Enhancing recommendation competence in nearest neighbour models

R. Latha

Physica A: Statistical Mechanics and its Applications, 2022, vol. 592, issue C

Abstract: Collaborative Filtering approaches are viewed as essential tools to suggest products to users based on historical knowledge. Widely adopted user based Collaborative Filtering approaches rely on ratings provided by similar users of the target user to generate appropriate recommendations. The pair-wise user similarity is based on set of common rated items between users and identifying similar users is a challenging task when the set is small. To improve the recommendation quality in low correlated data, User Trait Model and Bayesian Global Agreement model are suggested in this work. The proposed models assign global agreement score to users. A linear function of any baseline user similarity and global agreement score of users is defined as a new similarity measure. From inception, recommendation approaches are keen on improving accuracy of recommended items and downplays the diversity of recommendations, which results in poor user satisfaction. The proposed models focus on improving accuracy and diversity of recommendations. Experiments are conducted on three benchmark data sets and the results are compared with other user based CF approaches suggested in the literature. The experimental results indicate that the proposed approaches outperform other user based CF approaches based on the evaluation metrics namely, MAE, RMSE, F1 for prediction accuracy, MN, ILD for recommendation diversity and Coverage for the extent of recommendations.

Keywords: Collaborative filtering; Global agreement; Diversity; Bayesian model (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437121009948
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:592:y:2022:i:c:s0378437121009948

DOI: 10.1016/j.physa.2021.126835

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().