The book recommendation algorithm based on knowledge graph and collaborative filtering

Zhang Hui; Tan Hongsen

doi:10.1117/12.3033200

5 July 2024 The book recommendation algorithm based on knowledge graph and collaborative filtering

Zhang Hui, Tan Hongsen

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131842K (2024) https://doi.org/10.1117/12.3033200
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Traditional book recommendation algorithms only consider external rating data when handling book recommendations and face issues such as cold start for items. This study proposes a book recommendation algorithm that integrates Knowledge Graphs with collaborative filtering. By constructing a book Knowledge Graph and introducing semantic information between books as a basis for recommendation, this approach uses the knowledge representation learning model TransE to map the book Knowledge Graph into a low-dimensional continuous vector space. This mapping captures the relationships between book entities and calculates the semantic similarity between books. Then, by combining this with user behavior similarity derived from collaborative filtering algorithms through linear fusion, a recommendation list is generated. Experimental results show that compared to baseline algorithms, this algorithm improves in terms of accuracy, recall, and F-measure.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhang Hui and Tan Hongsen "The book recommendation algorithm based on knowledge graph and collaborative filtering", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131842K (5 July 2024); https://doi.org/10.1117/12.3033200

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