Analysis (AMEC/TADA'15) , 2015. @conference{Gujar15b, title = {Auction Based Mechanisms for Dynamic Task Assignments in Expert Crowdsourcing }, author = {Sujit Guajr and Boi Faltings}, year = {2015}, date = {2015-00-00}, booktitle = {Proceedings of the International workshop on Agent Mediated E-Commerce and Trading Agent Design and Analysis (AMEC/TADA'15) }, keywords = {}, pubstate = {published}, tppubtype = {conference} } Guajr, Sujit and Faltings, Boi, Auction Based Mechanisms for Dynamic Task Assignments in Expert Crowdsourcing , Proceedings of the International workshop on Agent Mediated E-Commerce and Trading Agent Design and Analysis (AMEC/TADA'15) , 2015. |
Mukherjee, Tridib; Dutta, Partha; Hegde, Vinay G and Gujar, Sujit 29th IEEE International Parallel &Distributed Processing Symposium, IPDPS, 2015. @conference{mukherjeerisc, title = {RISC: Robust Infrastructure over Shared Computing Resources Through Dynamic Pricing and Incentivization}, author = {Tridib Mukherjee and Partha Dutta and Vinay G Hegde and Sujit Gujar}, year = {2015}, date = {2015-00-00}, booktitle = {29th IEEE International Parallel &Distributed Processing Symposium, IPDPS}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Mukherjee, Tridib; Dutta, Partha; Hegde, Vinay G and Gujar, Sujit, RISC: Robust Infrastructure over Shared Computing Resources Through Dynamic Pricing and Incentivization, 29th IEEE International Parallel &Distributed Processing Symposium, IPDPS, 2015. |
Musat, Claudiu Cristian and Faltings, Boi Personalizing Product Rankings Using Collaborative Filtering on Opinion-Derived Topic Profiles Journal Article 24th IJCAI, pp. 830-836, 2015. Abstract | Links | BibTeX | Citation @article{MusatFaltings2015b, title = {Personalizing Product Rankings Using Collaborative Filtering on Opinion-Derived Topic Profiles}, author = {Claudiu Cristian Musat and Boi Faltings}, year = {2015}, date = {2015-00-00}, journal = {24th IJCAI}, pages = {830-836}, abstract = {Product review sites such as TripAdvisor, Yelp or Amazon provide a single, non personalized ranking of products. The sparse review data makes personalizing recommendations difficult. Topic Profile Collaborative Filtering exploits review texts to identify user profiles as a basis for similarity. We show that careful use of the available data and separating users into classes can greatly improve the performance of such techniques. We significantly improve MAE, RMSE, and Kendall tau, compared to the previous best results. In addition, we show that personalization does not benefit all the users to the same extent. We propose switching between a personalized and a non personalized method based on the user opinion profile. We show that the user’s opinionatedness is a good indicator of whether the personalization will work or not.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Product review sites such as TripAdvisor, Yelp or Amazon provide a single, non personalized ranking of products. The sparse review data makes personalizing recommendations difficult. Topic Profile Collaborative Filtering exploits review texts to identify user profiles as a basis for similarity. We show that careful use of the available data and separating users into classes can greatly improve the performance of such techniques. We significantly improve MAE, RMSE, and Kendall tau, compared to the previous best results. In addition, we show that personalization does not benefit all the users to the same extent. We propose switching between a personalized and a non personalized method based on the user opinion profile. We show that the user’s opinionatedness is a good indicator of whether the personalization will work or not. Musat, Claudiu Cristian and Faltings, Boi, Personalizing Product Rankings Using Collaborative Filtering on Opinion-Derived Topic Profiles, 24th IJCAI, pp. 830-836, 2015. |
Maksai, Andrii; Garcin, Florent and Faltings, Boi Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics Journal Article ACM Conference on Recommender Systems (Recsys), (9), pp. 179-186, 2015. Abstract | Links | BibTeX | Citation @article{Maksai2015, title = {Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics}, author = {Andrii Maksai and Florent Garcin and Boi Faltings}, year = {2015}, date = {2015-00-00}, journal = {ACM Conference on Recommender Systems (Recsys)}, number = {9}, pages = {179-186}, abstract = {We investigate how metrics that can be measured offline can be used to predict the online performance of recommender systems, thus avoiding costly A-B testing. In addition to accuracy metrics, we combine diversity, coverage, and serendipity metrics to create a new performance model. Using the model, we quantify the tradeoff between different metrics and propose to use it to tune the parameters of recommender algorithms without the need for online testing. Another application for the model is a self-adjusting algorithm blend that optimizes a recommender's parameters over time. We evaluate our findings on data and experiments from news websites.