Cross-domain Book Recommendation Method Based on Heterogeneous Information Network

doi:10.3969/j.issn.1006-2475.2025.08.004

Abstract

Abstract:
Abstract: Aiming at the problem that the accuracy of the recommendation algorithm is reduced due to data sparsity and cold start in the learning data domain in the current student book recommendation model， a cross-domain recommendation method HeterCDR （Heter Cross-Domain Recommendation） based on heterogeneous information networks is proposed. The modeling of source domain information is realized by introducing the translation distance model to construct a heterogeneous information network， and the DANN model is used to realize the migration of source domain information to the target domain. The heterogeneous information network and cross-domain recommendation are combined to improve the accuracy of target domain recommendation. The experimental data set uses the relevant data of students from grade 20 to grade 21 of a higher vocational college. The experimental results show that compared with other recommendation models， the hit rate of the HeterCDR model is improved by about 3.35% on average， the NDCG index is improved by about 2.8%， and the RMSE index is reduced by about 2.65%.

Key words: Key words: book recommendation, heterogeneous information network, cross-domain recommendation, the network represents learning, transfer learning

CLC Number:

中图分类号：TP391

SHI Fengyuan1, 2, MAO Yi3, JIAO Lei4. Cross-domain Book Recommendation Method Based on Heterogeneous Information Network[J]. Computer and Modernization, 2025, 0(08): 24-30.

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