Abstract
Recommender systems, a specialised subfield within information retrieval, are crucial for identifying items that align with users’ preferences. A knowledge graph-based recommender system can excel in the task of making recommendations due to the rich semantic information inherent in knowledge graphs. In this paper, our central focus is to investigate the impact of the semantic richness of knowledge graphs on the effectiveness of such recommender systems. To explore this research topic, we focus on the movie recommendation domain. For this, we create seven movie ontologies with varying levels of semantic richness, and combine these ontologies with movie data from the MovieLens 1M dataset and augmented using additional open linked data derived from Wikidata to produce seven different movie knowledge graphs. We provide the ontologies and knowledge graphs in an open-source repository. We then conduct experiments using two different approaches, consisting of nine Knowledge Graph Based Recommending (KGBR) methods and four Link Prediction (LP) methods based on Knowledge Graph Embeddings (KGE). The results demonstrate that richness of the knowledge graph does not impact the performance of KGBR methods significantly, but has a considerable impact on the KGE-based LP methods. We furthermore compare the best performing KGBR methods with the KGE-based LP methods, showing that the LP methods outperform all other recommendation methods when paired with the most extensive knowledge graph. From this, we conclude that the richness of the knowledge graph does not have a significant impact if the method already integrates other recommending approaches, but can heavily impact the LP methods employing the KGE approach, which interprets relationships as translations in embedding spaces. This supports the idea that using extended knowledge graphs is an effective approach for successful recommender systems.
Original language | English |
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Pages (from-to) | 11-20 |
Number of pages | 10 |
Journal | CEUR Workshop Proceedings |
Volume | 3817 |
Publication status | Published - 1 Jan 2024 |
Event | 6th Knowledge-Aware and Conversational Recommender Systems Workshop, KaRS 2024 - Bari, Italy Duration: 14 Oct 2024 → 18 Oct 2024 Conference number: 6th https://kars-workshop.github.io/2024/ |
Keywords
- Embedding
- Knowledge Graphs
- Link Prediction
- Ontologies
- Recommender Systems