抄録
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 four types of features: item co-occurrence embeddings, text embeddings of
titles and brands, and price. These features are used to train supervised
classifiers (LightGBM, XGBoost, CatBoost, support vector machine, and logistic
regression) to predict the session boundaries. We construct a manually
annotated dataset from real user browsing histories and evaluate the
segmentation performance using F1-score, area under the precision-recall curve
(PR-AUC), and area under the receiver operating characteristic curve. 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.