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Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization

机译：通过跨域协作过滤解决用户冷门问题：在矩阵分解中利用项目元数据

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摘要

Providing relevant personalized recommendations for new users is one of the major challenges in recommender systems. This problem, known as the user cold start has been approached from different perspectives. In particular, cross-domain recommendation methods exploit data from source domains to address the lack of user preferences in a target domain. Most of the cross-domain approaches proposed so far follow the paradigm of collaborative filtering, and avoid analyzing the contents of the items, which are usually highly heterogeneous in the cross-domain setting. Content-based filtering, however, has been successfully applied in domains where item content and metadata play a key role. Such domains are not limited to scenarios where items do have text contents (e.g., books, news articles, scientific papers, and web pages), and where text mining and information retrieval techniques are often used. Potential application domains include those where items have associated metadata, e.g., genres, directors and actors for movies, and music styles, composers and themes for songs. With the advent of the Semantic Web, and its reference implementation Linked Data, a plethora of structured, interlinked metadata is available on the Web. These metadata represent a potential source of information to be exploited by content-based and hybrid filtering approaches. Motivated by the use of Linked Data for recommendation purposes, in this paper we present and evaluate a number of matrix factorization models for cross-domain collaborative filtering that leverage metadata as a bridge between items liked by users in different domains. We show that in case the underlying knowledge graph connects items from different domains and then in situations that benefit from cross-domain information, our models can provide better recommendations to new users while keeping a good trade-off between recommendation accuracy and diversity.
机译：为新用户提供相关的个性化推荐是推荐系统中的主要挑战之一。已经从不同的角度解决了这个称为用户冷启动的问题。特别地，跨域推荐方法利用来自源域的数据来解决目标域中用户偏好的缺乏。迄今为止，提出的大多数跨域方法都遵循协作过滤的范式，并避免分析项目的内容，这些内容在跨域设置中通常是高度异构的。但是，基于内容的筛选已成功应用于项目内容和元数据起关键作用的域中。这样的领域不限于其中项目确实具有文本内容（例如，书籍，新闻文章，科学论文和网页），并且经常使用文本挖掘和信息检索技术的场景。潜在的应用领域包括那些项目具有相关元数据的领域，例如流派，电影的导演和演员，音乐的风格，歌曲的作曲家和主题。随着语义Web及其参考实现“链接数据”的出现，Web上提供了许多结构化，相互链接的元数据。这些元数据代表了基于内容的过滤和混合过滤方法可利用的潜在信息源。受链接数据用于推荐目的的激励，在本文中，我们介绍并评估了许多用于跨域协作过滤的矩阵分解模型，这些模型利用元数据作为不同域中用户喜欢的项目之间的桥梁。我们表明，如果基础知识图连接了来自不同域的项目，然后在受益于跨域信息的情况下，我们的模型可以为新用户提供更好的推荐，同时在推荐准确性和多样性之间保持良好的权衡。

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