Collaborative filtering recommender systems

By Mehrbakhsh Nilashi, Karamollah Bagherifard, Othman bin Ibrahim, H. Alizadeh, L. Nojeem, Nazanin Roozegar. 2013

1,869 citations recorded by the source corpus.

Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decision- making processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). Recommender systems are used to address the Information Overload (IO) problem by recommending potentially interesting or useful items to users. They have proven to be worthy tools for online users to deal with the IO and have become one of the most popular and powerful tools in E-commerce. Many existing recommender systems rely on the Collaborative Filtering (CF) and have been extensively used in E-commerce .They have proven to be very effective with powerful techniques in many famous E-commerce companies. This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research

How researchers see this paper

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

In Computer Networks and Communications

1. François Taïani
2. Xing Xie
3. Víctor Martínez
4. Feng Wang
5. Juan C. Cubero
6. Mahdi Jalili
7. Quan Z. Sheng
8. Anne-Marie Kermarrec

In Information Systems

1. Xing Xie
2. Fuzheng Zhang
3. Xiangnan He
4. Le Wu
5. Xiting Wang
6. Feng Wang
7. Guanliang Chen
8. Víctor Martínez

In Management Science and Operations Research

1. Xing Xie
2. Feng Wang
3. Bela Gipp
4. Mahdi Jalili
5. Sirui Yao
6. Quan Z. Sheng
7. Anne-Marie Kermarrec
8. Li Chen

Citations in the corpus

• Hybrid recommender systems: A systematic literature review (2017, background): “man Computer Interaction (HCI) or Information Retrieval (IR) [2].”
• A Survey of Link Prediction in Complex Networks (2016, methodology): “Link prediction techniques are used to improve similar users’ selection in recommender systems that follow a collaborative approach, leading to better recommendation results [Schafer et al. 2007].”
• Collaborative Knowledge Base Embedding for Recommender Systems (2016, background): “2939673 filtering (CF) based methods, which make use of historical interactions or preferences, have made significant success [23].”
• Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, methodology): “The most widely used algorithm for collaborative filtering is the k Nearest Neighbors (kNN) (Adomavicius and Tuzhilin 2005; Schafer et al. 2007; Bobadilla et al. 2011).”
• A Reinforcement Learning Framework for Explainable Recommendation (2018, background): “Studies on explainable recommendation suggest that a user will be overwhelmed if s/he is presented with too much information within an explanation [21], [22].”
• Optimized Cost per Click in Taobao Display Advertising (2017, background): “Different from the recommendation [20] not involving advertisers, the matching service recalling related users has to reflect the advertisers’ bidding will and ensuring market depth.”
• Recommender Systems (2016, neutral): “Detailed surveys and books on the topic may be found in [5, 46, 88, 275, 291, 307, 364, 378, 505, 529, 570].”
• Want to scale in centralized systems? Think P2P (2015, methodology): “In (user-based) collaborative filtering [31], a KNN graph connects each user u to a user v if v is one of the k nearest neighbors of u in the considered application.”
• Beyond Parity: Fairness Objectives for Collaborative Filtering (2017, background): “The fundamental assumption behind collaborative filtering is that other users’ opinions can be selected and aggregated in such a way as to provide a reasonable prediction of the active user’s preference [7].”
• A Survey on Session-based Recommender Systems (2019, background): “A variety of RS research areas have emerged with great success, such as content-based RSs [2, 84], collaborative filtering based RSs [24, 98], and hybrid RSs [8] which combine the first two.”
• Recommendation systems: Principles, methods and evaluation (2015, background): “Also, CF technique has the ability to provide serendipitous recommendations, which means that it can recommend items that are relevant to the user even without the content being in the user’s profile [65].”