2017/11/28 by Danijar Hafner, Alexander Immer, Hafner, Danijar +5
Computer Science · Mathematics · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music and Audio Processing #Recommender Systems and Techniques #Topic Modeling #cs.CL #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1711.10327
arxiv created 2017/11/28 · openalex publication_date 2017/11/28 · arxiv updated 2017/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Learning distributed representations of documents has pushed the state-of-the-art in several natural language processing tasks and was successfully applied to the field of recommender systems recently. In this paper, we propose a novel content-based recommender system based on learned representations and a generative model of user interest. Our method works as follows: First, we learn representations on a corpus of text documents. Then, we capture a user's interest as a generative model in the space of the document representations. In particular, we model the distribution of interest for each user as a Gaussian mixture model (GMM). Recommendations can be obtained directly by sampling from a user's generative model. Using Latent semantic analysis (LSA) as comparison, we compute and explore document representations on the Delicious bookmarks dataset, a standard benchmark for recommender systems. We then perform density estimation in both spaces and show that learned representations outperform LSA in terms of predictive performance.