Session 8: Knowledge and Context

Date: Thursday September 21, 11:15 AM – 12:35 PM (GMT+8)
Room: Hall 406D
Session Chair: Vito Walter Anelli
Parallel with: Session 7: Interactive Recommendation 1

• RESKnowledge-based Multiple Adaptive Spaces Fusion for Recommendation
  by Meng Yuan (Institute of Artificial Intelligence, Beihang University, Beijing 100191, China), Fuzhen Zhuang (Institute of Artificial Intelligence, Beihang University, Beijing 100191, China), Zhao Zhang (University of Chinese Academy of Sciences, Beijing 100191, China), Deqing Wang (School of Computer Science and Engineering, Beihang University, Beijing 100191, China) and Jin Dong (Beijing Academy of Blockchain and Edge Computing).

  Since Knowledge Graphs (KGs) contain rich semantic information, recently there has been an influx of KG-enhanced recommendation methods. Most of existing methods are entirely designed based on euclidean space without considering curvature. However, recent studies have revealed that a tremendous graph-structured data exhibits highly non-euclidean properties. Motivated by these observations, in this work, we propose a knowledge-based multiple adaptive spaces fusion method for recommendation, namely MCKG. Unlike existing methods that solely adopt a specific manifold, we introduce the unified space that is compatible with hyperbolic, euclidean and spherical spaces. Furthermore, we fuse the multiple unified spaces in an attention manner to obtain the high-quality embeddings for better knowledge propagation. In addition, we propose a geometry-aware optimization strategy which enables the pull and push processes benefited from both hyperbolic and spherical spaces. Specifically, in hyperbolic space, we set smaller margins in the area near to the origin, which is conducive to distinguishing between highly similar positive items and negative ones. At the same time, we set larger margins in the area far from the origin to ensure the model has sufficient error tolerance. The similar manner also applies to spherical spaces. Extensive experiments on three real-world datasets demonstrate that the MCKG has a significant improvement over state-of-the-art recommendation methods. Further ablation experiments verify the importance of multi-space fusion and geometry-aware optimization strategy, justifying the rationality and effectiveness of MCKG.

  Full text in ACM Digital Library

• RESKGTORe: Tailored Recommendations through Knowledge-aware GNN Models
  by Alberto Carlo Maria Mancino (Politecnico di Bari), Antonio Ferrara (Politecnico di Bari), Salvatore Bufi (Polytechnic University of Bari), Daniele Malitesta (Polytechnic University of Bari), Tommaso Di Noia (Polytechnic University of Bari) and Eugenio Di Sciascio (Polytechnic University of Bari).

  Knowledge graphs (KG) have been proven to be a powerful source of side information to enhance the performance of recommendation algorithms. Their graph-based structure paves the way for the adoption of graph-aware learning models such as Graph Neural Networks (GNNs). In this respect, state-of-the-art models achieve good performance and interpretability via user-level combinations of intents leading users to their choices. Unfortunately, such results often come from and end-to-end learnings that considers a combination of the whole set of features contained in the KG without any analysis of the user decisions. In this paper, we introduce KGTORe, a GNN-based model that exploits KG to learn latent representations for the semantic features, and consequently, interpret the user decisions as a personal distillation of the item feature representations. Differently from previous models, KGTORe does not need to process the whole KG at training time but relies on a selection of the most discriminative features for the users, thus resulting in improved performance and personalization. Experimental results on three well-known datasets show that KGTORe achieves remarkable accuracy performance and several ablation studies demonstrate the effectiveness of its components.

  Full text in ACM Digital Library

• RESPairwise Intent Graph Embedding Learning for Context-Aware Recommendation
  by Dugang Liu (Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)), Yuhao Wu (Shenzhen University), Weixin Li (Shenzhen University), Xiaolian Zhang (Huawei 2012 Lab), Hao Wang (Huawei 2012 Lab), Qinjuan Yang (Huawei 2012 Lab) and Zhong Ming (College of Computer Science and Software Engineering, Shenzhen University).

  Although knowledge graph have shown their effectiveness in mitigating data sparsity in many recommendation tasks, they remain underutilized in context-aware recommender systems (CARS) with the specific sparsity challenges associated with the contextual features, i.e., feature sparsity and interaction sparsity. To bridge this gap, in this paper, we propose a novel pairwise intent graph embedding learning (PING) framework to efficiently integrate knowledge graph into CARS. Specifically, our PING contains three modules: 1) a graph construction module is used to obtain a pairwise intent graph (PIG) containing nodes for users, items, entities and enhanced intent, where enhanced intent nodes are generated by applying user intent fusion (UIF) on relational intent and contextual intent, and two sub-intents are derived from the semantic information and contextual information, respectively; 2) a pairwise intent joint graph convolution module is used to obtain the refined embeddings of all the features by executing a customized convolution strategy on PIG, where each enhanced intent node acts as a hub to efficiently propagate information among different features and between all the features and knowledge graph; 3) a recommendation module with the refined embeddings is used to replace the randomly initialized embeddings of downstream recommendation models to improve model performance. Finally, we conduct extensive experiments on three public datasets to verify the effectiveness and compatibility of our PING.

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• INDHeterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
  by Bin Yin (Meituan), Junjie Xie (Meituan), Yu Qin (Meituan), Zixiang Ding (Meituan), Zhichao Feng (Meituan), Xiang Li (Unaffiliated) and Wei Lin (Unaffiliated).

  The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems. However, the conventional approach of incorporating various types of heterogeneous behavior into recommendation models leads to feature sparsity and knowledge fragmentation issues. To address this challenge, we propose a novel approach for personalized recommendation via Large Language Model (LLM), by extracting and fusing heterogeneous knowledge from user heterogeneous behavior information. In addition, by combining heterogeneous knowledge and recommendation tasks, instruction tuning is performed on LLM for personalized recommendations. The experimental results demonstrate that our method can effectively integrate user heterogeneous behavior and significantly improve recommendation performance.

  Full text in ACM Digital Library

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