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We investigate how metrics that can be measured offline can be used to predict the online performance of recommender systems, thus avoiding costly A-B testing. In addition to accuracy metrics, we combine diversity, coverage, and serendipity metrics to create a new performance model. Using the model, we quantify the tradeoff between different metrics and propose to use it to tune the parameters of recommender algorithms without the need for online testing. Another application for the model is a self-adjusting algorithm blend that optimizes a recommender's parameters over time. We evaluate our findings on data and experiments from news websites. Maksai, Andrii; Garcin, Florent and Faltings, Boi, Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics, ACM Conference on Recommender Systems (Recsys), (9), pp. 179-186, 2015. |
Radanovic, Goran and Faltings, Boi Incentivizing Truthful Responses with the Logarithmic Peer Truth Serum Conference Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers., 2015. @conference{Radanovic&Faltings:15c, title = {Incentivizing Truthful Responses with the Logarithmic Peer Truth Serum}, author = {Goran Radanovic and Boi Faltings}, year = {2015}, date = {2015-00-00}, booktitle = {Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Radanovic, Goran and Faltings, Boi, Incentivizing Truthful Responses with the Logarithmic Peer Truth Serum, Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers., 2015. |
2014 |
Cigler, Ludek and Faltings, Boi Symmetric Subgame-Perfect Equilibria in Resource Allocation Journal Article Journal of Artificial Intelligence Research (JAIR), 49 , pp. 323-361, 2014. Abstract | Links | BibTeX | Citation @article{Cigler2014a, title = {Symmetric Subgame-Perfect Equilibria in Resource Allocation}, author = {Ludek Cigler and Boi Faltings}, year = {2014}, date = {2014-00-00}, journal = {Journal of Artificial Intelligence Research (JAIR)}, volume = {49}, pages = {323-361}, abstract = {We analyze symmetric protocols to rationally coordinate on an asymmetric, efficient allocation in an infinitely repeated N-agent, C-resource allocation problems, where the resources are all homogeneous. Bhaskar proposed one way to achieve this in 2-agent, 1-resource games: Agents start by symmetrically randomizing their actions, and as soon as they each choose different actions, they start to follow a potentially asymmetric "convention" that prescribes their actions from then on. We extend the concept of convention to the general case of infinitely repeated resource allocation games with N agents and C resources. We show that for any convention, there exists a symmetric subgame-perfect equilibrium which implements it. We present two conventions: bourgeois, where agents stick to the first allocation; and market, where agents pay for the use of resources, and observe a global coordination signal which allows them to alternate between different allocations. We define price of anonymity of a convention as a ratio between the maximum social payoff of any (asymmetric) strategy profile and the expected social payoff of the subgame-perfect equilibrium which implements the convention. We show that while the price of anonymity of the bourgeois convention is infinite, the market convention decreases this price by reducing the conflict between the agents.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We analyze symmetric protocols to rationally coordinate on an asymmetric, efficient allocation in an infinitely repeated N-agent, C-resource allocation problems, where the resources are all homogeneous. Bhaskar proposed one way to achieve this in 2-agent, 1-resource games: Agents start by symmetrically randomizing their actions, and as soon as they each choose different actions, they start to follow a potentially asymmetric "convention" that prescribes their actions from then on. We extend the concept of convention to the general case of infinitely repeated resource allocation games with N agents and C resources. We show that for any convention, there exists a symmetric subgame-perfect equilibrium which implements it. We present two conventions: bourgeois, where agents stick to the first allocation; and market, where agents pay for the use of resources, and observe a global coordination signal which allows them to alternate between different allocations. We define price of anonymity of a convention as a ratio between the maximum social payoff of any (asymmetric) strategy profile and the expected social payoff of the subgame-perfect equilibrium which implements the convention. We show that while the price of anonymity of the bourgeois convention is infinite, the market convention decreases this price by reducing the conflict between the agents. Cigler, Ludek and Faltings, Boi, Symmetric Subgame-Perfect Equilibria in Resource Allocation, Journal of Artificial Intelligence Research (JAIR), 49 , pp. 323-361, 2014. |
