Recent trends in recommender systems: a survey

• Published in: International journal of multimedia information retrieval 2024-12, Vol.13 (4), p.41, Article 41
• Main Authors: Kumar, Chintoo, Chowdary, C. Ravindranath, Meena, Ashok Kumar
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Collaboration; Computer Science; Data Mining and Knowledge Discovery; Database Management; Deep learning; Graphical representations; Image Processing and Computer Vision; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Machine learning; Multimedia Information Systems; Neural networks; Ratings & rankings; Recommender systems; Similarity measures; Social networks; Sparsity; Trends and Surveys; Websites
• ISSN: 2192-6611; 2192-662X
• DOI: 10.1007/s13735-024-00349-1

In an era where the number of choices is overwhelming on the internet, it is crucial to filter, prioritize and deliver relevant information to a user. A recommender system addresses this issue by recommending items that users might like from many available items. Nowadays, the prevalence of providing personalized content to users through a website has increased profoundly. The majority of such websites use recommendation models to reduce a user’s searching time. Many new recommendation models are being proposed to address the changing business requirements of eCommerce organizations. Recommender systems can be broadly classified into three categories, i.e., clustering-based, matrix-factorization-based, and deep learning-based models. Many scopes and use cases are available where recommendation models play a vital role. The advent of graph representation learning and LLMs hinders recommendation models from being more effective in promptly providing relevant suggestions. This survey comprehensively discusses various deep learning-based recommendation models available for different domains. We also discuss the pros and cons of popular recommendation models. We also discuss various open issues of recommender systems and outline a few future directions. This study also provides insight to explore novel and helpful research problems related to recommendation systems.