| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 104 === In this thesis, we aim at building a recommender system for sequential data. The goal is to predict a user’s next action based on his or her last basket of actions. In order to solve this task, FPMC is proposed by Rendle to model both sequential behavior and user preference. By utilizing similar concept of FPMC, we attempt to construct a generalized model to predict actions based on not only current actions of users but also their farther previous sequential behavior. In addition, we extend FPMC to incorporate temporal information of behavior. In our model, two simple and effective methods are introduced. First, relationship between time and items is captured by exploiting Matrix Factorization. Second, we improve negative sampling technique by taking time constraint into account for solving BPR optimization. Experimental results on two datasets, including music dataset and course dataset, show that our method outperforms state-of-the-art.
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