用相似度特征自动划分用户行为会话，提升推荐系统对动态兴趣的捕捉能力。
Similarity-Based Supervised User Session Segmentation Method for Behavior Logs
- 基于物品嵌入与属性的相似度计算，构建会话分割特征。
- 在真实数据上达到F1=0.806，PR-AUC=0.831，优于传统方法。
- 适合做个性化推荐中的行为建模，尤其关注短期兴趣变化。
在信息推荐中，会话指特定时间段内用户的一系列操作。会话推荐系统旨在捕捉短期偏好并生成相关推荐。然而，用户兴趣可能在会话内部发生变化，因此恰当的会话分割对建模动态行为至关重要。本文提出一种基于相似度特征的监督式会话分割方法，利用动作嵌入和属性信息计算候选分割点附近物品间的相似度。相似度特征来源于物品共现嵌入、标题与品牌文本嵌入以及价格信息。这些特征用于训练分类模型以预测会话边界。我们从真实浏览历史构建了人工标注数据集，并使用F1-score、PR-AUC和ROC-AUC评估分割性能。LightGBM模型表现最佳，F1-score为0.806，PR-AUC为0.831。结果表明该方法有效，具备捕捉动态用户行为的潜力。
原文摘要 · Abstract (English)
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.
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