Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation Academic Article

abstract

• 2018 IEEE. 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.

authors

• Caverlee, James

published proceedings

• 2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)

author list (cited authors)

• He, Y., Chen, H., Zhu, Z., & Caverlee, J.

citation count

• 5

complete list of authors

• He, Yun||Chen, Haochen||Zhu, Ziwei||Caverlee, James

publication date

• November 2018

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining  Journal

keywords

• Collaborative Filtering
• Data Sparsity
• Implicit Feedback
• Random Walk
• Recommender Systems

Digital Object Identifier (DOI)

• 10.1109/ICDM.2018.00129

start page

• 1025

end page

• 1030

volume

• 2018-November

URL

• http://dx.doi.org/10.1109/icdm.2018.00129