A Bayesian generative model is presented for recommending interesting items and trustworthy users to the targeted users in social rating networks with asymmetric and directed trust relationships. The proposed model is the first unified approach to …
Variational autoencoders were proven successful in domains such as computer vision and speech processing. Their adoption for modeling user preferences is still unexplored, although recently it is starting to gain attention in the current literature. …
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 …
A Bayesian generative model is presented for recommending interesting items and trustworthy users to the targeted users in social rating networks with asymmetric and directed trust relationships. The proposed model is the first unified approach to …
Probabilistic topic models are widely used in different contexts to uncover the hidden structure in large text corpora. One of the main (and perhaps strong) assumption of these models is that generative process follows a bag-of-words assumption, i.e. …
Recent works in Recommender Systems (RS) have investigated the relationships between the prediction accuracy, i.e. the ability of a RS to minimize a cost function (for instance the RMSE measure) in estimating users' preferences, and the accuracy of …
Probabilistic topic models are widely used in different contexts to uncover the hidden structure in large text corpora. One of the main features of these models is that generative process follows a bag-of-words assump- tion, i.e each token is …
This paper presents a hierarchical probabilistic approach to collaborative filtering which allows the discovery and analysis of both global patterns (i.e., tendency of some products of being ‘universally appreciated’) and local patterns (tendency of …
In this work we perform an analysis of probabilistic approaches to recommendation upon a different validation perspective, which focuses on accuracy metrics such as recall and precision of the recommendation list. Traditionally, state-of-art …
We propose a bayesian probabilistic model for explicit preference data. The model introduces a generative process, which takes into account both item selection and rating emission to gather into communities those users who experience the same items …