Context-Similarity Collaborative Filtering Recommendation

Hiep Xuan Huynh, Nghia Quoc Phan, Nghi Mong Pham, Van Huy Pham, Le Hoang Son, Mohamed Abdel-Basset, Mahmoud Ismail

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

This article proposes a new method to overcome the sparse data problem of the collaborative filtering models (CF models) by considering the homologous relationship between users or items calculated on contextual attributes when we build the CF models. In the traditional CF models, the results are built only based on data from the user's ratings for items. The results of the proposed models are calculated on two factors: (1) the similar factors based on rating values; (2) the similar factors based on contextual attributes. The findings from the experimentation on two datasets DePaulMovie and InCarMusic, show that the proposed models have higher accuracy than the traditional CF models.

Original languageEnglish
Article number8998219
Pages (from-to)33342-33351
Number of pages10
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020
Externally publishedYes

Fingerprint

Dive into the research topics of 'Context-Similarity Collaborative Filtering Recommendation'. Together they form a unique fingerprint.

Cite this