A hybrid approach of topic model and matrix factorization based on two-step recommendation framework

文献类型： 外文期刊

第一作者： Zhao, Xiangyu

作者： Zhao, Xiangyu;Niu, Zhendong;Chen, Wei;Shi, Chongyang;Niu, Ke;Liu, Donglei;Zhao, Xiangyu;Chen, Wei

作者机构：

关键词： Collaborative filtering;Two-step recommendation framework;Hybrid approach;Top-N recommendation

期刊名称：JOURNAL OF INTELLIGENT INFORMATION SYSTEMS （ 影响因子：1.888； 五年影响因子：1.939 ）

ISSN： 0925-9902

年卷期： 2015 年 44 卷 3 期

页码：

收录情况： SCI

摘要： Recommender systems become increasingly significant in solving the information explosion problem. Two typical kinds of techniques treat the recommendation problem as either a rating prediction or a ranking prediction one. In contrast, we propose a two-step framework that considers recommendation as a simulation of users' behaviors to generate ratings. The first step is to predict the probability that a user rates an item, and the second step is to predict rating values. After that, the predicted results from both steps are combined to compute the expectations of users' ratings on items, which are used to generate recommendations. Based on this framework, we propose a hybrid approach which uses topic model in the first step and matrix factorization in the second to solve the recommendation problem. Experiments with MovieLens and EachMovie datasets demonstrate the effectiveness of the proposed framework and the recommendation approach.

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