Cognitive Temporal Document Priors

  • Maria-Hendrike Peetz
  • Maarten de Rijke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7814)

Abstract

Temporal information retrieval exploits temporal features of document collections and queries. Temporal document priors are used to adjust the score of a document based on its publication time. We consider a class of temporal document priors that is inspired by retention functions considered in cognitive psychology that are used to model the decay of memory. Many such functions used as a temporal document prior have a positive effect on overall retrieval performance. We examine the stability of this effect across news and microblog collections and discover interesting differences between retention functions. We also study the problem of optimizing parameters of the retention functions as temporal document priors; some retention functions display consistent good performance across large regions of the parameter space. A retention function based on a Weibull distribution is the preferred choice for a temporal document prior.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Porter, S., Birt, A.R.: Is traumatic memory special? Appl. Cogn. Psych. 15, 101–117 (2001)CrossRefGoogle Scholar
  2. 2.
    Meeter, M., Murre, J.M.J., Janssen, S.M.J.: Remembering the news: modeling retention data from a study with 14,000 participants. Memory & Cognition 33, 793–810 (2005)CrossRefGoogle Scholar
  3. 3.
    Hertwig, R., et al.: Fluency heuristic: a model of how the mind exploits a by-product of information retrieval. J. Exp. Psych.: Learning, Memory, and Cogn. 34, 1191–1206 (2008)CrossRefGoogle Scholar
  4. 4.
    Chessa, A.G., Murre, J.M.: A memory model for internet hits after media exposure. Physica A Statistical Mechanics and its Applications (2004)Google Scholar
  5. 5.
    Chessa, A.G., Murre, J.M.: Modelling memory processes and internet response times: Weibull or power-law? Physica A Statistical Mechanics and its Applications (2006)Google Scholar
  6. 6.
    Li, X., Croft, W.B.: Time-Based Language Models. In: CIKM 2003 (2003)Google Scholar
  7. 7.
    Massoudi, K., Tsagkias, M., de Rijke, M., Weerkamp, W.: Incorporating Query Expansion and Quality Indicators in Searching Microblog Posts. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 362–367. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Efron, M., Organisciak, P., Fenlon, K.: Improving retrieval of short texts through document expansion. In: SIGIR 2012 (2012)Google Scholar
  9. 9.
    Efron, M., Golovchinsky, G.: Estimation Methods for Ranking Recent Information. In: SIGIR 2011 (2011)Google Scholar
  10. 10.
    Ebbinghaus, H.: Memory: a contribution to experimental psychology. Teachers College, Columbia University (1913)Google Scholar
  11. 11.
    Schooler, L.J., Anderson, J.R.: The role of process in the rational analysis of memory. Cognitive Psychology 32, 219–250 (1997)CrossRefGoogle Scholar
  12. 12.
    Rubin, D.C., Hinton, S., Wenzel, A.: The precise time course of retention. Journal of Experimental Psychology: Learning, Memory, and Cognition 25, 1161–1176 (1999)CrossRefGoogle Scholar
  13. 13.
    Wickens, T.D.: Measuring the time course of retention. On human memory: Evolution, progress, and reflections on the 30th anniversary of the Atkinson–Shiffrin model (1999)Google Scholar
  14. 14.
    Heathcote, A., Brown, S., Mewhort, D.J.: The power law repealed: the case for an exponential law of practice. Psychonomic Bulletin & Review 7, 185–207 (2000)CrossRefGoogle Scholar
  15. 15.
    Alonso, O., Strötgen, J., Baeza-Yates, R., Gertz, M.: Temporal Information Retrieval: Challenges and Opportunities. In: TWAW 2011, pp. 1–8 (2011)Google Scholar
  16. 16.
    Verhagen, M., Pustejovsky, J.: Temporal processing with the TARSQI toolkit. In: COLING 2008 (2008)Google Scholar
  17. 17.
    Odijk, D., de Rooij, O., Peetz, M.-H., Pieters, T., de Rijke, M., Snelders, S.: Semantic Document Selection. In: Zaphiris, P., Buchanan, G., Rasmussen, E., Loizides, F. (eds.) TPDL 2012. LNCS, vol. 7489, pp. 215–221. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Keikha, M., Gerani, S., Crestani, F.: Time-based relevance models. In: SIGIR 2011 (2011)Google Scholar
  19. 19.
    Amodeo, G., Amati, G., Gambosi, G.: On relevance, time and query expansion. In: CIKM 2011. ACM (2011)Google Scholar
  20. 20.
    Dakka, W., Gravano, L., Ipeirotis, P.G.: Answering General Time Sensitive Queries. In: CIKM 2008, pp. 1437–1438 (2008)Google Scholar
  21. 21.
    Peetz, M.-H., Meij, E., de Rijke, M., Weerkamp, W.: Adaptive Temporal Query Modeling. In: Baeza-Yates, R., de Vries, A.P., Zaragoza, H., Cambazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 455–458. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  22. 22.
    Efron, M.: Query-specific recency ranking. In: SIGIR 2012 Workshop on Time-aware Information Access (2012)Google Scholar
  23. 23.
    Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: SIGIR 1998 (1998)Google Scholar
  24. 24.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)MATHCrossRefGoogle Scholar
  25. 25.
    Ainslie, G., Haslam, N.: Hyperbolic discounting. In: Choice over time. Russell Sage Foundation (1992)Google Scholar
  26. 26.
    Amati, G., et al.: FUB, IASI-CNR, UNIVAQ at TREC 2011. In: TREC 2011, NIST (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Maria-Hendrike Peetz
    • 1
  • Maarten de Rijke
    • 1
  1. 1.ISLAUniversity of AmsterdamThe Netherlands

Personalised recommendations