Holistic Entropy Reduction for Collaborative Filtering
نویسندگان
چکیده
منابع مشابه
Holistic Entropy Reduction for Collaborative Filtering
We propose a collaborative filtering (CF) method that uses behavioral data provided as propositions having the RDF-compliant form of (user X , likes, item Y ) triples. The method involves the application of a novel self-configuration technique for the generation of vector-space representations optimized from the information-theoretic perspective. The method, referred to as Holistic Probabilisti...
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Within the task of collaborative filtering two challenges for computing conditional probabilities exist. First, the amount of training data available is typically sparse with respect to the size of the domain. Thus, support for higher-order interactions is generally not present. Second, the variables that we are conditioning upon vary for each query. That is, users label different variables dur...
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Collaborative filtering (CF) involves predicting the preferences of a user for a set of items given partial knowledge of the user’s preferences for other items, while leveraging a database of profiles for other users. CF has applications e.g. in predicting Web sites a person will visit and in recommending products. Fundamentally, CF is a pattern recognition task, but a formidable one, often inv...
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Recommender systems make suggestions about products or services based on matching known or estimated preferences of users with properties of products or services (contentbased), properties of other users considered to be similar (collaborative filtering), or some hybrid approach. Collaborative filtering is widely used in E-commerce. To generate accurate recommendations in collaborative filterin...
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ژورنال
عنوان ژورنال: Foundations of Computing and Decision Sciences
سال: 2014
ISSN: 2300-3405
DOI: 10.2478/fcds-2014-0012