Trustworthiness, diversity and inference in recommendation systems




Chen, Cheng

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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.



Bipartite Graphs, Matchings, NP-hardness, Linear Programming, Submodular Systems, Recommendation Systems, Anomaly Detection, Community Question and Answer Websites, Paid Posters, Adaptive Detection Systems, Weighted Bipartite b-Matching, Conflict Constraints, Optimization, Approximation, Reverse Engineering of Recommendations, Wi-Fi Data Mining, Profile Inference, Copula Modelling