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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Yuan Zhang
- sl:arxiv_num : 1912.08422
- sl:arxiv_published : 2019-12-18T07:43:52Z
- sl:arxiv_summary : Recently, the embedding-based recommendation models (e.g., matrix
factorization and deep models) have been prevalent in both academia and
industry due to their effectiveness and flexibility. However, they also have
such intrinsic limitations as lacking explainability and suffering from data
sparsity. In this paper, we propose an end-to-end joint learning framework to
get around these limitations without introducing any extra overhead by
distilling structured knowledge from a differentiable path-based recommendation
model. Through extensive experiments, we show that our proposed framework can
achieve state-of-the-art recommendation performance and meanwhile provide
interpretable recommendation reasons.@en
- sl:arxiv_title : Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation@en
- sl:arxiv_updated : 2019-12-18T07:43:52Z
- sl:bookmarkOf : https://arxiv.org/abs/1912.08422
- sl:creationDate : 2020-05-12
- sl:creationTime : 2020-05-12T11:11:16Z
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