Trustworthiness, diversity and inference in recommendation systems

dc.contributor.authorChen, Cheng
dc.contributor.supervisorWu, Kui
dc.contributor.supervisorSrinivasan, Venkatesh of Computer Scienceen_US of Philosophy Ph.D.en_US
dc.description.abstractRecommendation systems are information filtering systems that help users effectively and efficiently explore large amount of information and identify items of interest. Accurate predictions of users' interests improve user satisfaction and are beneficial to business or service providers. Researchers have been making tremendous efforts to improve the accuracy of recommendations. Emerging trends of technologies and application scenarios, however, lead to challenges other than accuracy for recommendation systems. Three new challenges include: (1) opinion spam results in untrustworthy content and makes recommendations deceptive; (2) users prefer diversified content; (3) in some applications user behavior data may not be available to infer users' preference. This thesis tackles the above challenges. We identify features of untrustworthy commercial campaigns on a question and answer website, and adopt machine learning-based techniques to implement an adaptive detection system which automatically detects commercial campaigns. We incorporate diversity requirements into a classic theoretical model and develop efficient algorithms with performance guarantees. We propose a novel and robust approach to infer user preference profile from recommendations using copula models. The proposed approach can offer in-depth business intelligence for physical stores that depend on Wi-Fi hotspots for mobile advertisement.en_US
dc.identifier.bibliographicCitationCheng Chen, Kui Wu, Venkatesh Srinivasan, and R. Bharadwaj. "The best answers? think twice: online detection of commercial campaigns in the CQA forums," in Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 458-465, August 2013.en_US
dc.identifier.bibliographicCitationCheng Chen, Sean Chester, Venkatesh Srinivasan, Kui Wu and Alex Thomo, "Group-Aware Weighted Bipartite $b$-Matching," in Proceedings of the 25th ACM Conference on Information and Knowledge Management, October 2016.en_US
dc.identifier.bibliographicCitationCheng Chen, Fang Dong, Kui Wu, Venkatesh Srinivasan and Alex Thomo, "From Recommendation to Profile Inference (Rec2PI): A Value-added Service to Wi-Fi Data Mining," in Proceedings of the 25th ACM Conference on Information and Knowledge Management, October 2016.en_US
dc.identifier.bibliographicCitationCheng Chen, Kui Wu, Venkatesh Srinivasan, R. Kesav Bharadwaj. "The Best Answers? Think Twice: Identifying Commercial Campagins in the CQA Forums," Springer Journal of Computer Science and Technology, vol. 30, no. 4, pp. 810-828, July 2015.en_US
dc.identifier.bibliographicCitationCheng Chen, Lan Zheng, Venkatesh Srinivasan, Alex Thomo, Kui Wu and Anthony Sukow, "Conflict-Aware Weighted Bipartite B-Matching and Its Application to e-commerce," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, pp. 1475-1488, June 1 2016.en_US
dc.identifier.bibliographicCitationCheng Chen, Lan Zheng, Alex Thomo, Kui Wu, and Venkatesh Srinivasan, "Comparing the staples in latent factor models for recommender systems," in Proceedings of the 29th Annual ACM Symposium on Applied Computing - Data Mining track, pp. 91-96, March 2014.en_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectBipartite Graphsen_US
dc.subjectLinear Programmingen_US
dc.subjectSubmodular Systemsen_US
dc.subjectRecommendation Systemsen_US
dc.subjectAnomaly Detectionen_US
dc.subjectCommunity Question and Answer Websitesen_US
dc.subjectPaid Postersen_US
dc.subjectAdaptive Detection Systemsen_US
dc.subjectWeighted Bipartite b-Matchingen_US
dc.subjectConflict Constraintsen_US
dc.subjectReverse Engineering of Recommendationsen_US
dc.subjectWi-Fi Data Miningen_US
dc.subjectProfile Inferenceen_US
dc.subjectCopula Modellingen_US
dc.titleTrustworthiness, diversity and inference in recommendation systemsen_US


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