Learning Intents behind Interactions with Knowledge Graph for Recommendation (2102.07057v1)

Abstract: Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained level of intents, and (2) exploit relation dependencies to preserve the semantics of long-range connectivity. In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN). Technically, we model each intent as an attentive combination of KG relations, encouraging the independence of different intents for better model capability and interpretability. Furthermore, we devise a new information aggregation scheme for GNN, which recursively integrates the relation sequences of long-range connectivity (i.e., relational paths). This scheme allows us to distill useful information about user intents and encode them into the representations of users and items. Experimental results on three benchmark datasets show that, KGIN achieves significant improvements over the state-of-the-art methods like KGAT, KGNN-LS, and CKAN. Further analyses show that KGIN offers interpretable explanations for predictions by identifying influential intents and relational paths. The implementations are available at https://github.com/huangtinglin/Knowledge_Graph_based_Intent_Network.

Overview of "Learning Intents behind Interactions with Knowledge Graph for Recommendation"

The paper "Learning Intents behind Interactions with Knowledge Graph for Recommendation" presents a novel approach to enhance the efficacy of recommender systems utilizing knowledge graphs (KGs). The proposed solution, Knowledge Graph-based Intent Network (KGIN), confronts the challenges of coarse-grained relational modeling in existing graph neural network (GNN) based models by distinguishing user-item relationships through the lens of underlying intents and leveraging relational dependencies within knowledge graphs.

Technical Contributions

1. Intent-based Modeling: The paper introduces a mechanism to model user intents explicitly, enriching user-item interactions with these intents. Unlike traditional models that treat user-item relations uniformly, KGIN decomposes relationships into multiple intents, each associated with a distribution over KG relations. This approach allows for distilling significant differences among intents, enhancing both model capacity and interpretability.
2. Relational Path-aware Aggregation: KGIN advances the GNN-based recommender systems by incorporating a novel information aggregation scheme. This scheme emphasizes recursive integration of relational paths to capture the semantics of long-range connectivity within the KG. By focusing on relational dependencies rather than solely node-centric aggregation, KGIN effectively captures and encodes the complex interactions and information flows inherent in the KG structure.
3. Empirical Validation: Experimental evaluations conducted across three benchmark datasets demonstrate that KGIN significantly outperforms state-of-the-art methods such as KGAT, KGNN-LS, and CKAN in terms of recommendation accuracy. The utilization of multiple intents coupled with relational path-awareness provides robust interpretability and a nuanced understanding of user behavior by identifying influential intents and relational pathways that inform recommendations.

Implications and Future Directions

The introduction of fine-grained intent modeling within knowledge-aware recommendation systems not only improves predictive accuracy but also opens new avenues for making these systems more interpretable. By attributing specific user actions to distinct intents, researchers and practitioners can gain deeper insights into user preferences and behavior patterns.

Practically, KGIN's architecture suggests enhancements in recommendation systems where understanding and explaining user choices is as crucial as the recommendations themselves, such as in personalized learning or clinical decision support systems.

Theoretically, the work paves the way for further exploration into disentangling the complex intent and relationship structures embedded in multi-relational graphs. Future work could focus on refining the intent modeling to better align with user cognitive processes or extending the approach to dynamic environments where user intents and graph structures evolve over time. Additionally, integrating KGIN with advanced self-supervised learning techniques could further enhance representation learning, providing richer and more robust embeddings in sparse data scenarios.

In conclusion, the paper offers significant advancements in knowledge-aware recommendation by incorporating intent modeling and relational path awareness, setting a precedent for more personalized and interpretable recommendations through sophisticated relational structures.