Network-based recommendation algorithms: A review

Abstract

Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use - such as the possible influence of recommendation on the evolution of systems that use it - and finally discuss open research directions and challenges.

review article; 16 pages, 4 figures, 4 tables

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Cited by in corpus (17)

• Ranking in evolving complex networks
• Recommender Systems for Online and Mobile Social Networks: A survey
• Big Networks: A Survey
• Recommender systems based on graph embedding techniques: A comprehensive review
• A trust-based recommendation method using network diffusion processes
• Recommending investors for new startups by integrating network diffusion and investors' domain preference
• The essential role of time in network-based recommendation
• Alleviating the recommendation bias via rank aggregation
• Predictability of diffusion-based recommender systems
• An Adjustable Heat Conduction based KNN Approach for Session-based Recommendation
• Enhancing the long-term performance of recommender system
• LaSER: Language-Specific Event Recommendation
• Improving Recommendation Diversity by Highlighting the ExTrA Fabricated Experts
• TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrix
• Enhancing countries' fitness with recommender systems on the international trade network
• Network-based models for social recommender systems
• Inferring users' preferences through leveraging their social relationships