[深度学习] 2018-Collaborative Memory Network for Recommendation Systems.pdf

说明： 2018-Collaborative Memory Network for Recommendation Systems
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[深度学习] 2018-Buy It Again- Modeling Repeat Purchase Recommendations.pdf

说明： 2018-Buy It Again- Modeling Repeat Purchase Recommendations
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[深度学习] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba.pdf

说明： 2018-Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
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[深度学习] 2017-Neural Collaborative Filtering.pdf

说明： In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny.
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[深度学习] Content-aware Movie Recommendation.pdf

说明： 2017-Leveraging Long and Short-term Information in Content-aware Movie Recommendation
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[深度学习] 2017-Joint Deep Modeling of Users and Items Using Reviews for Recommendation.pdf

说明： 2017-Joint Deep Modeling of Users and Items Using Reviews for Recommendation
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[深度学习] 2017-Deep Matrix Factorization Models for Recommender Systems.pdf

说明： 2017-Deep Matrix Factorization Models for Recommender Systems
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[深度学习] 2017-Collaborative Autoencoder for Recommender Systems.pdf

说明： 2017-Collaborative Autoencoder for Recommender Systems
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[深度学习] 2016-Session based Recommendations with Recurrent Neural Networks.pdf

说明： Session based Recommendations with Recurrent Neural Networks
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[深度学习] Improved Recurrent Neural Networks for Session-based Recommendations.pdf

说明： Improved Recurrent Neural Networks for Session-based Recommendations
<qq_31367595> 上传 | 大小：397kb

[深度学习] Convolutional Matrix Factorization for Document Context-Aware Recommendation.pdf

说明： 2016-Convolutional Matrix Factorization for Document Context-Aware Recommendation
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[深度学习] 2016-Collaborative Denoising Auto-Encoders for Top-N Recommender Systems.pdf

说明： Most real-world recommender services measure their performance based on the top-N results shown to the end users. Thus, advances in top-N recommendation have far-ranging consequences in practical applications. In this paper, we present a novel metho
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