Rating prediction in review-based recommendations via adversarial auto-encoder
Recommendation methods usually use users' historical ratings on items to predict ratings on
their unrated items to make recommendations. However, the sparse rating data limit the
recommendation quality. In order to solve the sparsity problem, other auxiliary information is
combined to mine users' preferences for higher recommendation quality. This paper
proposes a novel recommendation model, which harnesses an adversarial learning among
auto-encoders to improve recommendation quality by minimizing the gap of rating and …
their unrated items to make recommendations. However, the sparse rating data limit the
recommendation quality. In order to solve the sparsity problem, other auxiliary information is
combined to mine users' preferences for higher recommendation quality. This paper
proposes a novel recommendation model, which harnesses an adversarial learning among
auto-encoders to improve recommendation quality by minimizing the gap of rating and …
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