Recommender Systems

By Claudio Lucchese, Cristina Ioana Muntean, Raffaele Perego, Fabrizio Silvestri, Hossein Vahabi, Rossano Venturini. 2021

3,890 citations recorded by the source corpus.

Recommender system (RS) has emerged to one of the most used technology in the recent years. This paper consist overview of recommender system, types of RS, problems in RS and future scope of RS. The main purpose of this paper is to spot the research trend in RS. More than 1,000 research papers, published by ACM, IEEE, Elsevier, and Springer since 2011 to the first quarter of 2020, have been considered. While learning the Recommender system several interesting finding come out which will help current and future RS researchers to assess and set their research roadmap. Furthermore, this paper also envisions the future of Recommender System which may open up new research directions in this domain.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity (2009, neutral): “Of the many products available, sales have concentrated among a small number of hits.”
Hybrid Recommender Systems: Survey and Experiments (2002, neutral): “Recommender systems were originally deﬁned as ones in which ’people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients’ [1].”
Learning to rank relevant files for bug reports using domain knowledge (2014, methodology): “This collaborative (cid:12)ltering eﬀect has been used before to improve the accuracy of recommender systems [28], consequently it is expected to be bene(cid:12)cial in our retrieval setting, too.”
Evaluating collaborative filtering recommender systems (2004, neutral): “Recommender systems use the opinions of a community of users to help individuals in that community more effectively identify content of interest from a potentially overwhelming set of choices [Resnick and Varian 1997].”
Graph Neural Networks for Social Recommendation (2019, background): “nformation through those around them, such as classmates, friends, or colleagues, implying that the underlying social relations of users can play a significant role in helping them filter information [23]. Hence, social relations have been proven to be helpful in boosting the recommendation performa”
Hybrid Web Recommender Systems (2007, positive): “Recommender systems are personalized information agents that provide recommendations: suggestions for items likely to be of use to a user [18, 41, 42].”
Context-Aware Recommendations in the Mobile Tourist Application COMPASS (2004, background): “Resnick and Varian [10] define recommender systems as systems that use opinions of a community of users to help individuals in that community more effectively identify content of interest from a potentially overwhelming set of choices.”
The "I Designed It Myself" Effect in Mass Customization (2010, background): “Recommender systems that compare the customer’s proﬁle to certain reference characteristics, seek to predict the optimal product based on this information (Resnick and Varian 1997, Adomavicius and Tuzhilin 2005), and therefore further reduce design costs might not enable such feelings at all.”
Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks (2010, background): “Broadly speaking, a recommender system provides specific suggestions about items (products or actions) within a given domain, which may be considered of interest to the given active user [1].”
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].”
The science of science: from the perspective of complex systems (2017, methodology): “Such a research topic is strongly connected to many other fields such as online product recommendation [222], biological network reconstruction [254,255] and community detection [64].”
Constraint-based recommender systems: technologies and research issues (2008, background): “Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services.”
Investigating interactions of trust and interest similarity (2007, neutral): “Online recommender systems [49] [33] intend to provide people with recommendations of products they might appreciate, taking into account their past ratings profile, purchase history, or interest.”
Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System (2015, background): “Twitter is large web company to scale the recommendations of their millions of users it uses clusters of machines [26].”