Graph Fusion in Reciprocal Recommender Systems

Learning with Heterogeneous Side Information Fusion for Recommender Systems

Recommender System (RS) is a hot area where artificial intelligence (AI) techniques can be effectively applied to improve performance. Since the wellknown Netflix Challenge, collaborative filtering (CF) has become the most popular and effective recommendation method. Despite their success in CF, various AI techniques still have to face the data sparsity and cold start problems. Previous works t...

A graph model for E-commerce recommender systems

Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers’ preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that pr...

RDF Graph Embeddings for Content-based Recommender Systems

Linked Open Data has been recognized as a useful source of background knowledge for building content-based recommender systems. Vast amount of RDF data, covering multiple domains, has been published in freely accessible datasets. In this paper, we present an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs...

Hierarchical graph maps for visualization of collaborative recommender systems

Jumping Connections: A Graph-Theoretic Model for Recommender Systems

Recommender systems have become paramount to customize information access and reduce information overload. They serve multiple uses, ranging from suggesting products and artifacts (to consumers), to bringing people together by the connections induced by (similar) reactions to products and services. This thesis presents a graph-theoretic model that casts recommendation as a process of ‘jumping c...