Explainable recommendation based on fusion representation of multi-type feature embedding-Reference-Cited by-同舟云学术

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Explainable recommendation based on fusion representation of multi-type feature embedding

Published:2023-12-22 Issue: Volume: Page:
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Zheng Jianxing,Chen Sen,Cao Feng,Peng Furong,Huang Mingqing

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanxi Province

Shenzhen Basic Research Foundation

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-023-05831-x.pdf

Reference55 articles.

1. Shi SY, Zhang M, YQ, L, Ma SP (2018) Attention-based adaptive model to unify warm and cold starts recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp 127–136

2. Zhang YF, Lai GK, Zhang M, Zhang Y, Liu YQ, Ma SP (2014) Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 83–92

3. Zhang XF, Liu HJ, Chen XY, Zhong JB, Wang D (2020) A novel hybrid deep recommendation system to differentiate user’s preference and item’s attractiveness. Inf Sci 519:306–316

4. Wu Z, Zhou X (2023) M2eu: Meta learning for cold-start recommendation via enhancing user preference estimation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1158–1167

5. Zhou ZH, Zhang LL, Yang N (2023) Contrastive collaborative filtering for cold-start item recommendation. In: Proceedings of the ACM Web Conference, pp 928–937

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