Research Article Open Access

A Comparative Analysis of Various Approaches for Incorporating Contextual Information into Recommender Systems

Quang-Hung Le1, Son-Lam Vu2 and Anh-Cuong Le3
  • 1 Department of Information Technology, Quy Nhon University, Vietnam
  • 2 Quy Nhon University, Vietnam
  • 3 Natural Language Processing and Knowledge Discovery Laboratory, Faculty of Information Technology, Ton Duc Thang University, Vietnam

Abstract

Recommender systems are being widely applied in many fields, such as e-commerce, e-documents, places and travel, multimedia, news and advertising and transportation. These systems are similar to an information filtering system that helps to identify a set of items that best satisfy the users’ demands based on their preference profiles. The integration of contextual information (e.g., location, weather conditions and user mood) into recommender systems to improve their performance has recently received considerable attention in the research literature. Studies in the relevant literature have focused on incorporating contextual information into conventional recommender systems by employing three approaches: Pre-filtering, post-filtering and modeling. In this paper, we conduct a systematic comparison of various approaches and show how to integrate contextual information into recommender systems. Additionally, we provide an in-depth analysis of the most notable studies to date and point out the strengths, weaknesses and application scenarios for each of the approaches. We also empirically evaluate the real-world datasets, analyzing distinct recommendation quality metrics and characteristics of the datasets. An important result is that accuracy-based comparisons show no clear winner among the approaches.

Journal of Computer Science
Volume 18 No. 3, 2022, 187-203

DOI: https://doi.org/10.3844/jcssp.2022.187.203

Submitted On: 16 December 2021 Published On: 28 March 2022

How to Cite: Le, Q., Vu, S. & Le, A. (2022). A Comparative Analysis of Various Approaches for Incorporating Contextual Information into Recommender Systems. Journal of Computer Science, 18(3), 187-203. https://doi.org/10.3844/jcssp.2022.187.203

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Keywords

  • Recommender Systems
  • Context-Aware Recommender System
  • Context-Awareness
  • Pre-Filtering
  • Post-Filtering
  • Contextual Modeling