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arXiv · 1711.10327

Generative Interest Estimation for Document Recommendations

Danijar Hafner·Alexander Immer·Willi Raschkowski·Fabian Windheuser
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Abstract

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.

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BibTeXRIS

Danijar Hafner, Alexander Immer, Willi Raschkowski, Fabian Windheuser. 2017-11-28. Generative Interest Estimation for Document Recommendations. https://arxiv.org/abs/1711.10327

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