000			04680nam a22005295i 4500
001			978-3-031-01906-7
003			DE-He213
005			20240730165154.0
007			cr nn 008mamaa
008			220601s2014 sz | s |||| 0|eng d
020			_a9783031019067 _9978-3-031-01906-7
024	7		_a10.1007/978-3-031-01906-7 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
072		7	_aUNF _2thema
072		7	_aUYQE _2thema
082	0	4	_a006.312 _223
100	1		_aBarbieri, Nicola. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987732
245	1	0	_aProbabilistic Approaches to Recommendations _h[electronic resource] / _cby Nicola Barbieri, Giuseppe Manco, Ettore Ritacco.
250			_a1st ed. 2014.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2014.
300			_aXV, 181 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSynthesis Lectures on Data Mining and Knowledge Discovery, _x2151-0075
505	0		_aPreface -- The Recommendation Process -- Probabilistic Models for Collaborative Filtering -- Bayesian Modeling -- Exploiting Probabilistic Models -- Contextual Information -- Social Recommender Systems -- Conclusions -- Bibliography -- Authors' Biographies .
520			_aThe importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. This book starts with a brief summary of the recommendation problem and its challenges and a review of some widely used techniques Next, we introduce and discuss probabilistic approaches for modeling preference data. We focus our attention on methods based on latent factors, such as mixture models, probabilistic matrix factorization, and topic models, for explicit and implicit preference data. These methods represent a significant advance in the research and technology of recommendation. The resulting models allow us to identify complex patterns in preference data, which can be exploited to predict future purchases effectively. The extreme sparsity of preference data poses serious challenges to the modeling of user preferences, especially in the cases where few observations are available. Bayesian inference techniques elegantly address the need for regularization, and their integration with latent factor modeling helps to boost the performances of the basic techniques. We summarize the strengths and weakness of several approaches by considering two different but related evaluation perspectives, namely, rating prediction and recommendation accuracy. Furthermore, we describe how probabilistic methods based on latent factors enable the exploitation of preference patterns in novel applications beyond rating prediction or recommendation accuracy. We finally discuss the application of probabilistic techniques in two additional scenarios, characterized by the availability of side information besides preference data. In summary, the book categorizes the myriad probabilistic approaches to recommendations and provides guidelines for their adoption in real-world situations.
650		0	_aData mining. _93907
650		0	_aStatistics . _931616
650	1	4	_aData Mining and Knowledge Discovery. _987734
650	2	4	_aStatistics. _914134
700	1		_aManco, Giuseppe. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987736
700	1		_aRitacco, Ettore. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987738
710	2		_aSpringerLink (Online service) _987741
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031007781
776	0	8	_iPrinted edition: _z9783031030345
830		0	_aSynthesis Lectures on Data Mining and Knowledge Discovery, _x2151-0075 _987743
856	4	0	_uhttps://doi.org/10.1007/978-3-031-01906-7
912			_aZDB-2-SXSC
942			_cEBK
999			_c86141 _d86141