Recommender systems survey

By Jesús Bobadilla, Fernando Ortega, Antonio Hernando, Abraham Gutiérrez. Knowledge-Based Systems, 2013

3,186 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Recommender Systems, Signal Processing

View Paper: https://doi.org/10.1016/J.KNOSYS.2013.03.012

How researchers see this paper

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

In Computer Science Applications

1. Alexandra Olteanu
2. Carlos Castillo
3. Xiangnan He
4. Bernardo Pereira Nunes
5. Qing Wang
6. Ghodai M. Abdelrahman
7. M. M. Afsar
8. Juan A. Gómez-Pulido

In Information Systems

1. Xiangnan He
2. Xing Xie
3. Tat-seng Chua
4. Xiang Wang
5. Dit-Yan Yeung
6. Fuzheng Zhang
7. Yongfeng Zhang
8. Hao Wang

In Recommender Systems

1. Xiangnan He
2. Xing Xie
3. Dit-Yan Yeung
4. Hao Wang
5. Tat-seng Chua
6. Hongwei Wang
7. Fuzheng Zhang
8. Miao Zhao

In Signal Processing

1. Yu Zheng
2. João Gama
3. Xing Xie
4. Qiang Yang
5. Indrė Žliobaitė
6. Susan Athey
7. Wei-Ying Ma
8. Jie Lu

Citations in the corpus

• Collaborative Deep Learning for Recommender Systems (2014, methodology): “Existing methods for RS can roughly be categorized into three classes [6]: content-based methods, collaborative filtering (CF) based methods, and hybrid methods.”
• 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.”
• Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017, background): “An effective recommender system is desired to be able to deal with information overload [2] by precisely locatingwhat votings favor each usermost, thus improves user experience and maximizes user engagement in votings.”
• Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Alternatively, general patterns can be probed across different classes of data (Bobadilla et al., 2013; Cohen and Ruths, 2013), as important variations may exist not only across datasets, but also within datasets due to differences in the demographics of users (Cohen and Ruths, 2013) or the types of…”
• Counterfactual inference for consumer choice across many product categories (2019, background): “Domencich and McFadden’s (1975) pioneering analysis of transportation choice articulated the benefits of using choice data to estimate latent parameters of user utility functions (see also Hausman and Wise, 1978): once estimated, a model of user utility can be used to analyze counterfactual scenario”
• Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System (2019, methodology): “To comprehensively compare the performance metrics we use the standard evaluation metrics [28] Table 2 shows test set performance metrics averaged across users over 10 rounds of the model rebuilds for the two real datasets and simulated dataset.”
• Recommender Systems: Introduction and Challenges (2015, background): “papers for additional material [13, 30, 34, 40, 44].”
• Hybrid recommender systems: A systematic literature review (2017, background): “As mentioned in introduction, similar increased academic interest in RSs is also reported by other surveys like [14] or [15].”
• Fake Co-visitation Injection Attacks to Recommender Systems (2017, methodology): “We refer readers to surveys [13, 14] on recommender systems for details.”
• Alors: An algorithm recommender system (2017, background): “Collaborative filtering (Su and Khoshgoftaar (2009); Bobadilla et al. (2013); section 3), popularized by the Netflix challenge (Bennett and Lanning, 2007), exploits the user data (the items she liked/disliked in the past) and the community data (recording which user liked which items), to recommend…”
• Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration (2017, background): “‘ere are two main solutions to recommender systems, namely, collaborative €ltering (CF) based and content based (CB) recommendation [9, 44].”
• Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review (2020, background): “In the context of students’ performance, the information can be acquired explicitly (by collecting users’ scores) or implicitly (by monitoring users’ behaviour, such as visits to teaching materials, documents downloaded, etc) [69].”
• Collaborative Knowledge Base Embedding for Recommender Systems (2016, background): “To tackle these problems, hybrid recommender systems, which combine collaborative filtering and auxiliary information such as item content, can usually achieve better recommendation results and have gained increasing popularity in recent years [2].”
• Recommender system application developments: A survey (2015, background): “[1] reviewed fundamental recommendation, evaluation, social filtering, and group recommendation techniques, as well as several recently-developed techniques such as the location-aware and bio-inspired recommendation techniques.”
• Discrete Collaborative Filtering (2016, background): “For comprehensive reviews of CF and hashing, please refer to [5] and [29], respectively.”