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Collaborative filtering driven by fast semantic feature analysis on Spark

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Abstract

Collaborative filtering (CF) is a prevailing technique utilized for recommendation systems and has been comprehensively explored to tackle the problem of information overload particularly in the Big Data context. The traditional CF algorithms are capable to perform adequately under various circumstances, nevertheless, there exist some shortcomings involving cold start and data sparsity. Moreover, a potential breakthrough rests in taking full advantage of any valuable semantic information contained in items. Therefore, for alleviating these defects, in this paper, we propose a two-stage collaborative filtering approach driven by Simhash-based semantic feature analysis, of which the first stage is Simhash-based semantic feature extraction for items and categories, and the second stage is reinforced CF rating prediction driven by intensely compressed category features. The rich semantic features of vast items and their categories can be rapidly extracted and compressed in the first stage by employing the Simhash, with being utilized to promote the traditional collaborative filtering processes. Besides, to solve the problems pertaining to the Big Data context, we design a parallel algorithm on Spark to accelerate the time-consuming process of semantic feature extraction for vast items. Finally, we conduct comprehensive experiments to validate the reinforced CF approach by adopting practical datasets, and the results reveal that compared with the traditional CF algorithms it can accomplish a promising performance.

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Acknowledgements

We would like to thank Professor Youping Li, the director of Future Network Research Center of SEU, for his enlightening suggestions for enhancing the traditional collaborative filtering via Simhash-based category features. This work is supported by the National Natural Science Foundation of China under Grants No. 61472080, No. 61672155, the Academician Consulting Project of Chinese Academy of Engineering under Grant No. 2018-XY-07, and the Collaborative Innovation Center of Novel Software Technology and Industrialization.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Peng Yang, Liang Gu & Xuan Liu

  2. Future Network Research Center, Southeast University, Nanjing, China

    Peng Yang, Liang Gu & Xuan Liu

  3. Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China

    Peng Yang, Liang Gu & Xuan Liu

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  1. Peng Yang
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  2. Liang Gu
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Correspondence to Peng Yang.

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Yang, P., Gu, L. & Liu, X. Collaborative filtering driven by fast semantic feature analysis on Spark. Wireless Netw 28, 1321–1334 (2022). https://doi.org/10.1007/s11276-018-01901-8

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  • DOI: https://doi.org/10.1007/s11276-018-01901-8

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