Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

2005/04/25 by Gediminas Adomavičius, Alexander Tuzhilin · 10,312 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Collaborative filtering #Computer science #Data science #Field (mathematics) #Image Retrieval and Classification Techniques #Information retrieval #Process (computing) #Range (aeronautics) #Recommender Systems and Techniques #Recommender system

paper · doi:10.1109/tkde.2005.99

published in IEEE Transactions on Knowledge and Data Engineering 17(6), 734-749 (IEEE Computer Society)

openalex publication_date 2005/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

Citations

• Preference-based Graphic Models for Collaborative Filtering
• An MDP-based Recommender System
• Probabilistic Latent Semantic Analysis
• Evaluating collaborative filtering recommender systems
• An overview of data warehousing and OLAP technology
• Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments
• Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach
• Empirical Analysis of Predictive Algorithms for Collaborative Filtering
• Splines minimizing rotation-invariant semi-norms in Sobolev spaces
• Item-based top-Nrecommendation algorithms

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• ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
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• A Survey of Personalization: From RAG to Agent
• IMPROVED COLLABORATIVE FILTERING ALGORITHM VIA INFORMATION TRANSFORMATION
• Formal concept analysis [wikipedia]