# Implicit Session Contexts for Next-Item Recommendations

arXiv: https://arxiv.org/abs/2208.09076 · HTML: https://mlsanity.com/paper/2208.09076/

## 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 (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://mlsanity.com/paper/1904.08030/) (2019)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [Global Context Enhanced Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2106.05081/) (2021)
- [RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation](https://mlsanity.com/paper/1812.02646/) (2018)
- [Exploring Data Splitting Strategies for the Evaluation of Recommendation Models](https://mlsanity.com/paper/2007.13237/) (2020)
- [Self-Supervised Graph Co-Training for Session-based Recommendation](https://mlsanity.com/paper/2108.10560/) (2021)
- [A Survey on Trajectory Data Management, Analytics, and Learning](https://mlsanity.com/paper/2003.11547/) (2020)
- [Sparse-Interest Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09267/) (2021)
- [TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2005.02844/) (2020)
- [Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems](https://mlsanity.com/paper/1904.04381/) (2019)
- [Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation](https://mlsanity.com/paper/1906.04473/) (2019)
- [Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks](https://mlsanity.com/paper/1904.10367/) (2019)

## Built on by (most cited first, 5 of 5)

- [Large Language Models for Intent-Driven Session Recommendations](https://mlsanity.com/paper/2312.07552/) (2023)
- [Code Recommendation for Open Source Software Developers](https://mlsanity.com/paper/2210.08332/) (2022)
- [FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning](https://mlsanity.com/paper/2402.03481/) (2024)
- [Hierarchical Multi-Task Learning Framework for Session-based Recommendations](https://mlsanity.com/paper/2309.06533/) (2023)
- [IntentRec: Predicting User Session Intent with Hierarchical Multi-Task Learning](https://mlsanity.com/paper/2408.05353/) (2024)

## 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.