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- {{Recommender systems}} ...zation Weights – A Simple Mechanism to Alleviate Cold Start in Recommender Systems|journal=ACM Transactions on Knowledge Discovery from Data |volume=13|pages= ...18 KB (2,656 words) - 03:07, 23 August 2024
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- ...tem-to-item''', is a form of [[collaborative filtering]] for [[recommender systems]] based on the similarity between items calculated using people's ratings o Earlier collaborative filtering systems based on [[Star (classification)|rating]] similarity between users (known a ...5 KB (715 words) - 22:01, 26 January 2025
- {{Recommender systems}} ...zation Weights – A Simple Mechanism to Alleviate Cold Start in Recommender Systems|journal=ACM Transactions on Knowledge Discovery from Data |volume=13|pages= ...18 KB (2,656 words) - 03:07, 23 August 2024
- * {{cite book|title=Social Network-Based Recommender Systems|first1=Daniel|last1=Schall|publisher=Springer|year=2015|page=12|isbn=978331 ...2 KB (270 words) - 04:11, 13 March 2024
- '''Location-based recommendation''' is a [[recommender system]] that incorporates location information, such as that from a mobile ...lar users gave and ratings that the user gave on previous occasions. These systems have become increasingly popular and are used for movies, music, news, book ...10 KB (1,567 words) - 02:42, 8 August 2023
- ...1=Gemmis |first1=Marco de |title=Learning Preference Models in Recommender Systems |date=2010 |work=Preference Learning |pages=387–407 |editor-last=Fürnkranz ...8 KB (1,185 words) - 05:29, 21 November 2024
- ...e=Additive Smoothing for Relevance-Based Language Modelling of Recommender Systems|journal=CERI '16 Proceedings of the 4th Spanish Conference on Information R ...12 KB (1,696 words) - 01:13, 15 January 2025
- ...analysis|cluster objects]], such as for [[collaborative filtering]] in a [[recommender system]], in which “similar” users and items are grouped based on the users In the case of recommender systems, a user’s preference for an item constitutes a relationship between the use ...16 KB (2,520 words) - 21:33, 5 July 2024
- ...is used in many different applications such as search engine queries and [[recommender system]]s.<ref>{{Cite web |date=2020-07-02 |title=What is Information Retri ...1997): The rationale for introducing probabilistic concepts is obvious: IR systems deal with natural language, and this is too far imprecise to enable a syste ...16 KB (2,433 words) - 02:00, 10 December 2024
- |title=Recommender Systems Handbook |chapter=Active Learning in Recommender Systems ...18 KB (2,520 words) - 17:49, 7 December 2024
- ...bstract-Conference.html |journal=Advances in Neural Information Processing Systems |language=en |volume=36 |pages=28748–28760}}</ref> {{See also|Recommender system}} ...42 KB (5,916 words) - 18:10, 22 February 2025
- ...ed Abstracts on Human Factors in Computing Systems |chapter=Why do tagging systems work? |date=2006|chapter-url=http://dl.acm.org/citation.cfm?doid=1125451.11 ...e in the number and diversity of tags. As opposed to structured annotation systems, tags provide users an unstructured, open-ended mechanism to annotate and o ...22 KB (3,177 words) - 08:18, 27 December 2023
- |chapter=Chapter 6: Information Processing in Dynamical Systems: Foundations of Harmony Theory ...2/NIPS2009_0817.pdf |date=2012-05-25 }}. ''[[Neural Information Processing Systems]]'' '''23'''.</ref> [[immunology]],<ref>{{Cite journal |last1=Bravi |first1 ...19 KB (2,742 words) - 11:22, 29 January 2025
- * [[Recommender system]]s, in which cases the data matrix has [[missing values]] and the ap ...el parameters. In the context of [[LTI system theory|linear time-invariant systems]], the elimination step is equivalent to [[Kalman filter|Kalman smoothing]] ...22 KB (3,404 words) - 09:11, 2 February 2025
- ...document/9416312|journal=IEEE Transactions on Neural Networks and Learning Systems|volume=PP|issue=2 |pages=494–514|doi=10.1109/TNNLS.2021.3070843|pmid=339009 ...38875|isbn=978-1-7281-2308-0|s2cid=209459928}}</ref> Training this kind of recommender system requires a huge amount of information from the users; however, knowl ...51 KB (7,424 words) - 03:33, 14 February 2025
- ...ction of [[ranking function|ranking models]] for [[information retrieval]] systems.<ref>[[Mehryar Mohri]], Afshin Rostamizadeh, Ameet Talwalkar (2012) ''Found ...ardt. |title=Early exit optimizations for additive machine learned ranking systems |journal=WSDM '10: Proceedings of the Third ACM International Conference on ...54 KB (7,235 words) - 22:22, 26 January 2025
- ...71|title=Polynomial Theory of Complex Systems|journal=IEEE Transactions on Systems, Man, and Cybernetics|issue=4|pages=364–378|doi=10.1109/TSMC.1971.4308320|v ...ve wide applications in [[Computer vision|image and video recognition]], [[recommender system]]s<ref>{{Cite book|url=http://papers.nips.cc/paper/5004-deep-content ...89 KB (12,410 words) - 12:07, 29 January 2025
- ...th_deep_convolutional.pdf|journal=NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada|access-date=2017-05-24|archive-date=2017-01-10|archive- ...ayers through which the data is transformed. More precisely, deep learning systems have a substantial ''credit assignment path'' (CAP) depth. The CAP is the c ...180 KB (23,460 words) - 20:17, 27 February 2025
- * [[recommender system]]s,<ref>{{cite book |url=https://proceedings.neurips.cc/paper/2013/f ...layered network of analog threshold elements |journal=IEEE Transactions on Systems Science and Cybernetics |volume=5 |issue=4 |pages=322–333 |doi=10.1109/TSSC ...138 KB (19,374 words) - 22:31, 20 February 2025