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Improving the Performance of Collaborative Filtering with Category-Specific Neighborhood

机译：通过类别特定邻域提高协作过滤的性能

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摘要

Recommender system (RS) helps customers to select appropriate products from millions of products and has become a key component in e-commerce systems. Collaborative filtering (CF) based approaches are widely employed to build RSs. In CF, recommendation to the target user is computed after forming the corresponding neighbourhood of users. Neighborhood of a target user is extracted based on the similarity between the product rating vector of the target user and the product rating vectors of individual users. In CF, the methodology employed for neighborhood formation influences the performance. In this paper, we have made an effort to improve the performance of CF by proposing a different approach to compute recommendations by considering two kinds of neighborhood. One is the neighborhood by considering the product ratings of the user as a single vector and the other is based on the neighborhood of the corresponding virtual users. For the target user, the virtual users are formed by dividing the ratings based on the category of products. We have proposed a combined approach to compute better recommendations by considering both kinds of neighborhoods. The experiments results on real world MovieLens dataset show that the proposed approach improves the performance over CF.
机译：推荐系统（RS）帮助客户从数百万种产品中选择合适的产品，并且已成为电子商务系统中的关键组件。基于协作过滤（CF）的方法已广泛用于构建RS。在CF中，在形成相应的用户邻域之后，计算对目标用户的推荐。基于目标用户的产品评价向量与各个用户的产品评价向量之间的相似度来提取目标用户的邻域。在CF中，用于邻域形成的方法会影响性能。在本文中，我们通过提出一种通过考虑两种邻域来计算推荐量的不同方法，努力提高了CF的性能。一个是通过将用户的产品评分视为单个向量的邻域，另一个是基于相应虚拟用户的邻域的邻域。对于目标用户，通过根据产品类别划分等级来形成虚拟用户。我们提出了一种综合方法，通过考虑两种邻域来计算更好的建议。在真实世界的MovieLens数据集上的实验结果表明，所提出的方法提高了CF的性能。

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