  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.
