Evaluating Content-based Pre-Training Strategies for a Knowledge-aware Recommender System based on Graph Neural Networks

被引:0
作者
Spillo, Giuseppe [1 ]
Bottalico, Francesco [1 ]
Musto, Cataldo [1 ]
de Gemmis, Marco [1 ]
Lops, Pasquale [1 ]
Semeraro, Giovanni [1 ]
机构
[1] Univ Bari, Bari, Italy
来源
PROCEEDINGS OF THE 32ND ACM CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION, UMAP 2024 | 2024年
关键词
Content-based RSs; Graph Neural Networks; Word Embeddings;
D O I
10.1145/3627043.3659548
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we introduce a Knowledge-aware Recommender System (KARS) based on Graph Neural Networks that exploit pre-trained content-based embeddings to improve the representation of users and items. Our approach relies on the intuition that textual features can describe the items in the catalog from a different point of view, so they are worth to be exploited to provide users with more accurate recommendations. Accordingly, we used encoding techniques to learn a pre-trained representation of the items in the catalogue based on textual content, and we used these embeddings to feed the input layer of a KARS based on GCNs. In this way, the GCN is able to encode both the knowledge coming from the unstructured content and the structured knowledge provided by the KG (ratings and item descriptive properties). As shown in our experiments, the exploitation of pre-trained embeddings improves the predictive accuracy of the KARS, which overcomes all the baselines we considered in several experimental settings.
引用
收藏
页码:165 / 171
页数:7
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