Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion (2007.04032v1)

Abstract: Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major issues still remain to be solved. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference. To address these issues, we incorporate both word-oriented and entity-oriented knowledge graphs (KG) to enhance the data representations in CRSs, and adopt Mutual Information Maximization to align the word-level and entity-level semantic spaces. Based on the aligned semantic representations, we further develop a KG-enhanced recommender component for making accurate recommendations, and a KG-enhanced dialog component that can generate informative keywords or entities in the response text. Extensive experiments have demonstrated the effectiveness of our approach in yielding better performance on both recommendation and conversation tasks.

An Overview of "Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion"

Introduction and Key Issues

Conversational Recommender Systems (CRSs) are engineered to enhance user experience by delivering item recommendations through interactive, natural language conversations. Despite their potential, two main challenges curb their efficacy: the scarcity of contextual information within conversation data, and the semantic dissonance between colloquial user expressions and item-level preferences. The paper by Kun Zhou et al., “Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion," proposes an innovative approach utilizing knowledge graphs (KGs) to address these challenges effectively.

Methodology and Framework

To counteract the limitations identified in traditional CRSs, this paper introduces a dual-KG approach, employing both word-oriented and entity-oriented knowledge graphs. Specifically, ConceptNet serves as the word-level KG, encapsulating semantic relationships between words, while DBpedia functions as the entity-level KG, offering structured data about items. The core innovation lies in the integration of these KGs with a Mutual Information Maximization (MIM) strategy to align and fuse their semantic spaces, thereby enriching data representations and bridging the gap between language and item semantics.

The methodology unfolds in several critical stages:

1. Encoding Knowledge Graphs: Graph Convolutional Networks (GCN) are utilized to encode ConceptNet, and R-GCN models handle DBpedia, enabling the capture of complex relations within and between words and items.
2. Semantic Fusion via MIM: The MIM framework enforces a proximity in semantic space between related words and items, informed by conversational contexts. This mutual information strategy draws upon co-occurrence data, effectively aligning disparate semantic spaces.
3. KG-enhanced Modules: The paper proposes KG-enhanced recommender and dialog components. The recommender leverages user preferences drawn from the aligned semantic space to recommend items, while the dialog component uses Transformer architectures enriched by KG data to generate substantive conversational replies.

Experimental Results

The comprehensive experimentations reported in the paper demonstrate significant improvements in both recommendation accuracy and dialogue generation metrics, compared to contemporary baseline models such as REDIAL and KBRD. Specifically, KGSF exhibited stronger Recall rates across diverse settings, with compelling performance even in traditionally challenging cold-start scenarios. This robust performance underscores the model's proficiency in leveraging KG-based semantic fusion to transcend the limitations of sparse conversational data.

In terms of dialogue generation, KGSF achieved higher scores in diversity and informativeness, confirmed through both computational metrics such as Distinct-n and human evaluation metrics. This indicates a successful generation of diverse and contextually rich replies, crucial for enhancing overall user experience.

Implications and Future Directions

The paper advances the CRS domain by showcasing how concept-oriented semantic embeddings can enhance user interaction through more accurate item recommendations and sophisticated dialogue generation. It bridges the semantic chasm in CRS and offers a scalable framework adaptable to various domains beyond movie recommendations.

Looking forward, the authors suggest exploring further enrichment of CRS by incorporating additional user-centered data like demographic information, potentially allowing for even more personalized recommendations. Moreover, integrating historical user-item interaction data could offer pre-learned user profiles, expanding the CRS capabilities. Another suggested extension is to enhance the persuasive nature of responses, thereby making systems not only informative but also more engaging and convincing.

In conclusion, this paper provides a robust framework that effectively enhances conversational recommender systems. Through knowledge graph-based semantic fusion, Zhou et al. significantly advance the ability of CRSs to interact naturally and respond meaningfully to user inputs, promising improvements in both technical implementation and user satisfaction.