News Session-Based Recommendations using Deep Neural Networks

Abstract

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks. In this paper, it is proposed an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News Recommender Systems. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks. The recommendation task addressed in this work is next-item prediction for users sessions: "what is the next most likely article a user might read in a session?" Users sessions context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach. A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality. Experiments with an extensive number of session-based recommendation methods were performed and the proposed instantiation of CHAMELEON meta-architecture obtained a significant relative improvement in top-n accuracy and ranking metrics (10% on Hit Rate and 13% on MRR) over the best benchmark methods.

arXiv commentAccepted for the Third Workshop on Deep Learning for Recommender Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada. https://recsys.acm.org/recsys18/dlrs/

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Cited by in corpus (9)

• Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
• Empirical Analysis of Session-Based Recommendation Algorithms
• Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
• EB-NeRD: A Large-Scale Dataset for News Recommendation
• How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
• NewsEmbed: Modeling News through Pre-trained Document Representations
• On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems
• Improving Next-Application Prediction with Deep Personalized-Attention Neural Network
• Hybrid Session-based News Recommendation using Recurrent Neural Networks