WeightedSLIM: A Novel Item-Weights Enriched Baseline Recommendation Model

Haiyang Zhang*, Xinyi Zeng, Ivan Ganchev

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)201-210
Number of pages10
JournalWSEAS Transactions on Computer Research
Publication statusPublished - 2024


  • Bayesian Personalized Ranking (BPR)
  • implicit feedback
  • ItemRank
  • ranking-based recommendation
  • SLIM
  • WeightedSLIM


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