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With the in\u2010depth 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\u2010aware convolutional neural network\u2010 (CNN\u2010) based personalized recommender system <jats:italic>TC-PR<\/jats:italic> is proposed. <jats:italic>TC-PR<\/jats:italic> actively recommends items that meet users\u2019 interests by analyzing users\u2019 features, items\u2019 features, and users\u2019 ratings, as well as users\u2019 time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time\u2010aware CNN\u2010based 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\u20101m real dataset show that the proposed <jats:italic>TC-PR<\/jats:italic> can effectively solve the cold\u2010start problem and greatly improve the speed of data processing and the accuracy of recommendation.<\/jats:p>","DOI":"10.1155\/2019\/9476981","type":"journal-article","created":{"date-parts":[[2019,12,18]],"date-time":"2019-12-18T23:32:23Z","timestamp":1576711943000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Time\u2010Aware CNN\u2010Based Personalized Recommender System"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3817-7333","authenticated-orcid":false,"given":"Dan","family":"Yang","sequence":"first","affiliation":[]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4244-4091","authenticated-orcid":false,"given":"Sifeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"XueDong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2019,12,18]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"crossref","unstructured":"DongJ. 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