A literature review and classification of recommender systems research

By D. Park, Hyeakyeong Kim, I. Choi, Jae Kyeong Kim. 2012

716 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, 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 Vision and Pattern Recognition

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Stable and Fair Classification (2019, background): “One example is recommendation systems that rely on classiﬁcation algorithms [60, 62].”
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].”
Recommender Systems (2016, neutral): “A recent classification of various facets of recommender system research may be found in [462].”
News recommender systems - Survey and roads ahead (2018, background): “Recommenders have been successfully applied in a variety of domains , and the recommendable objects include movies, books, travel and tourism services, research articles, search queries, and many more [9, 15, 144].”
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, background): “Moreover, Park et al. (2012) carried out a review on recommender systems and classified them according to year of publication, journal in which they are published, the application field and data mining techniques.”
Make it personal: A social explanation system applied to group recommendations (2017, background): “There are many domains where group recommendation techniques can be applied (Park et al., 2012).”
Recommender Systems: Introduction and Challenges (2015, background): “papers for additional material [13, 30, 34, 40, 44].”
Recommender systems survey (2013, background): “Recently, RS implementation in the Internet has increased, which has facilitated its use in diverse areas [171].”
Recommendation systems: Principles, methods and evaluation (2015, background): “This is the problem that occurs as a result of lack of enough information, that is, when only a few of the total number of items available in a database are rated by users [34,67].”
Recommender system application developments: A survey (2015, background): “It was pointed out recently that application study is the main research focus of current recommender system research, especially in the current age of big data [1, 13].”
Social Network and Tag Sources Based Augmenting Collaborative Recommender System (2015, background): “Generally, CF approaches can be classiﬁed as either item-based or user-based techniques [1].”