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Trustworthiness, diversity and inference in recommendation systems

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dc.contributor.author Chen, Cheng
dc.date.accessioned 2016-09-28T17:02:40Z
dc.date.available 2016-09-28T17:02:40Z
dc.date.copyright 2016 en_US
dc.date.issued 2016-09-28
dc.identifier.uri http://hdl.handle.net/1828/7576
dc.description.abstract Recommendation 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.language English eng
dc.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.subject Bipartite Graphs en_US
dc.subject Matchings en_US
dc.subject NP-hardness en_US
dc.subject Linear Programming en_US
dc.subject Submodular Systems en_US
dc.subject Recommendation Systems en_US
dc.subject Anomaly Detection en_US
dc.subject Community Question and Answer Websites en_US
dc.subject Paid Posters en_US
dc.subject Adaptive Detection Systems en_US
dc.subject Weighted Bipartite b-Matching en_US
dc.subject Conflict Constraints en_US
dc.subject Optimization en_US
dc.subject Approximation en_US
dc.subject Reverse Engineering of Recommendations en_US
dc.subject Wi-Fi Data Mining en_US
dc.subject Profile Inference en_US
dc.subject Copula Modelling en_US
dc.title Trustworthiness, diversity and inference in recommendation systems en_US
dc.type Thesis en_US
dc.contributor.supervisor Wu, Kui
dc.contributor.supervisor Srinivasan, Venkatesh
dc.degree.department Department of Computer Science en_US
dc.degree.level Doctor of Philosophy Ph.D. en_US
dc.identifier.bibliographicCitation Cheng 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.bibliographicCitation Cheng 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.bibliographicCitation Cheng 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.bibliographicCitation Cheng 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.bibliographicCitation Cheng 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.bibliographicCitation Cheng 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.description.scholarlevel Graduate en_US
dc.description.proquestcode 0984 en_US


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