# Session-aware Linear Item-Item Models for Session-based Recommendation

arXiv: https://arxiv.org/abs/2103.16104 · HTML: https://mlsanity.com/paper/2103.16104/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 17 of 17)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (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)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [Embarrassingly Shallow Autoencoders for Sparse Data](https://mlsanity.com/paper/1905.03375/) (2019)
- [Sequence-Aware Recommender Systems](https://mlsanity.com/paper/1802.08452/) (2018)
- [RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation](https://mlsanity.com/paper/1812.02646/) (2018)
- [Evaluation of Session-based Recommendation Algorithms](https://mlsanity.com/paper/1803.09587/) (2018)
- [Empirical Analysis of Session-Based Recommendation Algorithms](https://mlsanity.com/paper/1910.12781/) (2019)
- [NISER: Normalized Item and Session Representations to Handle Popularity Bias](https://mlsanity.com/paper/1909.04276/) (2019)
- [Markov Random Fields for Collaborative Filtering](https://mlsanity.com/paper/1910.09645/) (2019)
- [Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations](https://mlsanity.com/paper/1905.01997/) (2019)
- [Rethinking Item Importance in Session-based Recommendation](https://mlsanity.com/paper/2005.04456/) (2020)
- [Collaborative Filtering via High-Dimensional Regression](https://mlsanity.com/paper/1904.13033/) (2019)

## Built on by (most cited first, 4 of 4)

- [Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation](https://mlsanity.com/paper/2205.04181/) (2022)
- [Temporal Linear Item-Item Model for Sequential Recommendation](https://mlsanity.com/paper/2412.07382/) (2024)
- [LLM-Enhanced Linear Autoencoders for Recommendation](https://mlsanity.com/paper/2508.13500/) (2025)
- [Representation Learning of Tangled Key-Value Sequence Data for Early Classification](https://mlsanity.com/paper/2404.07454/) (2024)

## Abstract (arXiv)

Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e.g., on e-commerce or multimedia streaming services. Specifically, session data exhibits some unique characteristics, i.e., session consistency and sequential dependency over items within the session, repeated item consumption, and session timeliness. In this paper, we propose simple-yet-effective linear models for considering the holistic aspects of the sessions. The comprehensive nature of our models helps improve the quality of session-based recommendation. More importantly, it provides a generalized framework for reflecting different perspectives of session data. Furthermore, since our models can be solved by closed-form solutions, they are highly scalable. Experimental results demonstrate that the proposed linear models show competitive or state-of-the-art performance in various metrics on several real-world datasets.