# Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation

arXiv: https://arxiv.org/abs/2205.04181 · HTML: https://mlsanity.com/paper/2205.04181/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 52 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [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)
- [A Survey on Session-based Recommender Systems](https://mlsanity.com/paper/1902.04864/) (2019)
- [Improved Recurrent Neural Networks for Session-based Recommendations](https://mlsanity.com/paper/1606.08117/) (2016)
- [Global Context Enhanced Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2106.05081/) (2021)
- [Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation](https://mlsanity.com/paper/2012.06852/) (2020)
- [RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation](https://mlsanity.com/paper/1812.02646/) (2018)
- [Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1911.11942/) (2019)
- [Self-Supervised Graph Co-Training for Session-based Recommendation](https://mlsanity.com/paper/2108.10560/) (2021)
- [Sparse-Interest Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09267/) (2021)
- [Session-based Recommendation with Hypergraph Attention Networks](https://mlsanity.com/paper/2112.14266/) (2021)
- [Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation](https://mlsanity.com/paper/2110.03996/) (2021)
- [Empirical Analysis of Session-Based Recommendation Algorithms](https://mlsanity.com/paper/1910.12781/) (2019)
- [Price-aware Recommendation with Graph Convolutional Networks](https://mlsanity.com/paper/2003.03975/) (2020)
- [Session-aware Linear Item-Item Models for Session-based Recommendation](https://mlsanity.com/paper/2103.16104/) (2021)

## Built on by (most cited first, 11 of 11)

- [Homophily-oriented Heterogeneous Graph Rewiring](https://mlsanity.com/paper/2302.06299/) (2023)
- [FineRec:Exploring Fine-grained Sequential Recommendation](https://mlsanity.com/paper/2404.12975/) (2024)
- [Disentangling ID and Modality Effects for Session-based Recommendation](https://mlsanity.com/paper/2404.12969/) (2024)
- [ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint](https://mlsanity.com/paper/2307.09193/) (2023)
- [Analysis of Anonymous User Interaction Relationships and Prediction of Advertising Feedback Based on Graph Neural Network](https://mlsanity.com/paper/2506.13787/) (2025)
- [Modeling Reference-dependent Choices with Graph Neural Networks](https://mlsanity.com/paper/2408.11302/) (2024)
- [Pseudo Session-Based Recommendation with Hierarchical Embedding and Session Attributes](https://mlsanity.com/paper/2306.10029/) (2023)
- [KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions](https://mlsanity.com/paper/2308.09329/) (2023)
- [Context-aware Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/2310.09593/) (2023)
- [A GNN Model with Adaptive Weights for Session-Based Recommendation Systems](https://mlsanity.com/paper/2408.05051/) (2024)
- [Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation](https://mlsanity.com/paper/2502.08685/) (2025)

## Abstract (arXiv)

Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recommendation. Firstly, it is hard to handle heterogeneous information from various features of items to capture users' price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices. To address the above challenges, we propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation. Towards the first challenge, we devise a heterogeneous hypergraph to represent heterogeneous information and rich relations among them. A dual-channel aggregating mechanism is then designed to aggregate various information in the heterogeneous hypergraph. After that, we extract users' price preferences and interest preferences via attention layers. As to the second challenge, a co-guided learning scheme is designed to model the relations between price and interest preferences and enhance the learning of each other. Finally, we predict user actions based on item features and users' price and interest preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoHHN. Further analysis reveals the significance of price for session-based recommendation.