[1]WANG Jianzong,XIAO Jing,ZHU Xinghua,et al.Cold starts in collaborative filtering for federated recommender systems[J].CAAI Transactions on Intelligent Systems,2021,16(1):178-185.[doi:10.11992/tis.202009032]
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Cold starts in collaborative filtering for federated recommender systems
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
16
Number of periods:
2021 1
Page number:
178-185
Column:
吴文俊人工智能科学技术奖论坛
Public date:
2021-01-05
- Title:
- Cold starts in collaborative filtering for federated recommender systems
- Keywords:
- federated learning; privacy protection; data island; recommender system; collaborative filtering; cold start; machine learning; secure inner product
- CLC:
- TP391
- DOI:
- 10.11992/tis.202009032
- Abstract:
- Recommender systems based on federated learning has become one of the research hotspots in the recommender field. However, few studies focused on the cold start problem of federated recommender systems. Under the framework of federated learning, we propose a novel collaborative filtering algorithm for its solution: through involving more rating matrices, we can get a similarity matrix with secure inner product method, and implement the recommendation for new users to the system. In this work, we verify the performance of our method on MovieLens. The results show that our proposal is effective in solving the cold start problem in similarity-based collaborative filtering, and the recommendation effects vary according to the data distribution among different parties.
- References:
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Last Update:
2021-02-25