Journal
IEEE ACCESS
Volume 8, Issue -, Pages 18350-18361Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2968297
Keywords
Recommender systems; new user problems; user behavior; context-aware recommendation; efficient training
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Funding
- National Natural Science Foundation of China [61902231]
- Research Startup Fund Project of Shantou University [NTF18017]
- Natural Science Foundation of Guangdong Province [2019A1515010943]
- Key Project of Basic and Applied Basic Research of Colleges and Universities in Guangdong Province (Natural Science) [2018KZDXM035]
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Predicting new user behavior has always been a challenging issue in intelligent recommender systems. This challenge is mainly due to the extreme asymmetry of information between new users and old users. Existing factorization models can efficiently process and map asymmetric information, but they are not good at mining deep relationships between contexts when compressing high-dimensional data. In contrast, neural network methods can deeply exploit the relationship between contexts; however, their training cost is much larger than factorization approaches. Therefore, this paper proposes a scalable and efficient recommender to solve the new user problem by filling the gap between factorization models and neural networks. The scalable part is a neural network that can jointly encode, compress, and fuse various types of contexts. The efficient part is a factorization model based on a correlation constraint mechanism and a projection strategy, which enables an asymmetric mapping of information between old and new users. The entire recommender fuses the two parts so that the factorization model and the neural network can complement each other. The experimental results show that our approach can achieve a good balance between performance and training efficiency compared to state-of-the-art methods.
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