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arXiv · 1709.07545

Attention-based Mixture Density Recurrent Networks for History-based Recommendation

Tian Wang·Kyunghyun Cho
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Abstract

The goal of personalized history-based recommendation is to automatically output a distribution over all the items given a sequence of previous purchases of a user. In this work, we present a novel approach that uses a recurrent network for summarizing the history of purchases, continuous vectors representing items for scalability, and a novel attention-based recurrent mixture density network, which outputs each component in a mixture sequentially, for modelling a multi-modal conditional distribution. We evaluate the proposed approach on two publicly available datasets, MovieLens-20M and RecSys15. The experiments show that the proposed approach, which explicitly models the multi-modal nature of the predictive distribution, is able to improve the performance over various baselines in terms of precision, recall and nDCG.

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Tian Wang, Kyunghyun Cho. 2017-09-22. Attention-based Mixture Density Recurrent Networks for History-based Recommendation. https://arxiv.org/abs/1709.07545

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