| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 106 === Recently, one-class matrix factorization has been considered for recommendation systems that have only implicit user feedbacks. However, most of existing works focus on the methodology. They conduct evaluations on some public or even artificially generated data, rather than deploying their approaches to a large production system. Therefore, many practical considerations are not discussed. In this thesis, we aim to fill the gap by providing an end-to-end study of applying one-class matrix factorization on a large-scale service of trending query recommendation. We discuss some practical challenges and demonstrate a more than 20\% improvement in our online production system. On the methodology side, based on properties of real data, we point out some computational bottlenecks not addressed in past works and provide efficient training procedures.
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