Collaborative Filtering with Maximum Entropy
نویسندگان
چکیده
منابع مشابه
Maximum Entropy for Collaborative Filtering
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...
متن کاملA Maximum Entropy Approach for Collaborative Filtering
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...
متن کاملA Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training
Item neighbourhood methods for collaborative filtering learn a weighted graph over the set of items, where each item is connected to those it is most similar to. The prediction of a user’s rating on an item is then given by that rating of neighbouring items, weighted by their similarity. This paper presents a new neighbourhood approach which we call item fields, whereby an undirected graphical ...
متن کاملA Maximum Entropy Method for Particle Filtering
Standard ensemble or particle filtering schemes do not properly represent states of low priori probability when the number of available samples is too small, as is often the case in practical applications. We introduce here a set of parametric resampling methods to solve this problem. Motivated by a general H-theorem for relative entropy, we construct parametric models for the filter distributi...
متن کاملHolistic 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...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Intelligent Systems
سال: 2004
ISSN: 1541-1672
DOI: 10.1109/mis.2004.59