Wasserstein Autoencoders for Collaborative Filtering
The recommender systems have long been investigated in the literature.
Recently, users' implicit feedback like click' or browse' are considered to
be able to enhance the recommendation performance. Therefore, a number of
attempts have been made to resolve this issue. Among them, the variational
autoencoders (VAE) approach already achieves a superior performance. However,
the distributions of the encoded latent variables overlap a lot which may
restrict its recommendation ability. To cope with this challenge, this paper
tries to extend the Wasserstein autoencoders (WAE) for collaborative filtering.
Particularly, the loss function of the adapted WAE is re-designed by
introducing two additional loss terms: (1) the mutual information loss between
the distribution of latent variables and the assumed ground truth distribution,
and (2) the L1 regularization loss introduced to restrict the encoded latent
variables to be sparse. Two different cost functions are designed for measuring
the distance between the implicit feedback data and its re-generated version of
data. Experiments are valuated on three widely adopted data sets, i.e., ML-20M,
Netflix and LASTFM. Both the baseline and the state-of-the-art approaches are
chosen for the performance comparison which are Mult-DAE, Mult-VAE, CDAE and
Slim. The performance of the proposed approach outperforms the compared methods
with respect to evaluation criteria Recall@1, Recall@5 and NDCG@10, and this
demonstrates the efficacy of the proposed approach.
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Collaborative FilteringRecommendation SystemsMethods 이 논문이 사용한 방법론
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