Content-based recommender systems (CBRSs) rely on item and user descriptions (content) to build item representations and user profiles that can be effectively exploited to suggest …
This paper investigates the use of automatically extracted visual features of videos in the context of recommender systems and brings some novel contributions in the domain of …
Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on …
In this article we propose a framework that generates natural language explanations supporting the suggestions generated by a recommendation algorithm. The cornerstone of …
This book is an introduction to social data analytics along with its challenges and opportunities in the age of Big Data and Artificial Intelligence. It focuses primarily on …
In this paper we investigate the effectiveness of Recurrent Neural Networks (RNNs) in a top- N content-based recommendation scenario. Specifically, we propose a deep architecture …
In this paper, we present a knowledge-aware recommendation framework based on neuro- symbolic graph embeddings that encode first-order logical (FOL) rules. In particular, our …
In this paper we present a deep content-based recommender system (DeepCBRS) that exploits Bidirectional Recurrent Neural Networks (BRNNs) to learn an effective …
The growth of the Web is the most influential factor that contributes to the increasing importance of text retrieval and filtering systems. On one hand, the Web is becoming more …