Abstract
In query based Web search, a significant percentage of user queries are underspecified, most likely by naive users. Collaborative ranking helps the naive user by exploiting the collective expertise. We present a novel algorithmic model inspired by the network flow theory, which constructs a search network based on search engine logs to describe the relationship between the relevant entities in search: queries, documents, and users. This formal model permits the theoretical investigation of the nature of collaborative ranking in more concrete terms, and the learning of the dependence relations among the different entities. FlowRank, an algorithm derived from this model through an analysis of empirical usage patterns, is implemented and evaluated. We empirically show its potential in experiments involving real-world user relevance ratings and a random sample of 1,334 documents and 100 queries from a popular document search engine. Definite improvements over two baseline ranking algorithms for approximately 47% of the queries are reported.
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Bharat, K., Broder, A., Henzinger, M., Kumar, P., Venkatasubramanian, S.: The Connectivity Server: Fast Access to Linkage Information on the Web. In: Proc. of the 7th International World Wide Web Conference, pp. 469–477 (1998)
Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., Wiener, J.: Graph structure in the web. In: Proc. of the 9th International World Wide Web Conference, pp. 309–320 (2000)
Carriere, J., Kazman, R.: WebQuery: Searching and visualizing the Web through connectivity. In: Proc. of the 6th International World Wide Web Conference (1997)
Kleinberg, J.: Authoritative sources in a hyperlinked environment. J. of the ACM 46(5), 604–632 (1999)
Brin, S., Page, L.: The anatomy of a large scale hypertextual web search engine. In: Proc. of the 7th International World Wide Web Conference, pp. 107–117 (1998)
Chakrabati, S., Dom, B., Gibson, D., Kleinberg, J., Raghavan, P., Rajagonpalan, S.: Automatic Resource List Compilation by Analyzing Hyperlink Structure and Associated Text. In: Proc. of the 7th International World Wide Web Conference (1998)
Tomita, J., Kikui, G.: Interactive Web Search by Graphical Query Refinement. In: Proc. the 10th International World Wide Web Conference (2002)
Flake, G.W., Lawrence, S., Giles, C.L.: Efficient identification of web communities. In: Proc. of the 6th International Conference on Knowledge Discovery and Data Mining, pp. 150–160 (2000)
Flake, G.W., Tsioutsiouliklis, K., Zhukov, L.: Methods for Mining Web Communities: Bibliometric, Spectral, and Flow. In: Web Dynamics. Springer, Heidelberg (2003)
Menache, I., Mannor, S., Shimkin, N.: Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 295–306. Springer, Heidelberg (2002)
Chitrapura, K.P., Kashyap, S.R.: Node Ranking in Labeled Directed Graphs. In: Proc. of ACM Conference on Information and Knowledge Management, pp. 597–606 (2004)
Chidlovskii, B., Glance, N.S., Grasso, M.A.: Collaborative Re-Ranking of Search Results. In: The National Conference on Artificial Intelligence 2000 Workshop on AI for Web Search, pp. 18–23 (2000)
Zaiane, O.R., Strilets, A.: Finding Similar Queries to Satisfy Searches based on Query Traces. In: Proc. of the International Workshop on Efficient Web-Based Information Systems, pp. 207–216 (2002)
Joachims, T., Granka, L., Pan, B., Hembrooke, H., Gay, G.: Accurately Interpreting Clickthrough Data as Implicit Feedback. In: Proc. of Annual ACM Conference on Research and Development in Information Retrieval (SIGIR) 2005, pp. 154–161 (2005)
Daume, H., Brill, E.: Web search intent induction via automatic query reformulation. In: Human Language Technology Conference / North American Chapter of the Association for Computational Linguistics (2004)
Wen, J., Nie, J., Zhang, H.: Clustering user queries of a search engine. In: Proc. of the 10th International World Wide Web Conference, pp. 162–168 (2001)
Freyne, J., Smyth, B., Coyle, M., Balfe, E., Briggs, P.: Further Experiments on Collaborative Ranking in Community-Based Web Search. Artificial Intelligence Review 21(3-4), 229–252 (2004)
Smyth, B., Balfe, E., Boydell, O., Bradley, K., Briggs, P., Coyle, M., Freyne, J.: A Live-User Evaluation of Collaborative Web Search. In: Proc. of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005) (2005)
Vakkari, P.: Subject knowledge, source of terms and term selection in query expansion. In: Crestani, F., Girolami, M., van Rijsbergen, C.J.K. (eds.) ECIR 2002. LNCS, vol. 2291, p. 110. Springer, Heidelberg (2002)
Ford Jr., L.R., Fulkerson, D.R.: Maximal flow through a network. Canadian J. of Mathematics 8, 399–404 (1956)
Savoy, J., Vrajitoru, D.: Evaluation of learning schemes used in information retrieval. Technical Report CR-I-95-02, Faculty of Sciences, University of Neuchatel (1996)
Risvik, K.M., Mikolajewski, T., Boros, P.: Query Segmentation for Web Search. In: Proc. of the 11th International World Wide Web Conference, May 20-24 (2003)
Goldberg, A.V., Tarjan, R.E.: A new approach to the maximum-flow problem. J. of the ACM 35(4), 921–940 (1998)
Beeferman, D., Berger, A.: Agglomerative clustering of a search engine query log. In: Proc. of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 407–416 (2000)
Cheung, K., Tian, L.: Learning User Similarity and Rating Style for Collaborative Recommendation. Information Retrieval 7(3-4), 395–410 (2004)
Jarvelin, K., Kekalainen, J.: IR Evaluation Methods for Retrieving Highly Relevant Documents. In: Proc. of the 23rd Annual ACM Conference on Research and Development in Information Retrieval (SIGIR), pp. 41–48 (2000)
Chien, S., Immorlica, N.: Semantic Similarity Between Search Engine Queries Using Temporal Correlation. In: Proc. of the 14th International Conference on World Wide Web, pp. 2–11 (2005)
Jarvelin, K., Kekalainen, J.: Cummulated Gain-based Evaluation of IR Techniques. ACM Transactions on Information Systems (TOIS) 20(4), 422–446 (2002)
Edmonds, J., Kapr, R.M.: Theoretical improvements in the algorithmic efficiency for network flow problems. J. of ACM 19, 248–264 (1972)
Chartrand, G.: Cut-Vertices and Bridges. In: Introductory Graph Theory, pp. 45–49. Dover, New York (1985)
Jansen, J., Spink, A.: An Analysis of Web Documents Retrieved and Viewed. In: Proc. of the 4th International Conference on Internet Computing (2003)
Flake, G.W., Tarjan, R.E., Tsioutsiouliklis, K.: Graph Clustering and Minimum Cut Trees. J. of Internet Mathematics 1(4), 385–408 (2004)
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Zhuang, Z., Cucerzan, S., Giles, C.L. (2006). Network Flow for Collaborative Ranking. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Knowledge Discovery in Databases: PKDD 2006. PKDD 2006. Lecture Notes in Computer Science(), vol 4213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871637_41
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DOI: https://doi.org/10.1007/11871637_41
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