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Trustworthiness, diversity and inference in recommendation systems

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. / Graduate / 0984 / cchenv@uvic.ca

  1. http://hdl.handle.net/1828/7576
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
Identiferoai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/7576
Date28 September 2016
CreatorsChen, Cheng
ContributorsWu, Kui, Srinivasan, Venkatesh
Source SetsUniversity of Victoria
LanguageEnglish, English
Detected LanguageEnglish
TypeThesis
RightsAvailable to the World Wide Web

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