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A novel video recommendation system based on efficient retrieval of human actions

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  • Ramezani, Mohsen
  • Yaghmaee, Farzin

Abstract

In recent years, fast growth of online video sharing eventuated new issues such as helping users to find their requirements in an efficient way. Hence, Recommender Systems (RSs) are used to find the users’ most favorite items. Finding these items relies on items or users similarities. Though, many factors like sparsity and cold start user impress the recommendation quality. In some systems, attached tags are used for searching items (e.g. videos) as personalized recommendation. Different views, incomplete and inaccurate tags etc. can weaken the performance of these systems. Considering the advancement of computer vision techniques can help improving RSs. To this end, content based search can be used for finding items (here, videos are considered). In such systems, a video is taken from the user to find and recommend a list of most similar videos to the query one. Due to relating most videos to humans, we present a novel low complex scalable method to recommend videos based on the model of included action. This method has recourse to human action retrieval approaches. For modeling human actions, some interest points are extracted from each action and their motion information are used to compute the action representation. Moreover, a fuzzy dissimilarity measure is presented to compare videos for ranking them. The experimental results on HMDB, UCFYT, UCF sport and KTH datasets illustrated that, in most cases, the proposed method can reach better results than most used methods.

Suggested Citation

  • Ramezani, Mohsen & Yaghmaee, Farzin, 2016. "A novel video recommendation system based on efficient retrieval of human actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 607-623.
    • Handle: RePEc:eee:phsmap:v:457:y:2016:i:c:p:607-623
    DOI: 10.1016/j.physa.2016.03.101
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    References listed on IDEAS

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    1. Ramezani, Mohsen & Moradi, Parham & Akhlaghian, Fardin, 2014. "A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 72-84.
    2. Shang, Ming-Sheng & Zhang, Zi-Ke & Zhou, Tao & Zhang, Yi-Cheng, 2010. "Collaborative filtering with diffusion-based similarity on tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1259-1264.
    3. Zhang, Jing & Peng, Qinke & Sun, Shiquan & Liu, Che, 2014. "Collaborative filtering recommendation algorithm based on user preference derived from item domain features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 66-76.
    4. Zhang, Zi-Ke & Zhou, Tao & Zhang, Yi-Cheng, 2010. "Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 179-186.
    5. Zeng, Wei & Zhu, Yu-Xiao & Lü, Linyuan & Zhou, Tao, 2011. "Negative ratings play a positive role in information filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4486-4493.
    6. Shang, Ming-Sheng & Jin, Ci-Hang & Zhou, Tao & Zhang, Yi-Cheng, 2009. "Collaborative filtering based on multi-channel diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(23), pages 4867-4871.
    7. Tao Zhou & Matúš Medo & Giulio Cimini & Zi-Ke Zhang & Yi-Cheng Zhang, 2011. "Emergence of Scale-Free Leadership Structure in Social Recommender Systems," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-6, July.
    8. Zhang, Chu-Xu & Zhang, Zi-Ke & Yu, Lu & Liu, Chuang & Liu, Hao & Yan, Xiao-Yong, 2014. "Information filtering via collaborative user clustering modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 195-203.
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    Cited by:

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