We present a collection recommender system that can automatically create and recommend collections of items at a user level. Unlike regular recommender systems, which output top-N relevant items, a collection recommender system outputs collections of items such that the items in the collections are relevant to a user, and the items within a collection follow a specific theme. Our system builds on top of the user-item representations learnt by item recommender systems. We employ dimensionality reduction and clustering techniques along with intuitive heuristics to create collections with their ratings and titles. We test these ideas in a real-world setting of music recommendation, within a popular music streaming service. We find that there is a 2.3x increase in recommendation-driven consumption when recommending collections over items. Further, it results in effective utilization of real estate and leads to recommending a more and diverse set of items. To our knowledge, these are first of its kind experiments at such a large scale.

中文翻译：

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自动创建和推荐馆藏

我们提供了一个集合推荐器系统，该系统可以在用户级别自动创建和推荐项目集合。与输出前N个相关项目的常规推荐系统不同，集合推荐系统输出项目的集合，以使集合中的项目与用户相关，并且集合中的项目遵循特定主题。我们的系统建立在项目推荐系统学习到的用户项目表示之上。我们采用降维和聚类技术以及直观的试探法来创建具有其评分和标题的收藏集。我们在流行音乐流媒体服务中的真实音乐推荐环境中测试了这些想法。我们发现，在对商品进行推荐时，推荐驱动的消费量增长了2.3倍。更多，它可以有效利用房地产，并可以推荐更多不同的商品。就我们所知，这些都是大规模的同类实验。