Embedding Factorization Models for Jointly Recommending Items and User Generated Lists

Existing recommender algorithms mainly focused on recommending individual items by utilizing user-item interactions. However, little attention has been paid to recommend user generated lists (e.g., playlists and booklists). On one hand, user generated lists contain rich signal about item co-occurrence, as items within a list are usually gathered based on a specific theme. On the other hand, a user's preference over a list also indicate her preference over items within the list. We believe that 1) if the rich relevance signal within user generated lists can be properly leveraged, an enhanced recommendation for individual items can be provided, and 2) if user-item and user-list interactions are properly utilized, and the relationship between a list and its contained items is discovered, the performance of user-item and user-list recommendations can be mutually reinforced. Towards this end, we devise embedding factorization models, which extend traditional factorization method by incorporating item-item (item-item-list) co-occurrence with embedding-based algorithms. Specifically, we employ factorization model to capture users' preferences over items and lists, and utilize embedding-based models to discover the co-occurrence information among items and lists. The gap between the two types of models is bridged by sharing items' latent factors. Remarkably, our proposed framework is capable of solving the new-item cold-start problem, where items have never been consumed by users but exist in user generated lists. Overall performance comparisons and micro-level analyses demonstrate the promising performance of our proposed approaches.