基于耦合相似度的矩阵分解推荐方法

doi:10.11896/j.issn.1002-137X.2016.04.050

摘要/Abstract

摘要： 随着因特网和信息技术的高速发展,信息过载现象越来越严重。推荐系统能够给个人和商家(例如电子商务和零售商)提供个性化的推荐。数据稀疏性和分数预测质量问题被公认为是现存推荐系统中的主要挑战。当前绝大多数推荐系统技术都依赖于协同过滤方法,它主要利用用户-项目评分矩阵来表示用户和项目之间的关系。一些研究利用附加信息来提高推荐准确性,但是,绝大多数现存的引入项目之间关系的方法并不能很好地用于预测和推荐,因为其假设项目属性之间是独立同分布的,而实际上项目(或用户)的属性之间是存在耦合关系的。由此提出了基于属性耦合关系的矩阵分解模型,它能有效地刻画项目之间的耦合相关性,从而更加合理地预测用户对项目的评分。实验结果表明,所提出的模型在热启动和冷启动的推荐准确性方面均优于传统的推荐算法。

关键词: 推荐系统,相似度,矩阵分解,冷启动,预测

Abstract: With the rapid development of Internet and information technology,information overload becomes more and more seriously.Recommender system can provide personalized recommendations to both individual users and businesses (such as e-commerce and retail enterprises).The data sparsity and prediction quality are recognized as the key challenges in the existing recommender systems.Most of the existing recommender systems depend on collaborating filtering (CF) method,which mainly uses the user-item rating matrix to represent the relationship between users and items.Se-veral researches consider utilizing extra information to improve the accuracy.However,most of the existing methods usually fail to provide accurate information for predicting recommendations,as there is an assumption that the relationship between attributes of items is independent and identically distributed,while,there are often several kinds of coupling relationships or connections existing among items or users in real applications.This paper incorporated the coupling relationship analysis to capture under-discovered relationships of items and aimed to make the ratings more reasonable.This paper proposed a coupled attribute-based matrix factorization model,which can capture the coupling correlations between items effectively.The experimental evaluations demonstrate the proposed algorithms outperform the state-of-the-art algorithms in the warm start and cold start settings.

Key words: Recommender systems,Similarity,Matrix factorization,Cold-start,Predicting

郭梦娇,孙劲光,孟祥福. 基于耦合相似度的矩阵分解推荐方法[J]. 计算机科学, 2016, 43(4): 247-251. https://doi.org/10.11896/j.issn.1002-137X.2016.04.050

GUO Meng-jiao, SUN Jing-guang and MENG Xiang-fu. Coupling Similarity-based Matrix Factorization Technique for Recommendation[J]. Computer Science, 2016, 43(4): 247-251. https://doi.org/10.11896/j.issn.1002-137X.2016.04.050

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