Similarity-Based Supervised User Session Segmentation Method for Behavior Logs

Abstract

In information recommendation, a session refers to a sequence of user actions within a specific time frame. Session-based recommender systems aim to capture short-term preferences and generate relevant recommendations. However, user interests may shift even within a session, making appropriate segmentation essential for modeling dynamic behaviors. In this study, we propose a supervised session segmentation method based on similarity features derived from action embeddings and attributes. We compute the similarity scores between items within a fixed-size window around each candidate segmentation point, using item co-occurrence embeddings, text embeddings of titles and brands, and price information as sources for these similarity features. These features are used to train supervised classification models to predict the session boundaries. We construct a manually annotated dataset from real browsing histories and evaluate the segmentation performance using F1-score, PR-AUC, and ROC-AUC. The LightGBM model achieves the best performance, with an F1-score of 0.806 and a PR-AUC of 0.831. These results demonstrate the effectiveness of the proposed method for session segmentation and its potential to capture dynamic user behaviors.