Exploiting context-awareness and multi-criteria decision making to improve items recommendation using a tripartite graph-based model

作者:

Highlights:

• We propose a solution that exploits users’ contextual information and users’ feedbacks on items’ criteria to improve the prediction accuracy of recommender systems.

• We model the multi-dimensional available data in the form of a tripartite graph including three types of connected entities (users, contextual situations and criteria).

• We propose a context-aware multi-criteria recommendation approach that explores the idea of clustering contextually similar users evaluating items with respect to multiple criteria.

• We explore a novel way to predict cluster-based multi-criteria ratings for users involved in similar contextual situations by considering the dependence between contexts and also the correlation between criteria.

• We perform an intensive comparative evaluation with state-of-the-art baselines belonging to four categories of work: single rating based methods, context-aware based rating methods, multi-criteria rating based methods and context-aware multi-criteria rating based methods.

摘要

•We propose a solution that exploits users’ contextual information and users’ feedbacks on items’ criteria to improve the prediction accuracy of recommender systems.•We model the multi-dimensional available data in the form of a tripartite graph including three types of connected entities (users, contextual situations and criteria).•We propose a context-aware multi-criteria recommendation approach that explores the idea of clustering contextually similar users evaluating items with respect to multiple criteria.•We explore a novel way to predict cluster-based multi-criteria ratings for users involved in similar contextual situations by considering the dependence between contexts and also the correlation between criteria.•We perform an intensive comparative evaluation with state-of-the-art baselines belonging to four categories of work: single rating based methods, context-aware based rating methods, multi-criteria rating based methods and context-aware multi-criteria rating based methods.

论文关键词:Recommender systems,Multi-criteria decision,Tripartite graph,Co-clustering,Contextual situation,Rating prediction

论文评审过程:Received 17 November 2021, Revised 24 December 2021, Accepted 30 December 2021, Available online 1 February 2022, Version of Record 1 February 2022.

论文官网地址:https://doi.org/10.1016/j.ipm.2021.102861