A Time-Aware CNN-Based Personalized Recommender System

Complexity 2019:1-11 (2019)


Recommender 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.

Download options


    Upload a copy of this work     Papers currently archived: 72,766

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library


Added to PP

7 (#1,076,021)

6 months
1 (#386,499)

Historical graph of downloads
How can I increase my downloads?

References found in this work

No references found.

Add more references

Similar books and articles

Research on Context-Awareness Mobile SNS Recommendation Algorithm.Zhijun Zhang & Hong Liu - 2015 - Pattern Recognition and Artificial Intelligence 28.
Clustering Algorithms in Hybrid Recommender System on MovieLens Data.Urszula Kuzelewska - 2014 - Studies in Logic, Grammar and Rhetoric 37 (1):125-139.
A Multi-Agent Legal Recommender System.Lucas Drumond & Rosario Girardi - 2008 - Artificial Intelligence and Law 16 (2):175-207.