A New Approach for Item Ranking Based on Review Scores Reflecting Temporal Trust Factor

  • Kazumi Saito
  • Masahiro Kimura
  • Kouzou Ohara
  • Hiroshi Motoda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8393)


We propose a new item-ranking method that is reliable and can efficiently identify high-quality items from among a set of items in a given category using their review-scores which were rated and posted by users. Typical ranking methods rely only on either the number of reviews or the average review score. Some of them discount outdated ratings by using a temporal-decay function to make a fair comparison between old and new items. The proposed method reflects trust levels by incorporating a trust discount factor into a temporal-decay function. We first define the MTDF (Multinomial with Trust Discount Factor) model for the review-score distribution of each item built from the observed review data. We then bring in the notion of z-score to accommodate the trust variance that comes from the number of reviews available, and propose a z-score version of MTDF model. Finally we demonstrate the effectiveness of the proposed method using the MovieLens dataset, showing that the proposed ranking method can derive more reasonable and trustable rankings, compared to two naive ranking methods and the pure z-score based ranking method.


Item-ranking Trust discount factor Z-socre 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cormode, G., Shkapenyuk, V., Srivastava, D., Xu, B.: Forward decay: A practical time decay model for streaming systems. In: Proceedings of the 25th IEEE International Conference on Data Engineering (ICDE 2009), pp. 138–149 (2009)Google Scholar
  2. 2.
    Even-Dar, E., Shapira, A.: A note on maximizing the spread of influence in social networks. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 281–286. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Goldenberg, J., Libai, B., Muller, E.: Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters 12, 211–223 (2001)CrossRefGoogle Scholar
  4. 4.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003), pp. 137–146 (2003)Google Scholar
  5. 5.
    Kimura, M., Saito, K., Ohara, K., Motoda, H.: Opinion formation by voter model with temporal decay dynamics. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012, Part II. LNCS, vol. 7524, pp. 565–580. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Koren, Y.: Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), pp. 447–456 (2009)Google Scholar
  7. 7.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (WSDM 2011), pp. 287–296. ACM, New York (2011)CrossRefGoogle Scholar
  8. 8.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces (IUI 2005), pp. 167–174. ACM, New York (2005)Google Scholar
  9. 9.
    Papadakis, G., Niederée, C., Nejdl, W.: Decay-based ranking for social application content. In: Proceedings of the 6th International Conference on Web Information Systems and Technologies (WEBIST 2010), pp. 276–281 (2010)Google Scholar
  10. 10.
    Saito, K., Kimura, M., Ohara, K., Motoda, H.: Learning asynchronous-time information diffusion models and its application to behavioral data analysis over social networks. Journal of Computer Engineering and Informatics 1, 30–57 (2013)CrossRefGoogle Scholar
  11. 11.
    Sood, V., Redner, S.: Voter model on heterogeneous graphs. Physical Review Letters 94, 17801 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kazumi Saito
    • 1
  • Masahiro Kimura
    • 2
  • Kouzou Ohara
    • 3
  • Hiroshi Motoda
    • 4
    • 5
  1. 1.School of Administration and InformaticsUniversity of ShizuokaJapan
  2. 2.Department of Electronics and InformaticsRyukoku UniversityJapan
  3. 3.Department of Integrated Information TechnologyAoyama Gakuin UniversityJapan
  4. 4.Institute of Scientific and Industrial ResearchOsaka UniversityJapan
  5. 5.School of Computing and Information SystemsUniversity of TasmaniaJapan

Personalised recommendations