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.

中文翻译：

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跨域协同过滤解决用户冷启动问题：利用矩阵分解中的项目元数据

为新用户提供相关的个性化推荐是推荐系统的主要挑战之一。这个被称为用户冷启动的问题已经从不同的角度进行了处理。特别是，跨域推荐方法利用来自源域的数据来解决目标域中缺乏用户偏好的问题。迄今为止提出的大多数跨域方法都遵循协同过滤的范式，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。然而，基于内容的过滤已成功应用于项目内容和元数据起关键作用的领域。此类域不仅限于项目确实具有文本内容的场景（例如，书籍、新闻文章、科学论文和网页），以及经常使用文本挖掘和信息检索技术的地方。潜在的应用领域包括项目具有相关元数据的领域，例如电影的流派、导演和演员，以及歌曲的音乐风格、作曲家和主题。随着语义 Web 及其参考实现 Linked Data 的出现，Web 上提供了大量结构化、相互关联的元数据。这些元数据代表了基于内容和混合过滤方法可利用的潜在信息来源。受将关联数据用于推荐目的的启发，在本文中，我们提出并评估了许多用于跨域协同过滤的矩阵分解模型，这些模型利用元数据作为不同域用户喜欢的项目之间的桥梁。