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

By Gediminas Adomavicius, Alexander Tuzhilin. 2005

10,751 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research, Recommender Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Luc Van Gool
3. Xiangnan He
4. Yi Yang
5. Trevor Darrell
6. Xiaogang Wang
7. Ming-Hsuan Yang
8. Tat-seng Chua

In Information Systems

1. Xiangnan He
2. Yongfeng Zhang
3. Tat-seng Chua
4. Charu C. Aggarwal
5. Julian McAuley
6. Xiang Wang
7. Dietmar Jannach
8. Xing Xie

In Management Science and Operations Research

1. Xiangnan He
2. Dietmar Jannach
3. Julian McAuley
4. Harald Steck
5. Charu C. Aggarwal
6. Yehuda Koren
7. George Karypis
8. Tat-seng Chua

In Recommender Systems

1. Julian McAuley
2. Xiangnan He
3. Harald Steck
4. Tat-seng Chua
5. Hao Wang
6. Yongfeng Zhang
7. Xiang Wang
8. Ruining He

Citations in the corpus

• Image-Based Recommendations on Styles and Substitutes (2015, background): “The system shares these characteristics with more common forms of recommender system, but does so on the basis of the appearance of the object, rather than metadata, reviews, or similar.”
• Recurrent Latent Variable Networks for Session-Based Recommendation (2017, background): “Memory-based systems generate predictions under a neighborhood-driven rationale: Item rating prediction for a target user comprises determination of other users with similar ratings (target user neighbors), and computation of a weighted average of the ratings of each item provided by the target user”
• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, background): “Recommender systems [3, 4] have been investigated extensively because of its direct connection to profits of products.”
• Pinocchio: Nearly Practical Verifiable Computation (2013, background): “In a similar vein, the worker’s largest source of overhead is applying the coefficients from the circuit “in the exponent” to compute g Y ( s ) etc.”
• Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015, background): “Such systems are called contextaware recommender systems (see surveys [1, 2, 5, 47] and references therein).”
• Community detection in graphs (2009, background): “…in which customers are linked to the products they have bought, allow to classify customers based on the types of product they purchase more often: this could be used both to organize targeted advertising, as well as to give recommendations about future purchases (Adomavicius and Tuzhilin, 2005).”
• Efficient Private Statistics with Succinct Sketches (2015, background): “These recommendations are usually provided by means of recommender systems [2, 34] like ItemKNN (cf.”
• Horizon: Facebook's Open Source Applied Reinforcement Learning Platform (2018, background): “Data from recommender systems is often sparse, noisy and arbitrarily distributed (Adomavicius & Tuzhilin, 2005).”
• Comparing the Predictive Capability of Social and Interest Affinity for Recommendations (2014, neutral): “Collaborative Filtering (CF) has been widely used by major commercial applications such as Amazon, Movielens, or Netflix [24, 27, 1].”
• Empirical Analysis of the Impact of Recommender Systems on Sales (2010, background): “, [2]) on their effectiveness in increasing sales for retailers who provide these services.”
• Constraint-based recommender systems: technologies and research issues (2008, background): “• Contextual knowledge: Considerable inferential complexity may be involved in teasing out the consequences of particular contexts [2].”
• A survey of active learning in collaborative filtering recommender systems (2016, methodology): “[47] I.S.C. Nicholas, C.K. Nicholas, Combining content and collaboration in text filtering, in: Proceedings of the IJCAI’99 Workshop on Machine Learning for Information Filtering, 1999, pp. 86–91.”
• Relational Collaborative Topic Regression for Recommender Systems (2015, methodology): “Existing RS methods can be roughly categorized into three classes [1], [4], [10], [41]: content-based methods, collaborative filtering (CF) based methods, and hybrid methods.”
• Deep Learning Based Recommender System (2017, background): “Recommendation models are usually classi€ed into three categories [1, 69]: collaborative €ltering, content based and hybrid recommender system.”
• Collaborative Filtering-Based Recommendation of Online Social Voting (2017, background): “Many OSNs now offer the social voting function, through which a user can share with friends her opinions, e.g., like or dislike, on various subjects, ranging from user statuses, profile pictures, to games played, Manuscript received October 22, 2014; revised August 19, 2016; accepted January 24,…”
• Dimensionality Reduction via Matrix Factorization for Predictive Modeling from Large, Sparse Behavioral Data (2015, background): “Collaborative filtering systems accomplish this by recommending items rated highly by users with similar ratings histories (Adomavicius and Tuzhilin 2005).”