Feedback-Aware Social Event-Participant Arrangement

Online event-based social networks (EBSNs) and studies on global event-participant arrangement strategies for EBSNs are becoming popular recently. Existing works measure satisfaction of an arrangement by a linear combination of few factors, weights of which are predefined and fixed, and do not allow users to provide feedbacks on whether accepting the arrangement or not. Besides, most of them only consider offline scenarios, where full information of users is known in advance. However, on real-world EBSN platforms, users can dynamically log in the platform and register for events on a first come, first served basis. In other words, online scenarios of event-participant arrangement strategies should be considered. In this work, we study a new event-participant arrangement strategy for online scenarios, the Feedback-Aware Social Event-participant Arrangement (FASEA) problem, where satisfaction scores of an arrangement are learned adaptively and users can choose to accept or reject the arranged events. Particularly, we model the problem as a contextual combinatorial bandit setting and use efficient and effective algorithms to solve the problem. The effectiveness and efficiency of the solutions are evaluated with extensive experimental studies and our findings indicate that the state-of-the-art Thompson Sampling that is reported to work well under basic multi-armed bandit does not perform well under FASEA.

[1]  Shuai Li,et al.  Contextual Combinatorial Cascading Bandits , 2016, ICML.

[2]  Shie Mannor,et al.  Thompson Sampling for Complex Online Problems , 2013, ICML.

[3]  Peter Auer,et al.  Using Confidence Bounds for Exploitation-Exploration Trade-offs , 2003, J. Mach. Learn. Res..

[4]  Dimitris Papadias,et al.  Real-Time Multi-Criteria Social Graph Partitioning: A Game Theoretic Approach , 2015, SIGMOD Conference.

[5]  Bhaskar Krishnamachari,et al.  Combinatorial Network Optimization With Unknown Variables: Multi-Armed Bandits With Linear Rewards and Individual Observations , 2010, IEEE/ACM Transactions on Networking.

[6]  Mehryar Mohri,et al.  Multi-armed Bandit Algorithms and Empirical Evaluation , 2005, ECML.

[7]  Wei Chu,et al.  A contextual-bandit approach to personalized news article recommendation , 2010, WWW '10.

[8]  Wei Chen,et al.  Combinatorial Partial Monitoring Game with Linear Feedback and Its Applications , 2014, ICML.

[9]  Rodrygo L. T. Santos,et al.  Context-Aware Event Recommendation in Event-based Social Networks , 2015, RecSys.

[10]  John Langford,et al.  The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information , 2007, NIPS.

[11]  Lihong Li,et al.  An Empirical Evaluation of Thompson Sampling , 2011, NIPS.

[12]  Wei Chen,et al.  Combinatorial Multi-Armed Bandit: General Framework and Applications , 2013, ICML.

[13]  J. Walrand,et al.  Asymptotically efficient allocation rules for the multiarmed bandit problem with multiple plays-Part II: Markovian rewards , 1987 .

[14]  Xiaoyong Du,et al.  Location selection for utility maximization with capacity constraints , 2012, CIKM '12.

[15]  T. L. Lai Andherbertrobbins.  Asymptotically Efficient Adaptive Allocation Rules , 2022 .

[16]  W. R. Thompson.  ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES , 1933 .

[17]  Raphaël Troncy,et al.  Hybrid event recommendation using linked data and user diversity , 2013, RecSys.

[18]  Kyriakos Mouratidis,et al.  Capacity constrained assignment in spatial databases , 2008, SIGMOD Conference.

[19]  Donald A. Berry,et al.  Bandit Problems: Sequential Allocation of Experiments. , 1986 .

[20]  Kyriakos Mouratidis,et al.  Optimal matching between spatial datasets under capacity constraints , 2010, TODS.

[21]  Raymond Chi-Wing Wong,et al.  On Efficient Spatial Matching , 2007, VLDB.

[22]  Rui Meng,et al.  Bottleneck-aware arrangement over event-based social networks: the max-min approach , 2015, World Wide Web.

[23]  Laks V. S. Lakshmanan,et al.  On social event organization , 2014, KDD.

