Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation

TL;DR

This paper introduces a new category-aware session-based recommendation task and proposes IAGNN, a graph neural network that leverages item and category relationships to improve recommendation accuracy in user-specified category scenarios.

Contribution

The paper defines the novel CSBR task and develops IAGNN, a graph neural network that effectively incorporates category information for improved recommendations.

Findings

IAGNN outperforms state-of-the-art baselines on three real-world datasets.

The method effectively utilizes category and item relationships.

Experimental results demonstrate significant accuracy improvements.

Abstract

Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and short video recommendation. There is a common scenario that user specifies a target category of items as a global filter, however previous SBR settings mainly consider the item sequence and overlook the rich target category information. Therefore, we define a new task called Category-aware Session-Based Recommendation (CSBR), focusing on the above scenario, in which the user-specified category can be efficiently utilized by the recommendation system. To address the challenges of the proposed task, we develop a novel method called Intention Adaptive Graph Neural Network (IAGNN), which takes advantage of relationship between items and their categories to achieve an accurate recommendation result. Specifically, we construct a category-aware graph with both item and category nodes to represent the complex transition information in the session. An intention-adaptive graph neural network on the category-aware graph is utilized to capture user intention by transferring the historical interaction information to the user-specified category domain. Extensive experiments on three real-world datasets are conducted to show our IAGNN outperforms the state-of-the-art baselines in the new task.