Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K
  Recommendation   We propose PsiRec, a novel user preference propagation recommender that
incorporates pseudo-implicit feedback for enriching the original sparse
implicit feedback dataset. Three of the unique characteristics of PsiRec are:
(i) it views user-item interactions as a bipartite graph and models
pseudo-implicit feedback from this perspective; (ii) its random walks-based
approach extracts graph structure information from this bipartite graph, toward
estimating pseudo-implicit feedback; and (iii) it adopts a Skip-gram inspired
measure of confidence in pseudo-implicit feedback that captures the pointwise
mutual information between users and items. This pseudo-implicit feedback is
ultimately incorporated into a new latent factor model to estimate user
preference in cases of extreme sparsity. PsiRec results in improvements of
21.5% and 22.7% in terms of Precision@10 and Recall@10 over state-of-the-art
Collaborative Denoising Auto-Encoders. Our implementation is available at
https://github.com/heyunh2015/PsiRecICDM2018.

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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
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The categories of this paper are: physics
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph