Multiple Auxiliary Information Based Deep Model for Collaborative Filtering
Multiple Auxiliary Information Based Deep Model for Collaborative Filtering作者机构：School of Information Science and Technology Northeast Normal University Changchun 130117 China Key Laboratory of Applied Statistics of Ministry of Education Northeast Normal University Changchun 130024 China School of Environment Northeast Normal University Changchun 130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China
出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)
年 卷 期：2018年第33卷第4期
页 面：668-681页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This work was supported by the National Natural Science Foundation of China under Grant Nos. 71473035 and 11501095 the Fundamental Research Funds for the Central Universities of China under Grant No. 2412017QD028 the China Postdoctoral Science Foundation under Grant No. 2017M021192 the Scientific and Technological Development Program of Jilin Province of China under Grant Nos. 20180520022JH 20150204040GX and 20170520051JH Jilin Province Development and Reform Commission Project of China under Grant Nos. 2015Y055 and 2015Y054 and the Natural Science Foundation of Jilin Province of China under Grant No. 20150101057JC
主 题：semantic representation plot information denoising autoencoder collaborative filtering auxiliary information
摘 要：With the ever-growing dynamicity, complexity, technique is proposed and becomes one of the most effective and volume of information resources, the recommendation techniques for solving the so-called problem of information overload. Traditional recommendation algorithms, such as collaborative filtering based on the user or item, only measure the degree of similarity between users or items with single criterion, i.e., ratings. According to the experience of previous studies, single criterion cannot accurately measure the similarity between user preferences or items. In recent years, the application of deep learning techniques has gained significant momentum in recommender systems for better understanding of user preferences, item characteristics, and historical interactions. In this work, we integrate plot information as auxiliary information into the denoising autoencoder （DAE）, called SemRe-DCF, which aims at learning semantic representations of item descriptions and succeeds in capturing fine-grained semantic regularities by using vector arithmetic to get better rating prediction. The results manifest that the proposed method can effectively improve the accuracy of prediction and solve the cold start problem.
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