Bhat, Satyanath; Nath, Swaprava; Gujar, Sujit; Zoeter, Onno; Narahari, Y and Dance, Chris A mechanism to optimally balance cost and quality of labeling tasks outsourced to strategic agents Conference Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems, International Foundation for Autonomous Agents and Multiagent Systems 2014. @conference{bhat2014mechanism, title = {A mechanism to optimally balance cost and quality of labeling tasks outsourced to strategic agents}, author = {Satyanath Bhat and Swaprava Nath and Sujit Gujar and Onno Zoeter and Y Narahari and Chris Dance}, year = {2014}, date = {2014-00-00}, booktitle = {Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems}, pages = {917--924}, organization = {International Foundation for Autonomous Agents and Multiagent Systems}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Bhat, Satyanath; Nath, Swaprava; Gujar, Sujit; Zoeter, Onno; Narahari, Y and Dance, Chris, A mechanism to optimally balance cost and quality of labeling tasks outsourced to strategic agents, Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems, International Foundation for Autonomous Agents and Multiagent Systems 2014. |
Jain, Shweta; Gujar, Sujit; Zoeter, Onno and Narahari, Y A quality assuring multi-armed bandit crowdsourcing mechanism with incentive compatible learning Conference Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems, International Foundation for Autonomous Agents and Multiagent Systems 2014. @conference{jain2014quality, title = {A quality assuring multi-armed bandit crowdsourcing mechanism with incentive compatible learning}, author = {Shweta Jain and Sujit Gujar and Onno Zoeter and Y Narahari}, year = {2014}, date = {2014-00-00}, booktitle = {Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems}, pages = {1609--1610}, organization = {International Foundation for Autonomous Agents and Multiagent Systems}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Jain, Shweta; Gujar, Sujit; Zoeter, Onno and Narahari, Y, A quality assuring multi-armed bandit crowdsourcing mechanism with incentive compatible learning, Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems, International Foundation for Autonomous Agents and Multiagent Systems 2014. |
Boia, Marina; Musat, Claudiu Cristian and Faltings, Boi Acquiring Commonsense Knowledge for Sentiment Analysis Using Human Computation (Poster) Conference Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web, WWW'14 Companion Seoul, Korea, 2014. @conference{boia:2014a, title = {Acquiring Commonsense Knowledge for Sentiment Analysis Using Human Computation (Poster)}, author = {Marina Boia and Claudiu Cristian Musat and Boi Faltings}, year = {2014}, date = {2014-00-00}, booktitle = {Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web}, pages = {225--226}, address = {Seoul, Korea}, series = {WWW'14 Companion}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Boia, Marina; Musat, Claudiu Cristian and Faltings, Boi, Acquiring Commonsense Knowledge for Sentiment Analysis Using Human Computation (Poster), Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web, WWW'14 Companion Seoul, Korea, 2014. |
Kempter, Renato; Sintsova, Valentina; Musat, Claudiu and Pu, Pearl EmotionWatch: Visualizing Fine-Grained Emotions in Event-Related Tweets Conference 2014. Abstract | Links | BibTeX | Citation @conference{kempter2014emotionwatch, title = {EmotionWatch: Visualizing Fine-Grained Emotions in Event-Related Tweets}, author = {Renato Kempter and Valentina Sintsova and Claudiu Musat and Pearl Pu}, year = {2014}, date = {2014-00-00}, abstract = {Spectators are increasingly using social platforms to express their opinions and share their emotions during big public events. Those reactions reveal the subjective perception of the event and extend its understanding. This has motivated us to develop a system to explore and visualize volume, patterns, and trends of user sentiments as they evolve over time. Previous work in sentiment analysis and opinion mining has addressed these issues. But the majority of them distinguish only two polarity categories, leaving a more detailed and insightful anal- ysis to be desired. In this paper, we suggest using a fine- grained, multi-category emotion model to classify and visualize users’ emotional reactions in public events. We describe EmotionWatch, a tool that constructs visual summaries of public emotions, and apply it to the 2012 Olympics as a test case. We report findings from a user study evaluating the usability of the tool and val- idating the emotion model. Results show that users prefer a more detailed inspection of public emotions over the simplified analysis. Despite its complexity, users were able to effectively grasp, understand, and interpret the emotional reactions using EmotionWatch. The same user study also pointed out few design improvements for the future development of analogous systems.