Abstract
Long-tail effect is common in recommender systems, meaning that a tiny number of users have lots of interaction with items, while the majority of users have extremely little interaction. Most of existing approaches to recommender systems, especially methods based on collaborative filtering, suffer severely from long-tail problems due to the low resource issue. In order to handle the problem of long-tail recommendation, we utilize knowledge graph to enrich the representation of users and items. As auxiliary prior information source, knowledge graph has been popular technology for recommender systems nowadays, while it is rarely exploited with respect to the low resource issue. In this paper, we propose a knowledge-enhanced collaborative meta learner, which combines the priors in knowledge graph and collaborative information between head users to promote long-tail recommendation performance. We conduct several experiments on two real world datasets for long-tail recommendation. The results show that our approach outperforms several commonly used recommendation methods in the long tail scenario.
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Acknowledgements
This work is funded by NSFC U19B2027/91846204/61473260, national key research program 2018YFB1402800, and supported by AlibabaZJU Frontier Technology Research Center.
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Wen, B., Deng, S., Chen, H. (2021). Knowledge-Enhanced Collaborative Meta Learner for Long-Tail Recommendation. In: Chen, H., Liu, K., Sun, Y., Wang, S., Hou, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence. CCKS 2020. Communications in Computer and Information Science, vol 1356. Springer, Singapore. https://doi.org/10.1007/978-981-16-1964-9_26
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DOI: https://doi.org/10.1007/978-981-16-1964-9_26
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