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Books > Computing & IT > Applications of computing > Databases > Data mining
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Probabilistic Approaches to Recommendations (Paperback)
Loot Price: R1,090
Discovery Miles 10 900
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Probabilistic Approaches to Recommendations (Paperback)
Series: Synthesis Lectures on Data Mining and Knowledge Discovery
Expected to ship within 10 - 15 working days
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The 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.
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