Abstract
As an important technology of Internet products, the recommender system can help users to obtain the information they need and alleviate the problem of information overload. In the implicit feedback recommender system, the key issue is how to represent users and products. In recent years, deep learning has achieved good performance in many fields including speech recognition, computer vision and natural language processing. We propose a deep learning-enhanced framework for implicit feedback recommendation. In this framework, we simultaneously learn the new distributed representation of users and items via node2vec to improve the negative sampling strategy. Finally, we develop a deep neural network recommendation model to integrate user features, product features and interaction features. Experiments conducted on two real-world datasets demonstrate the effectiveness of the proposed framework and methods.
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Acknowledgments
This work is supported by the Major Program of the National Natural Science Foundation of China (71490725), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (71521001), the National Natural Science Foundation of China (71872060, 71722010, 91546114, 91746302, 71501057) and The National Key Research and Development Program of China (2017YFB0803303).
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Liu, Y., Tian, Z., Sun, J. et al. Distributed representation learning via node2vec for implicit feedback recommendation. Neural Comput & Applic 32, 4335–4345 (2020). https://doi.org/10.1007/s00521-018-03964-2
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DOI: https://doi.org/10.1007/s00521-018-03964-2