Factorization models with relational and contextual information

Abstract

The increasing availability of interconnected multi-modal information sources motivates the development of novel probabilistic models for recommender system that can leverage context present in relational data. Thus, we seek to integrate contextual information that can be relevant for determining the users’ information needs. In this thesis we focus on a set of techniques for modeling contextual information to factorization models, in particular models that uses implicit feedback such as event counts. Furthermore we propose analytical tools for those models, improving our capabilities with regards to find suitable hyparparameters. In order to model counts (for example, number of clicks in a page) as implicit user feedback, we chose to utilize the Poisson factorization as a building block. Then, we develop two Poisson factorization models that include social networks, item textual content and periodic time events as contextual information, incorporated into a joint matrix and tensor factorization model (in Chapters 3 and 4). Additionally, we develop a joint hierarchical recurrent neural networks and a temporal point process model for the problem of multi-session recommendations, where we observe sequences of items grouped into sequences of sessions, and create a model capable of providing itens recommendation and next-session time prediction (Chapter 5). Finally, we utilize and develop an approach based on the prior predictive distribution that allows us to set hyperparameters for Poisson factorization models without the need to fit the model to the data, obtaining both closed-form equations and an optimization algorithm for this task (Chapter 6). One relevant result here is a closed-form equation for the dimensionality of the latent space in Poisson factorization models. In general, we position this work as a contribution to probabilistic modeling in the context of recommender system utilizing multi-relational and count data as a signal for contextual information, with contributions ranging from model design, analysis and hyperparameter selection.