Deep Learning for News Recommender Systems: Designing neural architectures to tackle the challenges of news recommendation

Recommender Systems (RS) have been popular in assisting users with their choices, thus enhancing their engagement with online services. News RS are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated. News readers exhibit more unstable consumption behavior than users in other domains. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous.In this book, we provide a comprehensive introduction about Deep Learning architectures for RS and an effective neural meta-architecture is proposed: the CHAMELEON. Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based recommendation algorithms.