Recommender system

Recommender systems
Concepts
Methods and challenges
Implementations
Research
"The algorithm" redirects here. For other uses, see Algorithm (disambiguation).

A recommender system (RecSys), or a recommendation system (sometimes replacing system with terms such as platform, engine, or algorithm), is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user.[1][2][3] Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.[1][4]

Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read.[1] Recommender systems are used in a variety of areas, with commonly recognised examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders.[5][6] These systems can operate using a single type of input, like music, or multiple inputs within and across platforms like news, books and search queries. There are also popular recommender systems for specific topics like restaurants and online dating. Recommender systems have also been developed to explore research articles and experts,[7] collaborators,[8] and financial services.[9]

A content discovery platform is an implemented software recommendation platform which uses recommender system tools. It utilizes user metadata in order to discover and recommend appropriate content, whilst reducing ongoing maintenance and development costs. A content discovery platform delivers personalized content to websites, mobile devices and set-top boxes. A large range of content discovery platforms currently exist for various forms of content ranging from news articles and academic journal articles[10] to television.[11] As operators compete to be the gateway to home entertainment, personalized television is a key service differentiator. Academic content discovery has recently become another area of interest, with several companies being established to help academic researchers keep up to date with relevant academic content and serendipitously discover new content.[10]

  1. ^ a b c Cite error: The named reference handbook was invoked but never defined (see the help page).
  2. ^ Lev Grossman (May 27, 2010). "How Computers Know What We Want — Before We Do". TIME. Archived from the original on May 30, 2010. Retrieved June 1, 2015.
  3. ^ Roy, Deepjyoti; Dutta, Mala (2022). "A systematic review and research perspective on recommender systems". Journal of Big Data. 9 (59). doi:10.1186/s40537-022-00592-5.
  4. ^ Cite error: The named reference ResnickVarian was invoked but never defined (see the help page).
  5. ^ Gupta, Pankaj; Goel, Ashish; Lin, Jimmy; Sharma, Aneesh; Wang, Dong; Zadeh, Reza (2013). "WTF: the who to follow service at Twitter". Proceedings of the 22nd International Conference on World Wide Web. Association for Computing Machinery. pp. 505–514. doi:10.1145/2488388.2488433. ISBN 9781450320351.
  6. ^ Baran, Remigiusz; Dziech, Andrzej; Zeja, Andrzej (June 1, 2018). "A capable multimedia content discovery platform based on visual content analysis and intelligent data enrichment". Multimedia Tools and Applications. 77 (11): 14077–14091. doi:10.1007/s11042-017-5014-1. ISSN 1573-7721. S2CID 36511631.
  7. ^ H. Chen, A. G. Ororbia II, C. L. Giles ExpertSeer: a Keyphrase Based Expert Recommender for Digital Libraries, in arXiv preprint 2015
  8. ^ Chen, Hung-Hsuan; Gou, Liang; Zhang, Xiaolong; Giles, Clyde Lee (2011). "CollabSeer: a search engine for collaboration discovery" (PDF). Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries. Association for Computing Machinery. pp. 231–240. doi:10.1145/1998076.1998121. ISBN 9781450307444.
  9. ^ Felfernig, Alexander; Isak, Klaus; Szabo, Kalman; Zachar, Peter (2007). "The VITA Financial Services Sales Support Environment" (PDF). In William Cheetham (ed.). Proceedings of the 19th National Conference on Innovative Applications of Artificial Intelligence, vol. 2. pp. 1692–1699. ISBN 9781577353232. ACM Copy.
  10. ^ a b jobs (September 3, 2014). "How to tame the flood of literature : Nature News & Comment". Nature. 513 (7516). Nature.com: 129–130. doi:10.1038/513129a. PMID 25186906. S2CID 4460749.
  11. ^ Analysis (December 14, 2011). "Netflix Revamps iPad App to Improve Content Discovery". WIRED. Retrieved December 31, 2015.