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Cross-domain recommender system using generalized canonical correlation analysis
Knowledge and Information Systems ( IF 2.5 ) Pub Date : 2020-09-14 , DOI: 10.1007/s10115-020-01499-4
Seyed Mohammad Hashemi , Mohammad Rahmati

Recommender systems provide personalized recommendations to the users from a large number of possible options in online stores. Matrix factorization is a well-known and accurate collaborative filtering approach for recommender system, which suffers from cold-start problem for new users and items. When new users join the system, it will take some time before they enter some ratings in the system, until that time, there are not enough ratings to learn the matrix factorization model. Using auxiliary data such as user’s demographic, ratings and reviews in relevant domains, is an effective solution to reduce the new user problem. In this paper, we used the data of users activity from auxiliary domains to build domain-independent users representation that could be used to predict users ratings in the target domains. We proposed an iterative method which applied MAX-VAR generalized canonical correlation analysis (GCCA) on user’s latent factors learned from matrix factorization on each domain. Also, to improve the capability of GCCA to learn latent factors for new users, we propose a generalized canonical correlation analysis by inverse sum of selection matrices (GCCA-ISSM) approach, which provides better recommendations in cold-start scenarios. The proposed approach is extended using content-based features like topic models extracted from user’s reviews. We demonstrate the accuracy and effectiveness of the proposed approaches on cross-domain rating predictions using comprehensive experiments on Amazon and MovieLens datasets.



中文翻译:

使用广义规范相关分析的跨域推荐系统

推荐系统从在线商店中的大量可能选项中向用户提供个性化推荐。矩阵分解是一种针对推荐器系统的众所周知且精确的协作过滤方法,该方法遭受新用户和新商品的冷启动问题。当新用户加入系统时,要花一些时间才能在系统中输入一些等级,直到那时为止,没有足够的等级来学习矩阵分解模型。在相关领域中使用辅助数据(例如用户的人口统计信息,评分和评论)是减少新用户问题的有效解决方案。在本文中,我们使用了来自辅助域的用户活动数据来构建独立于域的用户表示形式,可以用来预测目标域中的用户评分。我们提出了一种迭代方法,该方法将MAX-VAR广义规范相关分析(GCCA)应用于在每个域上从矩阵分解中学习到的用户潜在因子。此外,为了提高GCCA学习新用户潜在因素的能力,我们提出了一种基于选择矩阵逆和的广义规范相关分析(GCCA-ISSM),在冷启动场景中提供了更好的建议。使用基于内容的功能(如从用户评论中提取的主题模型)扩展了该方法。我们使用Amazon和MovieLens数据集上的综合实验,论证了所提出方法在跨域评级预测中的准确性和有效性。我们通过选择矩阵逆和(GCCA-ISSM)方法提出了一种广义规范相关分析,它在冷启动场景中提供了更好的建议。使用基于内容的功能(如从用户评论中提取的主题模型)扩展了该方法。我们使用Amazon和MovieLens数据集上的综合实验,论证了所提出方法在跨域评级预测中的准确性和有效性。我们通过选择矩阵逆和(GCCA-ISSM)方法提出了一种广义规范相关分析,它在冷启动场景中提供了更好的建议。所提出的方法使用基于内容的功能(例如从用户评论中提取的主题模型)进行了扩展。我们使用Amazon和MovieLens数据集上的综合实验,论证了所提出方法在跨域评级预测中的准确性和有效性。

更新日期:2020-09-14
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