IU-PMF: Probabilistic Matrix Factorization Model Fused with Item Similarity and User Similarity

Shi, Yilong; Lin, Hong; Li, Yuqiang

doi:10.1007/978-3-319-68542-7_65

Cited by 2 publications

(1 citation statement)

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“…Predictions from UbCF and IbCF are combined through simple and weighted averaging and performance of these fusion approaches is compared with the performance of UbCf and IbCF when implemented individually. In the same way, [32] presents the IU-PMF model fusing Item Similarity and User Similarity into the PMF model to make more personalized and accurate recommendations. It combines the merits of both methods.…”

Section: Related Workmentioning

confidence: 99%

A Collaborative Filtering Approach Based on Naïve Bayes Classifier

Valdiviezo-Díaz

Ortega

Cobos³

et al. 2019

IEEE Access

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Recommender system is an information filtering tool used to alleviate information overload for users on the web. Collaborative filtering recommends items to users based on their historical rating information. There are two approaches: memory-based, which usually provides inaccurate but explainable recommendations; and model-based, whose recommendations are more precise but hard to understand. Here we propose a Bayesian model that not only provides us with recommendations as good as matrix factorization models, but these predictions can also be explained. The model is based on both user-based and item-based collaborative filtering approaches, which recommends items by using similar users' and items' information, respectively. Experiments carried out using four datasets present good results compared to several state-ofthe-art baselines, achieving the best performance using the Normalized Discounted Cumulative Gain (nDCG) quality measure and also improving the prediction's accuracy in some datasets.

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Section: Related Workmentioning

confidence: 99%