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Titlebook: Probabilistic Approaches to Recommendations; Nicola Barbieri,Giuseppe Manco,Ettore Ritacco Book 2014 Springer Nature Switzerland AG 2014

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樓主: 審美家
11#
發(fā)表于 2025-3-23 10:52:30 | 只看該作者
Probabilistic Models for Collaborative Filtering,ation problem. Probability theory can be applied in several facets: for modeling past events (i.e., users’ choices on a catalog of items) and making prediction about future ones; for decision theory; for model selection; etc. Clearly, many of these aspects are not specific to recommender systems, an
12#
發(fā)表于 2025-3-23 17:17:47 | 只看該作者
Bayesian Modeling,equally probable and, thus, the optimal parameter set is uniquely identified by the observed data. An alternative approach assumes that we can incorporate prior knowledge about the domain of .. Prior knowledge can be combined with observed data to determine the final optimal parameter set .. Rather
13#
發(fā)表于 2025-3-23 20:41:26 | 只看該作者
Social Recommender Systems,cing a virtual environment where one can exchange ideas, opinions, and information. The . realizes such ideas, by allowing different kinds of interactions among people with similar tastes. Social contents/relationships and microblogging features, such as following/follower relationships and sharing
14#
發(fā)表于 2025-3-23 22:26:11 | 只看該作者
Conclusions,or modeling preference data, and have revealed extreme flexibility to accommodate different situations. It is worth clarifying that the main thesis here is not the general superiority of probabilistic methods. It is well-known, e.g., from the Netflix prize, that the best approaches count an ensemble
15#
發(fā)表于 2025-3-24 06:10:20 | 只看該作者
16#
發(fā)表于 2025-3-24 08:35:55 | 只看該作者
2151-0067 rization, and topic models, for explicit and implicit preference data. These methods represent a significant advance in the research and technology of recommend978-3-031-00778-1978-3-031-01906-7Series ISSN 2151-0067 Series E-ISSN 2151-0075
17#
發(fā)表于 2025-3-24 14:34:49 | 只看該作者
Book 2014 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 recommend
18#
發(fā)表于 2025-3-24 16:13:31 | 只看該作者
19#
發(fā)表于 2025-3-24 21:18:51 | 只看該作者
Sowmya Puttaraju,Tony Brian D’souzags of children‘s literature.Consolidates the work of interna.This book investigates how cultural sameness and difference has been presented in a variety of forms and genres of children’s literature from Denmark, Germany, France, Russia, Britain, and the United States; ranging from English caricature
20#
發(fā)表于 2025-3-25 02:54:31 | 只看該作者
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