Exploiting context-awareness and multi-criteria decision making to improve items recommendation using a tripartite graph-based model. Issue 2 (March 2022)

• Record Type: Journal Article
• Authors: Dridi, Rim; Tamine, Lynda; Slimani, Yahya
• Is Part Of: Information processing & management. Volume 59:Issue 2(2022)
• Publication Date: 2022-03
• Subjects: Recommender systems -- Multi-criteria decision -- Tripartite graph -- Co-clustering -- Contextual situation -- Rating prediction; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2021.102861
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: Integrating useful input information is essential to provide efficient recommendations to users. In this work, we focus on improving items ratings prediction by merging both multiple contexts and multiple criteria based research directions which were addressed separately in most existent literature. Throughout this article, Criteria refer to the items attributes, while Context denotes the circumstances in which the user uses an item. Our goal is to capture more fine grained preferences to improve items recommendation quality using users' multiple criteria ratings under specific contextual situations. Therefore, we examine the recommenders' data from the graph theory based perspective by representing three types of entities (users, contextual situations and criteria) as well as their relationships as a tripartite graph. Upon the assumption that contextually similar users tend to have similar interests for similar item criteria, we perform a high-order co-clustering on the tripartite graph for simultaneously partitioning the graph entities representing users in similar contextual situations and their evaluated item criteria. To predict cluster-based multi-criteria ratings, we introduce an improved rating prediction method that considers the dependency between users and their contextual situations, and also takes into account the correlation between criteria in the prediction process. The predicted multi-criteria ratings are finally aggregated into a single representative output corresponding to an overall item rating. To guide our investigation, we create a research hypothesis to provide insights about the tripartite graph partitioning and design clear and justified preliminary experiments including quantitative and qualitative analyzes to validate it. Further thorough experiments on the two available context-aware multi-criteria datasets, TripAdvisor and Educational, demonstrate that our proposal exhibits substantial improvements over alternative recommendations approaches. 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.