A Multi-Level Location-Aware Approach for Session-Based News Recommendation

Recently, personalized news recommendation systems have been widely used, which can achieve personalized news recommendations based on people’s different preferences, optimize the reading experience, and alleviate the problem of information overload. Among them, session-based news recommendation has gradually become a research hotspot as it can recommend news without requiring users to log in or when their reading history is difficult to obtain. The key to session-based news recommendation is to use short-term interaction data to learn user preferences. Existing models often focus on mining news content information in sessions and do not fully utilize geolocation information related to news and sessions, and there is also a certain inconsistency between their training objective and model evaluation metric, leading to suboptimal model recommendation performance. In order to fully utilize geolocation information, this paper proposes a multi-level location-aware approach for session-based news recommendation (MLA4SNR). Firstly, a news-location heterogeneous graph is constructed, and a graph element-wise attention network is proposed to mine high-order relationships between news and location. Secondly, a session feature extraction network based on Transformer is proposed to extract session features. Then, a session-location heterogeneous graph is constructed, and a graph element-wise attention network is used to mine high-order relationships between sessions and locations. Finally, a loss function based on the NDCG is used to train the model. Experimental results on a real news dataset show that MLA4SNR outperforms the baselines significantly.