mlsanityData to 14 Oct 2026

Implicit Session Contexts for Next-Item Recommendations

arXiv 2208.09076 ↗First posted 18 Aug 2022cs.IR cs.LG cs.SIcited by 5

Not on the map. Why?

Abstract (arXiv)

Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are not explicitly given in most datasets, and implicitly inferring session context as an aggregation of item-level attributes is crude. In this paper, we propose ISCON, which implicitly contextualizes sessions. ISCON first generates implicit contexts for sessions by creating a session-item graph, learning graph embeddings, and clustering to assign sessions to contexts. ISCON then trains a session context predictor and uses the predicted contexts' embeddings to enhance the next-item prediction accuracy. Experiments on four datasets show that ISCON has superior next-item prediction accuracy than state-of-the-art models. A case study of ISCON on the Reddit dataset confirms that assigned session contexts are unique and meaningful.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 21 of its 45 references that are in the map. First posted on arXiv 18 Aug 2022.

Builds on

21 of 45 references.

References · 21

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9 more
  1. 2018RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendationcited by 51
  2. 2020Exploring Data Splitting Strategies for the Evaluation of Recommendation Modelscited by 48
  3. 2021Self-Supervised Graph Co-Training for Session-based Recommendationcited by 43
  4. 2020A Survey on Trajectory Data Management, Analytics, and Learningcited by 41
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  7. 2019Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systemscited by 28
  8. 2019Future Data Helps Training: Modeling Future Contexts for Session-based Recommendationcited by 16
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 2.

5 papers.

  1. 2023Large Language Models for Intent-Driven Session Recommendationscited by 30
  2. 2022Code Recommendation for Open Source Software Developerscited by 13
  3. 2024FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuningcited by 2
  4. 2023Hierarchical Multi-Task Learning Framework for Session-based Recommendationscited by 1
  5. 2024IntentRec: Predicting User Session Intent with Hierarchical Multi-Task Learningcited by 1