WeightedSLIM: A Novel Item-Weights Enriched Baseline Recommendation Model-Reference-Cited by-同舟云学术

WeightedSLIM: A Novel Item-Weights Enriched Baseline Recommendation Model

Published:2024-02-14 Issue: Volume:12 Page:201-210
ISSN:2415-1521
Container-title:WSEAS TRANSACTIONS ON COMPUTER RESEARCH
language:en
Short-container-title:

Author:

Zhang Haiyang¹,Zeng Xinyi²,Ganchev Ivan³

Affiliation:

1. Department of Computing, Xi’an Jiaotong, Liverpool University, Suzhou, CHINA

2. College of Artificial Intelligence, North China University of Science and Technology, Tangshan, CHINA

3. University of Plovdiv “Paisii Hilendarski”, Plovdiv, BULGARIA

Abstract

This paper proposes a novel weight-enriched ranking-based baseline model, WeightedSLIM, aiming to provide more accurate top-N recommendations from implicit user feedback. The model utilizes ItemRank to calculate the ranking score of each item, which is then used as an item weight within the Sparse Linear Model (SLIM), while using Bayesian Personalized Ranking (BPR) to optimize the item similarity matrix. Experiments, conducted for performance comparison of the proposed model with existing recommendation models, demonstrate that it can indeed provide better recommendations and can be used as a strong baseline for top-N recommendations.

Publisher

World Scientific and Engineering Academy and Society (WSEAS)

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