Complexity 2019:1-11 (2019)
AbstractRecommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of modern recommender system mainly depends on the understanding and application of the context of recommendation requests. However, when leveraging deep learning algorithms for recommendation, the impact of context information such as recommendation time and location is often neglected. In this paper, a time-aware convolutional neural network- based personalized recommender system TC-PR is proposed. TC-PR actively recommends items that meet users’ interests by analyzing users’ features, items’ features, and users’ ratings, as well as users’ time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time-aware CNN-based recommendation algorithm which can effectively solve the problems of large data volume, large model, and slow speed of recommender system. The experimental results on the MovieLens-1m real dataset show that the proposed TC-PR can effectively solve the cold-start problem and greatly improve the speed of data processing and the accuracy of recommendation.
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Citations of this work
An Improved Sequential Recommendation Algorithm Based on Short-Sequence Enhancement and Temporal Self-Attention Mechanism.Jianjun Ni, Guangyi Tang, Tong Shen, Yu Cai & Weidong Cao - 2022 - Complexity 2022:1-15.
Deep Interest-Shifting Network with Meta-Embeddings for Fresh Item Recommendation.Zhao Li, Haobo Wang, Donghui Ding, Shichang Hu, Zhen Zhang, Weiwei Liu, Jianliang Gao, Zhiqiang Zhang & Ji Zhang - 2020 - Complexity 2020:1-13.
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