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Spectators are increasingly using social platforms to express their opinions and share their emotions during big public events. Those reactions reveal the subjective perception of the event and extend its understanding. This has motivated us to develop a system to explore and visualize volume, patterns, and trends of user sentiments as they evolve over time. Previous work in sentiment analysis and opinion mining has addressed these issues. But the majority of them distinguish only two polarity categories, leaving a more detailed and insightful anal- ysis to be desired. In this paper, we suggest using a fine- grained, multi-category emotion model to classify and visualize users’ emotional reactions in public events. We describe EmotionWatch, a tool that constructs visual summaries of public emotions, and apply it to the 2012 Olympics as a test case. We report findings from a user study evaluating the usability of the tool and val- idating the emotion model. Results show that users prefer a more detailed inspection of public emotions over the simplified analysis. Despite its complexity, users were able to effectively grasp, understand, and interpret the emotional reactions using EmotionWatch. The same user study also pointed out few design improvements for the future development of analogous systems. Kempter, Renato; Sintsova, Valentina; Musat, Claudiu and Pu, Pearl, EmotionWatch: Visualizing Fine-Grained Emotions in Event-Related Tweets, 2014. |
Radanovic, Goran and Faltings, Boi Incentives for Truthful Information Elicitation of Continuous Signals Conference Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), 2014. @conference{RF:2014, title = {Incentives for Truthful Information Elicitation of Continuous Signals}, author = {Goran Radanovic and Boi Faltings}, year = {2014}, date = {2014-00-00}, booktitle = {Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14)}, pages = {770-776}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Radanovic, Goran and Faltings, Boi, Incentives for Truthful Information Elicitation of Continuous Signals, Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), 2014. |
Boia, Marina; Musat, Claudiu Cristian and Faltings, Boi Constructing Context-Aware Sentiment Lexicons with an Asynchronous Game with a Purpose Conference Proceedings of the 15th International Conference on Computational Linguistics and Intelligent Text Processing, Part II, 8404 , Lecture Notes in Computer Science Springer Berlin Heidelberg, 2014, ISBN: 978-3-642-54902-1. @conference{boia:2014b, title = {Constructing Context-Aware Sentiment Lexicons with an Asynchronous Game with a Purpose}, author = {Marina Boia and Claudiu Cristian Musat and Boi Faltings}, editor = {Gelbukh, Alexander}, isbn = {978-3-642-54902-1}, year = {2014}, date = {2014-00-00}, booktitle = {Proceedings of the 15th International Conference on Computational Linguistics and Intelligent Text Processing, Part II}, volume = {8404}, pages = {32-44}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Boia, Marina; Musat, Claudiu Cristian and Faltings, Boi, Constructing Context-Aware Sentiment Lexicons with an Asynchronous Game with a Purpose, Proceedings of the 15th International Conference on Computational Linguistics and Intelligent Text Processing, Part II, 8404 , Lecture Notes in Computer Science Springer Berlin Heidelberg, 2014, ISBN: 978-3-642-54902-1. |
Boia, Marina; Musat, Claudiu Cristian and Faltings, Boi Acquiring Commonsense Knowledge for Sentiment Analysis through Human Computation_1 Conference Proceedings of the 28th AAAI Conference on Artificial Intelligence, Québec City, Canada, 2014. @conference{boia:2014c, title = {Acquiring Commonsense Knowledge for Sentiment Analysis through Human Computation\_1}, author = {Marina Boia and Claudiu Cristian Musat and Boi Faltings}, year = {2014}, date = {2014-00-00}, booktitle = {Proceedings of the 28th AAAI Conference on Artificial Intelligence}, pages = {901--907}, address = {Québec City, Canada}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Boia, Marina; Musat, Claudiu Cristian and Faltings, Boi, Acquiring Commonsense Knowledge for Sentiment Analysis through Human Computation_1, Proceedings of the 28th AAAI Conference on Artificial Intelligence, Québec City, Canada, 2014. |
Garcin, Florent and Faltings, Boi Swissnoise: Online Polls with Game-Theoretic Incentives Journal Article Proceedings of the 26th Conference on Innovative Applications of AI, pp. 2972-2977, 2014. @article{gracin2014a, title = {Swissnoise: Online Polls with Game-Theoretic Incentives}, author = {Florent Garcin and Boi Faltings}, year = {2014}, date = {2014-00-00}, journal = {Proceedings of the 26th Conference on Innovative Applications of AI}, pages = {2972-2977}, keywords = {}, pubstate = {published}, tppubtype = {article} } Garcin, Florent and Faltings, Boi, Swissnoise: Online Polls with Game-Theoretic Incentives, Proceedings of the 26th Conference on Innovative Applications of AI, pp. 2972-2977, 2014. |