Explaining Query Modifications
An Alternative Interpretation of Term Addition and Removal
Conference paper
Abstract
In the course of a search session, searchers often modify their queries several times. In most previous work analyzing search logs, the addition of terms to a query is identified with query specification and the removal of terms with query generalization. By analyzing the result sets that motivated searchers to make modifications, we show that this interpretation is not always correct. In fact, our experiments indicate that in the majority of cases the modifications have the opposite functions. Terms are often removed to get rid of irrelevant results matching only part of the query and thus to make the result set more specific. Similarly, terms are often added to retrieve more diverse results. We propose an alternative interpretation of term additions and removals and show that it explains the deviant modification behavior that was observed.
Keywords
Query Term Original Query Result List Search Session Coherence Score
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
- 1.Boldi, P., Bonchi, F., Castillo, C., Vigna, S.: Query reformulation mining: models, patterns, and applications. Information Retrieval 14(3), 257–289 (2010)CrossRefGoogle Scholar
- 2.Bozzon, A., Chirita, P.A., Firan, C.S., Nejdl, W.: Lexical analysis for modeling web query reformulation. In: SIGIR 2007, pp. 739–740 (2007)Google Scholar
- 3.Bruza, P., Dennis, S.: Query reformulation on the internet: empirical data and the hyperindex search engine. In: RIAO 1997, pp. 488–499 (1997)Google Scholar
- 4.Costa, R.P., Seco, N.: Hyponymy Extraction and Web Search Behavior Analysis Based on Query Reformulation. In: Geffner, H., Prada, R., Machado Alexandre, I., David, N. (eds.) IBERAMIA 2008. LNCS (LNAI), vol. 5290, pp. 332–341. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 5.Cronen-Townsend, S., Croft, W.B.: Quantifying query ambiguity. In: HLT 2002, pp. 104–109 (2002)Google Scholar
- 6.Gonzalo, J., Peinado, V., Clough, P., Karlgren, J.: Overview of iCLEF 2009: exploring search behaviour in a multilingual folksonomy environment. In: CLEF 2009, pp. 13–20 (2010)Google Scholar
- 7.He, D., Göker, A., Harper, D.J.: Combining evidence for automatic web session identification. Information Processing and Management 38(5), 727–742 (2002)MATHCrossRefGoogle Scholar
- 8.He, J., Larson, M., de Rijke, M.: Using Coherence-Based Measures to Predict Query Difficulty. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 689–694. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 9.Hiemstra, D.: Term-specific smoothing for the language modeling approach to information retrieval: the importance of a query term. In: SIGIR 2002, pp. 35–41 (2002)Google Scholar
- 10.Hollink, V., Tsikrika, T., De Vries, A.P.: Semantic search log analysis: a method and a study on professional image search. JASIST 62(4), 691–713 (2011)CrossRefGoogle Scholar
- 11.Huang, J., Efthimiadis, E.N.: Analyzing and evaluating query reformulation strategies in web search logs. In: CIKM 2009, pp. 77–86 (2009)Google Scholar
- 12.Jansen, B.J., Booth, D.L., Spink, A.: Patterns of query reformulation during web searching. JASIST 60(7), 1358–1371 (2009)CrossRefGoogle Scholar
- 13.Jansen, B.J., Spink, A., Pedersen, J.O.: An analysis of multimedia searching on AltaVista. In: MIR 2003, pp. 186–192 (2003)Google Scholar
- 14.Jones, R., Fain, D.C.: Query word deletion prediction. In: SIGIR 2003, pp. 435–436 (2003)Google Scholar
- 15.Jörgensen, C., Jörgensen, P.: Image querying by image professionals. JASIST 56(12), 1346–1359 (2005)CrossRefGoogle Scholar
- 16.Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)MathSciNetMATHCrossRefGoogle Scholar
- 17.Özmutlu, H.C.: Markovian analysis for automatic new topic identification in search engine transaction logs. Applied Stochastic Models in Business and Industry 25(6), 737–768 (2009)MathSciNetCrossRefGoogle Scholar
- 18.Peinado, V., Gonzalo, J., Artiles, J., López-Ostenero, F.: Log Analysis of Multilingual Image Searches in Flickr. In: Peters, C., Deselaers, T., Ferro, N., Gonzalo, J., Jones, G.J.F., Kurimo, M., Mandl, T., Peñas, A., Petras, V. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 236–242. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 19.Rieh, S.Y., Xie, H.: Analysis of multiple query reformulations on the web: the interactive information retrieval context. Information Processing and Management 42(3), 751–768 (2006)CrossRefGoogle Scholar
- 20.Rudinac, S., Larson, M., Hanjalic, A.: Exploiting Result Consistency to Select Query Expansions for Spoken Content Retrieval. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 645–648. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 21.Whittle, M., Eaglestone, B., Ford, N., Gillet, V.J., Madden, A.: Data mining of search engine logs. JASIST 58(14), 2382–2400 (2007)CrossRefGoogle Scholar
- 22.Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E., Li, H.: Context-aware ranking in web search. In: SIGIR 2010, pp. 451–458 (2010)Google Scholar
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