Exploring Hierarchical Structures for Recommender Systems

Exploring Implicit Hierarchical Structures for Recommender Systems

Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the explicit hierarchical structures of items or user preferences can improve the performance of recommender systems. However, explicit hierarchical structures are usually unavailable, especially those of user p...

Community Structures in Recommender Systems

In the age of information overload, recommender systems help users to find what they like, but in return they can affect users interests. Recommender systems can narrow users options to a limited community of items or informations. Our goal is to develop tools to investigate the effect of recommender systems on the social networks. Netflix Prize winner algorithm is an example of good recommende...

Title of Thesis: Hierarchical Probabilistic Relational Models for Recommender Systems Hierarchical Probabilistic Relational Models for Recommender Systems

In this thesis we describe an approach to the recommender system problem based on the Probabilistic Relational Model framework. Traditionally, recommender systems have fallen into two broad categories: content-based and collaborative-filteringbased recommender systems, each of which has a distinct set of strengths and weaknesses. We present a sound statistical framework for integrating both of ...

Position Paper: Exploring Explanations for Matrix Factorization Recommender Systems

In this paper we address the problem of finding explanations for collaborative filtering algorithms that usematrix factorizationmethods. We look for explanations that increase the transparency of the system. To do so, we propose two measures. First, we show a model that describes the contribution of each previous rating given by a user to the generated recommendation. Second, we measure the inf...

Hierarchical graph maps for visualization of collaborative recommender systems