Collaborative Variational Autoencoder for Recommender Systems. Li, X. & She, J. ACM, New York, New York, USA, 2017. abstract bibtex Abstract Modern recommender systems usually employ collaborative filtering with rating information to recommend items to users due to its successful performance. However, because of the drawbacks of collaborative-based methods such as sparsity, cold start, etc., more attention has been drawn to hybrid methods that consider both the rating and content information. Most of the previous works in this area cannot learn a good representation from content for recommendation task or consider only text modality of the content, thus their
@Book{Li2017d,
author = {Li, Xiaopeng and She, James},
title = {Collaborative Variational Autoencoder for Recommender Systems},
volume = {},
pages = {305-314},
editor = {},
publisher = {ACM},
address = {New York, New York, USA},
year = {2017},
abstract = {Abstract Modern recommender systems usually employ collaborative filtering with rating information to recommend items to users due to its successful performance. However, because of the drawbacks of collaborative-based methods such as sparsity, cold start, etc., more attention has been drawn to hybrid methods that consider both the rating and content information. Most of the previous works in this area cannot learn a good representation from content for recommendation task or consider only text modality of the content, thus their},
keywords = {}}
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