Computing Personalized PageRank Based on Temporal-Biased Proximity

  • Bundit Manaskasemsak
  • Pramote Teerasetmanakul
  • Kankamol Tongtip
  • Athasit Surarerks
  • Arnon Rungsawang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 253)


Dynamic behaviors of World Wide Web is one of the most important characteristics that challenge search engine administrators to manipulate their search collection. Web content and links are changed each day to provide up-to-date information. In addition, a fresh web page, like new news article, is often more interesting to web users than a stale one. Thus, an analysis of temporal activities of the Web can contribute to improve better search and result ranking. In this paper, we propose a web personalized link-based ranking scheme that incorporates temporal information extracted from historical page activities. We first quantify page modifications over time and design a time-proximity model used in calculating inverse propagation scores of web pages. These scores are then used as a bias of personalized PageRank for page authority assessment. We conduct the experiments on a real-world web collection gathered from the Internet Archive. The results show that our approach improves upon PageRank in ranking of search results with respect to human users’ preference.


Temporal-biased personalized PageRank Time-proximity model Temporal analysis PageRank computation Web ranking algorithm 


  1. 1.
    Adamic LA, Huberman BA (2001) The web’s hidden order. Commun ACM 44(9):55–59CrossRefGoogle Scholar
  2. 2.
    Baeza-Yates RA, Ribeiro-Neto BA (1999) Modern Information Retrieval. ACM Press & Addison Wesley, New YorkGoogle Scholar
  3. 3.
    Berberich K, Vazirgiannis M, Weikum G (2006) Time-aware authority ranking. Internet Math 2(3):301–332CrossRefMathSciNetGoogle Scholar
  4. 4.
    Brin S, Motwani R, Page L, Winograd T (1998) What can you do with a web in your pocket. IEEE Data Eng Bull 21(2):37–47Google Scholar
  5. 5.
    Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN 30(1–7):107–117CrossRefGoogle Scholar
  6. 6.
    Cho J, Roy S.(2004) Impact of search engines on page popularity. In: Proceeding of the 13th WWW ConfGoogle Scholar
  7. 7.
    Dai N, Davison BD (2010) Freshness matters: In flowers, food, and web authority. In: Proceeding of the 33th ACM SIGIR ConfGoogle Scholar
  8. 8.
    Gerani S, Carman M, Crestani F (2012) Aggregation methods for proximity-based opinion retrieval. ACM Trans. Inform. Syst., 30(4), article 26Google Scholar
  9. 9.
    Golub GH, Loan CFV (1996) Matrix Computations. Johns Hopkins University Press, Baltimore and LondonMATHGoogle Scholar
  10. 10.
    Gyöngyi Z, Garcia-Molina H, Pederson J (2004) Combating web spam with TrustRank. In: Proceeding of the 30th Conf. on VLDBGoogle Scholar
  11. 11.
    Haveliwala TH (1999) Efficient computation of PageRank. Technical Report, Stanford InfoLabGoogle Scholar
  12. 12.
    Haveliwala TH (2003) Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search. IEEE Trans Knowl Data Eng 15(4):784–796CrossRefGoogle Scholar
  13. 13.
    Järvelin K, Kekäläinen J (2000) IR evaluation methods for retrieving highly relevant documents. In: Proceeding of the 23rd ACM SIGIR ConfGoogle Scholar
  14. 14.
    Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst 20(4):422–446CrossRefGoogle Scholar
  15. 15.
    Jeh G, Widom J (2003) Scaling personalized web search. In: Proceeding of the 12th WWW ConferenceGoogle Scholar
  16. 16.
    Liu Y, Liu TY, Gao B, Ma Z, Li H (2010) A framework to compute page importance based on user behaviors. Inf Retrieval 13(1):22–45CrossRefGoogle Scholar
  17. 17.
    Lv Y, Zhai C (2009) Positional language models for information retrieval. In: Proceeding of the 32nd ACM SIGIR ConferenceGoogle Scholar
  18. 18.
    Page L, Brin S, Motwani R, Winograd T (1999) The PageRank citation ranking: Bringing order to the web. Technical Report, Stanford InfoLabGoogle Scholar
  19. 19.
    Petkova D, Croft WB (2007) Proximity-based document representation for named entity retrieval. In: Proceeding of the 16th ACM CIKMGoogle Scholar
  20. 20.
    Yu PS, Li X, Liu B (2005) Adding the temporal dimension to search—A case study in publication search. In: Proceeding of the International Conference on Web IntelligenceGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Bundit Manaskasemsak
    • 1
  • Pramote Teerasetmanakul
    • 1
  • Kankamol Tongtip
    • 2
  • Athasit Surarerks
    • 2
  • Arnon Rungsawang
    • 1
  1. 1.Massive Information and Knowledge Engineering Laboratory, Department of Computer EngineeringKasetsart UniversityBangkokThailand
  2. 2.Engineering Laboratory in Theoretical Enumerable System,Department of Computer EngineeringChulalongkorn UniversityBangkokThailand

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