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

机译：用跨域协同过滤解决用户冷启动：利用矩阵分解中的项目元数据

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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建议是推荐系统中的主要挑战之一。从不同的角度接近了这个问题，被称为用户冷启动。特别是，跨域推荐方法从源域利用数据来解决目标域中缺少用户偏好。到目前为止提出的大多数跨域方法遵循协同滤波的范例，并避免分析物品的内容，这些物品通常在跨域设置中的高度异构。但是，基于内容的过滤已成功应用于项目内容和元数据播放关键角色的域中。此类域不仅限于项目确实具有文本内容的情况（例如，书籍，新闻文章，科学论文和网页），并且通常使用文本挖掘和信息检索技术。潜在的应用领域包括项目具有相关的元数据，例如电影的流派，董事和演员，以及歌曲的音乐风格，音乐制度和主题。随着语义Web的出现，及其参考实现链接数据，Web上有一个结构化的互连元数据。这些元数据表示要被基于内容和混合滤波方法利用的潜在信息来源。通过使用链接数据的推荐目的，在本文中，我们展示并评估了许多用于跨域协同滤波的矩阵分解模型，该模型将元数据作为不同域中的用户所喜好的项目之间的桥梁。我们表明，如果潜在的知识图表从不同域连接的项目，然后在跨域信息中受益的情况下，我们的模型可以为新用户提供更好的建议，同时在建议准确性和多样性之间保持良好的权衡。

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