Buch, Englisch, 197 Seiten, Paperback, Format (B × H): 187 mm x 235 mm
Reihe: Synthesis Lectures on Data Mining and Knowledge Discovery
Buch, Englisch, 197 Seiten, Paperback, Format (B × H): 187 mm x 235 mm
Reihe: Synthesis Lectures on Data Mining and Knowledge Discovery
ISBN: 978-1-62705-257-3
Verlag: Morgan & Claypool Publishers
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.
Autoren/Hrsg.
Weitere Infos & Material
- Preface
- The Recommendation Process
- Probabilistic Models for Collaborative Filtering
- Bayesian Modeling
- Exploiting Probabilistic Models
- Contextual Information
- Social Recommender Systems
- Conclusions
- Bibliography
- Authors' Biographies