A Taxonomy of Recommender Agents on the Internet

By Miquel Montaner, Beatriz López, Josep Lluís de la Rosa i Esteva. Artificial Intelligence Review, 2003

799 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a50962d14a445bd49feed1eedcbbbb3eeaad582e

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Citations in the corpus

E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact (2007, background): “Similarly, the majority of the review articles regarding RAs (Herlocker et al. 2004; Montaner et al. 2003; Sarwar et al. 2000; Schafer et al. 2001; Zhang 2002) provide either evaluations of different recommendation-generating algorithms (focusing primarily on criteria such as accuracy and coverage)…”
Learning preferences of new users in recommender systems: an information theoretic approach (2008, neutral): “.. ... ..... .. .... .... ... ..... ......... ........ ............ ............... .... ... ....... .”
Music recommendation and discovery in the long tail (2008, background): “2), and clustering based on stereotypes (Montaner et al., 2003).”
Content-based Recommender Systems: State of the Art and Trends (2011, neutral): “A thorough review is presented in [64, 69, 82].”
A model of a trust-based recommendation system on a social network (2006, background): “Recent research in computer science has dealt with recommendation systems [26,30].”
Introduction to Recommender Systems Handbook (2011, background): “The most complex items that have been considered are insurance policies, financial investments, travels, jobs [72].”
A unified approach to building hybrid recommender systems (2009, background): “Recommender systems suggest items of interest to users based on available information such as previous usage patterns, the usage patterns of other users, and features of the items themselves [20].”
Learning and adaptivity in interactive recommender systems (2007, background): “They have been exploited for recommending books, CDs, movies, travels, financial services, and in many other applications [1, 5, 15, 9].”
The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system (2010, background): “Recent research [21, 22] has shown the wide range of recommender systems’ application domains, such as websites, movies, TV shows, documents, news, music, and restaurants.”
Collaborative filtering recommender systems (2013, background): “Examples of low are news, Web pages, books, CDs, movies and those of larger complexity and values are digital cameras, mobile phones, PCs, etc. Insurance policies, financial investments, travels and jobs are considered the most complex items (Montaner et al., 2003).”
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, background): “With the advent of the Internet, choosing information from numerous alternatives has been a great challenge due to information overload (Montaner et al. 2003).”
Recommender Systems: Introduction and Challenges (2015, background): “The most complex items that have been considered are insurance policies, financial investments, travel, and jobs [39].”