Papers › Neural Attentive Session-based Recommendation
Neural Attentive Session-based Recommendation
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma
Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.
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141d8c882b945eea · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Session-Based Recommendations | Diginetica | NARM | Hit@20 | 49.70 | #12 of 13 | Archive leaderboard | report |
| Session-Based Recommendations | Diginetica | NARM | MRR@20 | 16.17 | #12 of 13 | Archive leaderboard | report |
| Session-Based Recommendations | yoochoose1/64 | NARM | HR@20 | 68.32 | #10 of 11 | Archive leaderboard | report |
| Session-Based Recommendations | yoochoose1/64 | NARM | MRR@20 | 28.63 | #10 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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