Presentation of the Phd. thesis defense of Gabriel de Souza Pereira Moreira at Instituto Tecnológico de Aeronáutica (ITA), on Dec. 09, 2019, in São José dos Campos, Brazil.
Recommender systems have been increasingly popular in assisting users with their choices, thus enhancing their engagement and overall satisfaction with online services. Since the last decade, recommender systems became a topic of increasing interest among machine learning, human-computer interaction, and information retrieval researchers.
News recommender systems 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release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated and irrelevant to most readers. News readers exhibit more unstable consumption behavior than users in other domains such as entertainment. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous, with no past behavior tracked.
Since 2016, Deep Learning methods and techniques have been explored in Recommender Systems research. In general, they can be divided into methods for: Deep Collaborative Filtering, Learning Item Embeddings, Session-based Recommendations using Recurrent Neural Networks (RNN), and Feature Extraction from Items' Unstructured Data such as text, images, audio, and video.
The main contribution of this research was named CHAMELEON a meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks.
As information about users' past interactions is scarce in the news domain, information such as the user context (e.g., time, location, device, the sequence of clicks within the session), static and dynamic article features like the article textual content and its popularity and recency, are explicitly modeled in a hybrid session-based recommendation approach using RNNs.
The recommendation task addressed in this work is the next-item prediction for user sessions, i.e., "what is the next most likely article a user might read in a session?". A temporal offline evaluation is used for a realistic offline evaluation of such task, considering factors that affect global readership interests like popularity, recency, and seasonality.
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 algorithms.