Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
Generating adequate recommendations for newcomers is a hard problem for a recommender system (RS) due to lack of detailed user profiles and social preference data. Empirical evide...
Patricia Victor, Chris Cornelis, Ankur Teredesai, ...
—In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their content ratings with friends. The rating ...
The Lasso is a popular technique for joint estimation and continuous variable selection, especially well-suited for sparse and possibly under-determined linear regression problems....
We present a probabilistic model for generating personalised recommendations of items to users of a web service. The Matchbox system makes use of content information in the form o...