ABSTRACT
Recommendation engines today suggest one product to another, e.g., an accessory to a product. However, intent to buy often precedes a user's appearance in a commerce vertical: someone interested in buying a skateboard may have earlier searched for {varial heelflip}, a trick performed on a skateboard. This paper considers how a search engine can provide early warning of commercial intent. The naive algorithm of counting how often an interest precedes a commercial query is not sufficient due to the number of related ways of expressing an interest. Thus, methods are needed for finding sets of queries where all pairs are related, what we call a query community, and this is the technical contribution of the paper. We describe a random model by which we obtain relationships between search queries and then prove general conditions under which we can reconstruct query communities. We propose two complementary approaches for inferring recommendations that utilize query communities in order to magnify the recommendation signal beyond what an individual query can provide. An extensive series of experiments on real search logs shows that the query communities found by our algorithm are more interesting and unexpected than a baseline of clustering the query-click graph. Also, whereas existing query suggestion algorithms are not designed for making commercial recommendations, we show that our algorithms do succeed in forecasting commercial intent. Query communities increase both the quantity and quality of recommendations.
- D. Achlioptas and F. McSherry. Fast computation of low rank matrix approximations. In STOC, pages 611--618, 2001. Google ScholarDigital Library
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB'94, Proceedings of 20th International Conference on Very Large Data Bases, pages 487--499, 1994. Google ScholarDigital Library
- R. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In KDD, pages 95--104, 2007. Google ScholarDigital Library
- P. Boldi, F. Bonchi, C. Castillo, D. Donato, A. Gionis, and S. Vigna. The query-flow graph: model and applications. In CIKM, pages 609--618, 2008. Google ScholarDigital Library
- H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li. Context-aware query suggestion by mining click-through and session data. In KDD, pages 875--883, 2008. Google ScholarDigital Library
- N. Craswell and M. Szummer. Random walks on the click graph. In SIGIR, pages 239--246, 2007. Google ScholarDigital Library
- A. Freund, D. Pelleg, and Y. Richter. Clustering from constraint graphs. In SIAM International Conference on Data Mining, 2008.Google ScholarCross Ref
- R. Frey, A. McNeil, and M. Nyfeler. Copulas and credit models. Risk, 14(10):111--114, 2001.Google Scholar
- A. Fuxman, A. Kannan, A. Goldberg, R. Agrawal, P. Tsaparas, and J. Shafer. Improving classification accuracy using automatically extracted training data. In KDD, pages 1145--1154, 2009. Google ScholarDigital Library
- A. Fuxman, P. Tsaparas, K. Achan, and R. Agrawal. Using the wisdom of the crowds for keyword generation. In WWW, pages 61--70, 2008. Google ScholarDigital Library
- S. Gupta, M. Bilenko, and M. Richardson. Catching the drift: Learning broad matches from clickthrough data. In KDD, 2009. Google ScholarDigital Library
- T. Hofmann and J. Puzicha. Latent class models for collaborative filtering. In IJCAI, pages 688--693, 1999. Google ScholarDigital Library
- T. Joachims, L. Granka, B. Pan, H. Hembrooke, F. Radlinski, and G. Gay. Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Trans. Inf. Syst., 25(2), 2007. Google ScholarDigital Library
- D. S. Johnson, C. H. Papadimitriou, and M. Yannakakis. On generating all maximal independent sets. Information Processing Letters, 27(3):119--123, 1988. Google ScholarDigital Library
- J. Kleinberg and M. Sandler. Using mixture models for collaborative filtering. In STOC, pages 569--578, 2004. Google ScholarDigital Library
- A. Korolova, K. Kenthapadi, N. Mishra, and A. Ntoulas. Releasing search queries and clicks privately. In WWW, pages 171--180, 2009. Google ScholarDigital Library
- G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76--80, 2003. Google ScholarDigital Library
- F. McSherry and I. Mironov. Differentially private recommender systems: building privacy into the net. In KDD, pages 627--636, 2009. Google ScholarDigital Library
- Q. Mei, D. Zhou, and K. Church. Query suggestion using hitting time. In CIKM, pages 469--478, 2008. Google ScholarDigital Library
- N. Mishra, R. Schreiber, I. Stanton, and R. Tarjan. Finding strongly knit clusters in social networks. Internet Mathematics, 5(1):155--174, 2009.Google Scholar
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In CSCW, pages 175--186, 1994. Google ScholarDigital Library
- M. Richardson. Learning about the world through long-term query logs. TWEB, 2(4):1--27, 2008. Google ScholarDigital Library
- D. Ron, Y. Singer, and N. Tishby. The power of amnesia: Learning probabilistic automata with variable memory length. Machine Learning, 25(2--3):117--149, 1996. Google ScholarDigital Library
- D. Ron, Y. Singer, and N. Tishby. On the learnability and usage of acyclic probabilistic finite automata. J. Comput. Syst. Sci., 56(2):133--152, 1998. Google ScholarDigital Library
- E. Sadikov, J. Madhavan, L. Wang, and A. Halevy. Clustering query refinements by user intent. In WWW, pages 841--850, 2010. Google ScholarDigital Library
- M. Sahami and T. Heilman. A web-based kernel function for measuring the similarity of short text snippets. In WWW, pages 377--386, 2006. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In WWW, pages 285--295, 2001. Google ScholarDigital Library
- D. Stern, R. Herbrich, and T. Graepel. Matchbox: large scale online bayesian recommendations. In WWW, pages 111--120, 2009. Google ScholarDigital Library
- S. Tsukiyama, M. Ide, H. Ariyoshi, and I. Shirakawa. A new algorithm for generating all the maximal independent sets. SIAM J. Comput, 6(3):505--517, 1977.Google ScholarDigital Library
- J. Yi and F. Maghoul. Query clustering using click-through graph. In WWW, pages 1055--1056, 2009. Google ScholarDigital Library
- J. Zheng, X. Wu, J. Niu, and A. Bolivar. Substitutes or complements: another step forward in recommendations. In EC, pages 139--146, 2009. Google ScholarDigital Library
Index Terms
- Shopping for products you don't know you need
Recommendations
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