@inproceedings{song-etal-2024-cagk,
    title = "{CAGK}: Collaborative Aspect Graph Enhanced Knowledge-based Recommendation",
    author = "Song, Xiaotong  and
      Lin, Huiping  and
      Zhu, Jiatao  and
      Gong, Xinyi",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.235",
    pages = "2612--2621",
    abstract = "Auxiliary information, such as knowledge graph (KG), has become increasingly crucial in recommender systems. However, the current KG-based recommendation still has some limitations: (1) low link rates between items and KG entities, (2) redundant knowledge in KG. In this paper, we introduce the aspect, which refers to keywords describing item attributes in reviews, to KG-based recommendation, and propose a new model, Collaborative Aspect Graph enhanced Knowledge-based Network (CAGK). Firstly, CAGK builds a Collaborative Aspect Graph (CAG) with user-item interactions, aspects and KG, where aspects can fill most of the sparsity. Secondly, we leverage interactive information and aspect features to generate aspect-aware guidance signals to customize knowledge extraction and eliminate redundant knowledge. Lastly, we utilize low ratings and negative aspect sentiment to capture features of that users dislike to prevent repetitive recommendations of disliked items. Experimental results on two widely used benchmark datasets, Amazon-book and Yelp2018, confirm the superiority of CAGK.",
}

Auxiliary information, such as knowledge graph (KG), has become increasingly crucial in recommender systems. However, the current KG-based recommendation still has some limitations: (1) low link rates between items and KG entities, (2) redundant knowledge in KG. In this paper, we introduce the aspect, which refers to keywords describing item attributes in reviews, to KG-based recommendation, and propose a new model, Collaborative Aspect Graph enhanced Knowledge-based Network (CAGK). Firstly, CAGK builds a Collaborative Aspect Graph (CAG) with user-item interactions, aspects and KG, where aspects can fill most of the sparsity. Secondly, we leverage interactive information and aspect features to generate aspect-aware guidance signals to customize knowledge extraction and eliminate redundant knowledge. Lastly, we utilize low ratings and negative aspect sentiment to capture features of that users dislike to prevent repetitive recommendations of disliked items. Experimental results on two widely used benchmark datasets, Amazon-book and Yelp2018, confirm the superiority of CAGK.
Song, Xiaotong Lin, Huiping Zhu, Jiatao Gong, Xinyi
CAGK: Collaborative Aspect Graph Enhanced Knowledge-based Recommendation