[24]  Aranyak Mehta,et al.  Online Matching and Ad Allocation , 2013, Found. Trends Theor. Comput. Sci..

[25]  Sourav S. Bhowmick,et al.  In search of influential event organizers in online social networks , 2014, SIGMOD Conference.

[26]  Qing Zhao,et al.  Logarithmic weak regret of non-Bayesian restless multi-armed bandit , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[27]  Felipe Caro,et al.  Dynamic Assortment with Demand Learning for Seasonal Consumer Goods , 2007, Manag. Sci..

[28]  J. Langford,et al.  The Epoch-Greedy algorithm for contextual multi-armed bandits , 2007, NIPS 2007.

[29]  Lei Chen,et al.  Online mobile Micro-Task Allocation in spatial crowdsourcing , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[30]  Yu Zhou,et al.  A Novel Social Event Organization Approach for Diverse User Choices , 2016, Comput. J..

[31]  Hing-Fung Ting,et al.  Near optimal algorithms for online maximum edge-weighted b-matching and two-sided vertex-weighted b-matching , 2015, Theor. Comput. Sci..

[32]  Rong Du,et al.  Who should I invite for my party?: combining user preference and influence maximization for social events , 2015, UbiComp.

[33]  Shipra Agrawal,et al.  Thompson Sampling for Contextual Bandits with Linear Payoffs , 2012, ICML.

[34]  Lei Chen,et al.  Conflict-Aware Event-Participant Arrangement and Its Variant for Online Setting , 2016, IEEE Transactions on Knowledge and Data Engineering.

[35]  Branislav Kveton,et al.  Efficient Learning in Large-Scale Combinatorial Semi-Bandits , 2014, ICML.

[36]  Wei Pang,et al.  Hete-CF: Social-Based Collaborative Filtering Recommendation Using Heterogeneous Relations , 2014, 2014 IEEE International Conference on Data Mining.

[37]  Yizhou Sun,et al.  LCARS: a location-content-aware recommender system , 2013, KDD.

[38]  Gao Cong,et al.  A general graph-based model for recommendation in event-based social networks , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[39]  Lei Chen,et al.  Utility-Aware Social Event-Participant Planning , 2015, SIGMOD Conference.

[40]  Chien Chin Chen,et al.  Exploring acquaintances of social network site users for effective social event recommendations , 2016, Inf. Process. Lett..

[41]  Wei Zhang,et al.  A Collective Bayesian Poisson Factorization Model for Cold-start Local Event Recommendation , 2015, KDD.

[42]  Xiaoyan Zhu,et al.  Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation , 2014, SDM.

[43]  Akshay Krishnamurthy,et al.  Contextual semibandits via supervised learning oracles , 2015, NIPS.

[44]  Thomas J. Walsh,et al.  Exploring compact reinforcement-learning representations with linear regression , 2009, UAI.

[45]  Mauro Dell'Amico,et al.  8. Quadratic Assignment Problems: Algorithms , 2009 .

[46]  Yajun Wang,et al.  Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms , 2014, J. Mach. Learn. Res..

[47]  Wei Chen,et al.  Combinatorial multi-armed bandit: general framework, results and applications , 2013, ICML 2013.

[48]  Wei Chu,et al.  Contextual Bandits with Linear Payoff Functions , 2011, AISTATS.

[49]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[50]  Chengqi Zhang,et al.  Modeling Location-Based User Rating Profiles for Personalized Recommendation , 2015, ACM Trans. Knowl. Discov. Data.

[51]  Richard M. Karp,et al.  An optimal algorithm for on-line bipartite matching , 1990, STOC '90.

[52]  Yuanyuan Tian,et al.  Event-based social networks: linking the online and offline social worlds , 2012, KDD.

[53]  Berthold Vöcking,et al.  An Optimal Online Algorithm for Weighted Bipartite Matching and Extensions to Combinatorial Auctions , 2013, ESA.

[54]  Li Guo,et al.  Combining Heterogenous Social and Geographical Information for Event Recommendation , 2014, AAAI.

[55]  Lei Chen,et al.  Conflict-aware event-participant arrangement , 2015, 2015 IEEE 31st International Conference on Data Engineering.