LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text
This work addresses the problem of data sparsity in recommender systems for users and platforms, presenting an incremental improvement over existing methods.
The paper tackles the sparsity issue in collaborative filtering by integrating unstructured text content with user-item interaction data, resulting in improved performance on real-world datasets as measured by hit ratio and NDCG metrics.
Collaborative filtering (CF) is the key technique for recommender systems. Pure CF approaches exploit the user-item interaction data (e.g., clicks, likes, and views) only and suffer from the sparsity issue. Items are usually associated with content information such as unstructured text (e.g., abstracts of articles and reviews of products). CF can be extended to leverage text. In this paper, we develop a unified neural framework to exploit interaction data and content information seamlessly. The proposed framework, called LCMR, is based on memory networks and consists of local and centralized memories for exploiting content information and interaction data, respectively. By modeling content information as local memories, LCMR attentively learns what to exploit with the guidance of user-item interaction. On real-world datasets, LCMR shows better performance by comparing with various baselines in terms of the hit ratio and NDCG metrics. We further conduct analyses to understand how local and centralized memories work for the proposed framework.