Interactive Path Reasoning on Graph for Conversational Recommendation (2007.00194v1)

Abstract: Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference. Conversational recommendation system (CRS) brings revolutions to those limitations by enabling the system to directly ask users about their preferred attributes on items. However, existing CRS methods do not make full use of such advantage -- they only use the attribute feedback in rather implicit ways such as updating the latent user representation. In this paper, we propose Conversational Path Reasoning (CPR), a generic framework that models conversational recommendation as an interactive path reasoning problem on a graph. It walks through the attribute vertices by following user feedback, utilizing the user preferred attributes in an explicit way. By leveraging on the graph structure, CPR is able to prune off many irrelevant candidate attributes, leading to better chance of hitting user preferred attributes. To demonstrate how CPR works, we propose a simple yet effective instantiation named SCPR (Simple CPR). We perform empirical studies on the multi-round conversational recommendation scenario, the most realistic CRS setting so far that considers multiple rounds of asking attributes and recommending items. Through extensive experiments on two datasets Yelp and LastFM, we validate the effectiveness of our SCPR, which significantly outperforms the state-of-the-art CRS methods EAR (arXiv:2002.09102) and CRM (arXiv:1806.03277). In particular, we find that the more attributes there are, the more advantages our method can achieve.

An Analysis of "Interactive Path Reasoning on Graph for Conversational Recommendation"

The paper entitled "Interactive Path Reasoning on Graph for Conversational Recommendation" presents an innovative approach to addressing the limitations inherent in traditional recommendation systems. These systems typically rely on historical interaction data to infer user preferences, which can lead to static and sometimes inaccurate recommendations. To overcome these challenges, the paper proposes a novel framework called Conversational Path Reasoning (CPR), where the recommender system interacts with users dynamically to determine user-preferred attributes.

Summary and Key Components

The CPR framework models conversational recommendation as a problem of interactive path reasoning on a heterogeneous graph. This graph incorporates vertices representing users, items, and attributes, with edges indicating relationships among them. The fundamental premise is that explicitly engaging users regarding their preferences via interactive conversation allows for a more accurate and interpretable recommendation process. The framework consists of several components, each focusing on different aspects of conversational recommendations:

1. Graph-Based Path Reasoning: The recommendation process is envisioned as navigating through a graph structure, where each walk on the graph corresponds to a sequence of attribute confirmations and item recommendations. This method effectively leverages the strength of graph-based reasoning to enhance the recommendation's accuracy and interpretability. By walking over attribute vertices, the CPR capitalizes on the explicit user feedback to make more informed recommendations.
2. Scalable and Explainable Recommendations: Unlike earlier approaches, CPR explicitly prunes irrelevant attributes by utilizing the graph structure, enhancing the efficiency and accuracy of the recommendations. This graph constraint also allows for the retention of conversation contextuality, leading to more coherent and focused dialogues.
3. Reinforcement Learning for Policy Optimization: The CPR framework incorporates a policy network using reinforcement learning to decide whether to ask attributes or recommend items. This reduces the decision space to two primary actions, simplifying training and optimizing policy efficiency.
4. Implementation and Empirical Study: A straightforward implementation named Simple CPR (SCPR) showcases the effectiveness of CPR through empirical studies on the Yelp and LastFM datasets. SCPR significantly outperforms existing conversational recommendation models like EAR and CRM, especially in scenarios with large attribute spaces.

Numerical Results and Implications

The experimental results underscore the enhanced success rate and reduced average turn count of SCPR compared to traditional methods. SCPR particularly excels in environments with extended attribute spaces, indicating that the graph-based pruning and explicit user involvement confer substantial benefits in context-rich settings.

The numerical comparison to state-of-the-art methods like EAR and CRM—both of which also incorporate Reinforcement Learning—demonstrates that SCPR's smaller decision space and explicit graph-based reasoning lead to more efficient and effective outcomes.

Implications and Future Directions

The CPR framework offers several theoretical and practical implications for the future of conversational recommendation systems. From a theoretical standpoint, the introduction of graph-based reasoning into conversational recommendations provides a robust foundation for building more nuanced, dynamic, and scalable systems. It also opens avenues for further exploration into interactive systems that can seamlessly integrate user feedback into multi-faceted recommendation processes.

Practically, the CPR framework has the potential to be adopted in various domains where fine-grained understanding of user preferences is crucial. The explicit nature of the reasoning paths ensures that recommendations are interpretable, allowing for transparency and user trust.

Future research could explore further augmentations of the CPR approach, including advanced graph learning techniques, handling negative feedback explicitly, and seamless integration of this framework with other user interaction modalities such as natural language processing and multimodal data. Additionally, exploring the possibility of updating user, item, and attribute embeddings dynamically during interactions stands as an intriguing frontier.

In conclusion, the paper provides a compelling advancement in the field of conversational recommendations, marking a significant step towards interactive, adaptive, and transparent AI systems.