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Knowledge Graph Context-Enhanced Diversified Recommendation

Xiaolong Liu, Liangwei Yang, Zhiwei Liu, Mingdai Yang, Chen Wang, Hao Peng, Philip S. Yu

TL;DR

This paper tackles the echo-chamber problem in recommender systems by leveraging knowledge graphs to diversify recommendations. It introduces KG-Diverse, a framework that propagates KG information to item embeddings, learns diversification-aware user representations via the Diversified Embedding Learning (DEL) module, and enforces conditional alignment and uniformity (CAU) to preserve KG semantics while promoting diversity. To quantify KG-informed diversity, it defines Entity Coverage (EC) and Relation Coverage (RC) and evaluates on three public datasets (Amazon-Book, Last.FM, Movielens), where KG-Diverse achieves superior EC/RC with only modest declines in traditional accuracy metrics. Ablation studies and hyperparameter analyses demonstrate the contributions of KG propagation, relation encoding, DEL, and CAU, and provide guidance on network depth and regularization settings for balancing accuracy and diversity.

Abstract

The field of Recommender Systems (RecSys) has been extensively studied to enhance accuracy by leveraging users' historical interactions. Nonetheless, this persistent pursuit of accuracy frequently engenders diminished diversity, culminating in the well-recognized "echo chamber" phenomenon. Diversified RecSys has emerged as a countermeasure, placing diversity on par with accuracy and garnering noteworthy attention from academic circles and industry practitioners. This research explores the realm of diversified RecSys within the intricate context of knowledge graphs (KG). These KGs act as repositories of interconnected information concerning entities and items, offering a propitious avenue to amplify recommendation diversity through the incorporation of insightful contextual information. Our contributions include introducing an innovative metric, Entity Coverage, and Relation Coverage, which effectively quantifies diversity within the KG domain. Additionally, we introduce the Diversified Embedding Learning (DEL) module, meticulously designed to formulate user representations that possess an innate awareness of diversity. In tandem with this, we introduce a novel technique named Conditional Alignment and Uniformity (CAU). It adeptly encodes KG item embeddings while preserving contextual integrity. Collectively, our contributions signify a substantial stride towards augmenting the panorama of recommendation diversity within the realm of KG-informed RecSys paradigms.

Knowledge Graph Context-Enhanced Diversified Recommendation

TL;DR

This paper tackles the echo-chamber problem in recommender systems by leveraging knowledge graphs to diversify recommendations. It introduces KG-Diverse, a framework that propagates KG information to item embeddings, learns diversification-aware user representations via the Diversified Embedding Learning (DEL) module, and enforces conditional alignment and uniformity (CAU) to preserve KG semantics while promoting diversity. To quantify KG-informed diversity, it defines Entity Coverage (EC) and Relation Coverage (RC) and evaluates on three public datasets (Amazon-Book, Last.FM, Movielens), where KG-Diverse achieves superior EC/RC with only modest declines in traditional accuracy metrics. Ablation studies and hyperparameter analyses demonstrate the contributions of KG propagation, relation encoding, DEL, and CAU, and provide guidance on network depth and regularization settings for balancing accuracy and diversity.

Abstract

The field of Recommender Systems (RecSys) has been extensively studied to enhance accuracy by leveraging users' historical interactions. Nonetheless, this persistent pursuit of accuracy frequently engenders diminished diversity, culminating in the well-recognized "echo chamber" phenomenon. Diversified RecSys has emerged as a countermeasure, placing diversity on par with accuracy and garnering noteworthy attention from academic circles and industry practitioners. This research explores the realm of diversified RecSys within the intricate context of knowledge graphs (KG). These KGs act as repositories of interconnected information concerning entities and items, offering a propitious avenue to amplify recommendation diversity through the incorporation of insightful contextual information. Our contributions include introducing an innovative metric, Entity Coverage, and Relation Coverage, which effectively quantifies diversity within the KG domain. Additionally, we introduce the Diversified Embedding Learning (DEL) module, meticulously designed to formulate user representations that possess an innate awareness of diversity. In tandem with this, we introduce a novel technique named Conditional Alignment and Uniformity (CAU). It adeptly encodes KG item embeddings while preserving contextual integrity. Collectively, our contributions signify a substantial stride towards augmenting the panorama of recommendation diversity within the realm of KG-informed RecSys paradigms.
Paper Structure (24 sections, 13 equations, 5 figures, 3 tables)

This paper contains 24 sections, 13 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. PROBLEM FORMULATION
  4. Item entity-aware representation via Knowledge Graph propagation
  5. Diversified Embedding Learning
  6. Conditional Alignment and Uniformity
  7. Model Prediction
  8. Experiments
  9. Experimental Settings
  10. Datasets
  11. Baselines
  12. Evaluation Metrics
  13. Performance Comparison (RQ1)
  14. Parameter Setting
  15. Ablation Study (RQ2)
  16. ...and 9 more sections

Figures (5)

  • Figure 1: Diversified RecSys with knowledge graph.
  • Figure 2: The framework of KG-Diverse. The leftmost figure is the collaborative knowledge graph.
  • Figure 3: Accuracy-Diversity trade-off comparison. The upper-right model is better.
  • Figure 4: Parameter sensitivity of $L$, $\lambda_1$, and $\lambda_2$
  • Figure 5: Real Example from Amazon-Book.