Interest before liking: Two-step recommendation approaches

文献类型： 外文期刊

第一作者： Zhao, Xiangyu

作者： Zhao, Xiangyu;Niu, Zhendong;Chen, Wei;Chen, Wei

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关键词： Recommender system;Binary user model;Collaborative filtering;Two-step recommendation;User interests

期刊名称：KNOWLEDGE-BASED SYSTEMS （ 影响因子：8.038； 五年影响因子：7.842 ）

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收录情况： SCI

摘要： Recommender systems become increasingly significant in solving the information explosion problem. Existing techniques focus on minimizing predicted rating errors and recommend items with high predicted values to people. They consider high and low rating values as liking and disliking, respectively, and tend to recommend items that users will like in the future. However, especially in the information overloaded age, we consider whether a user rates an item as a measure of interest no matter whether the value is high or low, and the rating values themselves represent the attitude to the quality of the target item. In this paper, we propose two-step recommendation approaches that recommend items matching users' interests first, and then try to find high quality items that users will like. Experiments using MovieLens dataset are carried out to evaluate the proposed methods with precision, recall, NDCG, preference-ratio and precision-like as evaluation metrics. The results show that our proposed approaches outperform the seven existing ones, i.e., UserCF, ItemCF, ALS-WR, Slope-one, SVD++, iExpand and LICF.

分类号： TP3

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[5]A hybrid approach of topic model and matrix factorization based on two-step recommendation framework. Zhao, Xiangyu,Niu, Zhendong,Chen, Wei,Shi, Chongyang,Niu, Ke,Liu, Donglei,Zhao, Xiangyu,Chen, Wei. 2015

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