Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks (1911.11942v2)

Abstract: Predicting a user's preference in a short anonymous interaction session instead of long-term history is a challenging problem in the real-life session-based recommendation, e.g., e-commerce and media stream. Recent research of the session-based recommender system mainly focuses on sequential patterns by utilizing the attention mechanism, which is straightforward for the session's natural sequence sorted by time. However, the user's preference is much more complicated than a solely consecutive time pattern in the transition of item choices. In this paper, therefore, we study the item transition pattern by constructing a session graph and propose a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system. We formulate the next item recommendation within the session as a graph classification problem. Specifically, we propose a weighted attention graph layer and a Readout function to learn embeddings of items and sessions for the next item recommendation. Extensive experiments have been conducted on two benchmark E-commerce datasets, Yoochoose and Diginetica, and the experimental results show that our model outperforms other state-of-the-art methods.

• The paper proposes a novel FGNN model that transforms session interactions into directed, weighted graphs to capture complex item transition patterns.
• It introduces innovative components like Weighted Graph Attention layers and a unique Readout function to derive flexible session-level representations.
• Experimental evaluations on Yoochoose and Diginetica datasets demonstrate significant improvements in recall and MRR over traditional sequence-based methods.

Analyzing Session-Based Recommendation Using Graph Neural Networks

The paper "Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks" presents a novel approach to the session-based recommendation problem, particularly within e-commerce platforms. Rather than leveraging long user histories, this research focuses on crafting recommendations based on short, anonymous interaction sessions, thereby highlighting a critical area in recommender systems that deals with limited user data.

Overview

The paper critiques existing session-based recommendation models that predominantly rely on modeling sequential patterns using attention mechanisms. The key argument here is that a user's preference cannot be simply deduced from consecutive time-based sequences due to inherent complexities in item transitions. To address these complexities, the authors propose constructing a session graph that considers both sequence order and latent order. The problem is then framed as a graph classification task, employing a Full Graph Neural Network (FGNN) model which utilizes weighted attention graph layers and Readout functions to effectively embed items and session action patterns.

Methodology and Innovation

The FGNN architecture introduced in this paper encompasses several innovative components.

1. Session Graph Construction: The approach transforms a sequence of interactions into a directed and weighted session graph, which considers the frequency of item transitions within a session. This transformation captures implicit relationships and transition probabilities among items, overcoming limitations of sequence-only models.
2. Weighted Graph Attention (WGAT) Layers: This layer aggregates information through a self-attention mechanism that is sensitive to both the edge direction and weight, thus preserving the inherent graph-based structure of item transitions.
3. Novel Readout Function: Going beyond traditional attention mechanisms, the model applies a Readout function capable of learning the optimal order of item transitions, thus generating a session-level representation that is not strictly tied to time-sequence data, allowing for more flexible pattern recognition.
4. Extensive Comparative Evaluation: The method was rigorously tested against state-of-the-art benchmarks using the Yoochoose and Diginetica datasets. FGNN showed significant improvements in recall (R@20) and mean reciprocal rank (MRR@20), suggesting superior accuracy in understanding and predicting user preferences.

Results and Implications

The experiments revealed that FGNN consistently outperforms prior models across multiple datasets, with observable improvements in both precision and ranking capabilities. The model's robust performance, particularly on sessions of varying lengths, demonstrates the advantage of employing graph-based structures in recommendation settings without extensive user data histories.

The implications of this research are notable in the context of dynamic and fast-changing environments like online shopping and media streaming. By accurately inferring preferences based on short interaction sessions, this model allows recommender systems to be more adaptive and responsive to immediate user needs.

Future Directions

The paper indicates potential for further exploration into incorporating inter-session information, which could enhance understanding of user preferences beyond isolated session data. Future research could expand on hybrid models that integrate session-based recommendations with more comprehensive user behavior analytics, possibly exploring how emerging techniques such as reinforcement learning could improve adaption to shifting user preferences over time.

Overall, this paper offers a significant contribution to the domain of session-based recommendations, foregrounding the utility of graph neural networks in capturing complex item transition patterns. Its methodology provides a novel lens for viewing recommendations through the structuring of implicit user interactions, paving the way for more intuitive and effective recommendation systems.