Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation
This addresses the problem of data sparsity for recommender systems, offering a domain-specific incremental improvement.
The paper tackles data sparsity in top-K recommendation by proposing PsiRec, a novel user preference propagation recommender that incorporates pseudo-implicit feedback, resulting in improvements of 21.5% in Precision@10 and 22.7% in Recall@10 over state-of-the-art methods.
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.