SNRBERT: session-based news recommender using BERT

Abstract

In recent years, research on transformer-based recommender systems has attracted a lot of attention. Our work examines how BERT, a transformer-based contextual language model, can be applied to build a session-based news recommender system. The session-based approach aims to recommend news by creating profiles for users and items and recommending items accordingly to maximize session length. The proposed model, called SNRBERT (Session-Based News Recommender using BERT), is fine-tuned to estimate the relationship between each user and item in a given session based on the interactions between the user and the item during that session. We introduce this method to address the challenges of session-based news recommendation, particularly in maximizing session length and capturing user–item relationships effectively. Given the limited information available about user preferences in session-based scenarios, the model estimates a score based on the amount of time users spend on each item in each session. The news recommendation is then performed based on this score. On top of BERT, we employed an Bi-LSTM network in order to capture more accurate information regarding the order in which users interact with items during a given session. We compare our results with the state-of-the-art models by using commonly known metrics: MRR, HR, and NDCG on the Adressa dataset, one of the most comprehensive datasets publicly available. Our results show that our SNRBERT model achieves HR@10 of 0.688, MRR@10 of 0.315, and nDCG@10 of 0.338. These results demonstrate that SNRBERT outperforms state-of-the-art models in terms of MRR@10 and HR@10 metrics, showcasing its effectiveness in addressing the challenges of session-based news recommendation.