ABSTRACT
Search plays an important role in online social networks as it provides an essential mechanism for discovering members and content on the network. Related search recommendation is one of several mechanisms used for improving members' search experience in finding relevant results to their queries. This paper describes the design, implementation, and deployment of Metaphor, the related search recommendation system on LinkedIn, a professional social networking site with over 175~million members worldwide. Metaphor builds on a number of signals and filters that capture several dimensions of relatedness across member search activity. The system, which has been in live operation for over a year, has gone through multiple iterations and evaluation cycles. This paper makes three contributions. First, we provide a discussion of a large-scale related search recommendation system. Second, we describe a mechanism for effectively combining several signals in building a unified dataset for related search recommendations. Third, we introduce a query length model for capturing bias in recommendation click behavior. We also discuss some of the practical concerns in deploying related search recommendations.
- Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. TKDE, 17 (6): 734--749, 2005. Google ScholarDigital Library
- James Allan, Ben Carterette, and Joshua Lewis. When will information retrieval be "good enough?". In Proceedings of the SIGIR, 2005. Google ScholarDigital Library
- Avi Arampatzis and Jaap Kamps. A study of query length. In Proceedings of the SIGIR, 2008. Google ScholarDigital Library
- Ricardo Baeza-Yates. Applications of web query mining. In Proceedings of the ECIR, 2005. Google ScholarDigital Library
- Ricardo Baeza-Yates. Graphs from search engine queries. LNCS, 4362: 1--8, 2007. Google ScholarDigital Library
- Ricardo Baeza-Yates and Berthier Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 1999. Google ScholarDigital Library
- Ricardo A. Baeza-Yates, Carlos A. Hurtado, and Marcelo Mendoza. Query recommendation using query logs in search engines. In Proceedings of the EDBT Workshops, 2004. Google ScholarDigital Library
- James Bennett and Stan Lanning. The Netflix prize. In KDD Cup and Workshop, 2007.Google Scholar
- Sumit Bhatia, Debapriyo Majumdar, and Prasenjit Mitra. Query suggestions in the absence of query logs. In Proceedings of the SIGIR, 2011. Google ScholarDigital Library
- Paolo Boldi, Francesco Bonchi, Carlos Castillo, Debora Donato, Aristides Gionis, and Sebastiano Vigna. The query-flow graph: model and applications. In Proceedings of the CIKM, 2008. Google ScholarDigital Library
- Paolo Boldi, Francesco Bonchi, Carlos Castillo, Debora Donato, and Sebastiano Vigna. Query suggestions using query-flow graphs. In Proceedings of the WSDM, 2009. Google ScholarDigital Library
- Leo Breiman. Bagging predictors. Machine Learning, 24 (2): 123--140, 1996. Google ScholarCross Ref
- Peter D. Bruza and Simon Dennis. Query reformulation on the internet: Empirical data and the hyperindex search engine. In Proceedings of the RIAO, 1997.Google Scholar
- Carlos Castillo, Claudio Corsi, Debora Donato, Paolo Ferragina, and Aristides Gionis. Query-log mining for detecting spam. In Proceedings of the AIRWeb, 2008. Google ScholarDigital Library
- Paul A. Chirita, Claudiu S. Firan, and Wolfgang Nejdl. Personalized query expansion for the web. In Proceedings of the SIGIR, 2007. Google ScholarDigital Library
- Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the RecSys, 2010. Google ScholarDigital Library
- Hang Cui, Ji-Rong Wen, Jian-Yun Nie, and Wei-Ying Ma. Query expansion by mining user logs. TKDD, 15 (4): 829--839, 2003. Google ScholarDigital Library
- Jeffrey Dean and Sanjay Ghemawat. MapReduce: simplified data processing on large clusters. In Proceedings of the OSDI, 2004. Google ScholarDigital Library
- Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swaminathan Sivasubramanian, Peter Vosshall, and Werner Vogels. Dynamo: Amazon's highly available key-value store. SIGOPS Oper. Syst. Rev., 41: 205--220, 2007. Google ScholarDigital Library
- Thomas G. Dietterich. Ensemble methods in machine learning. LNCS, 1857: 1--15, 2000. Google ScholarDigital Library
- Gideon Dror, Noam Koenigstein, Yehuda Koren, and Markus Weimer. Recommending music items based on the Yahoo! music dataset. In KDD-Cup, 2011.Google Scholar
- Bruno M. Fonseca, Paulo B. Golgher, Edleno S. de Moura, and Nivio Ziviani. Using association rules to discover search engines related queries. In Proceedings of the LA-WEB, 2003. Google ScholarDigital Library
- Joao Gama and Pavel Brazdil. Cascade generalization. Machine Learning, 41: 315--343, 2000. Google ScholarDigital Library
- Mohammad Al Hasan, Nish Parikh, Byanit Singh, and Neel Sundaresan. Query suggestion for E-commerce sites. In Proceedings of the WSDM, 2011. Google ScholarDigital Library
- Rosie Jones, Benjamin Rey, Omid Madani, and Wiley Greiner. Generating query substitutions. In Proceedings of the WWW, 2006. Google ScholarDigital Library
- Reiner Kraft and Jason Zien. Mining anchor text for query refinement. In Proceedings of the WWW, 2004. Google ScholarDigital Library
- Jay Kreps, Neha Narkhede, and Jun Rao. Kafka: A distributed messaging system for log processing. In Proceedings of the NetDB, 2011.Google Scholar
- Solomon Kullback and Richard A. Leibler. On information and sufficiency. Ann. Math. Statist., 22 (1): 79--86, 1951.Google ScholarCross Ref
- Qiaozhu Mei, Dengyong Zhou, and Kenneth Church. Query suggestion using hitting time. In Proceedings of the CIKM, 2008. Google ScholarDigital Library
- Christopher Olston, Benjamin Reed, Utkarsh Srivastava, Ravi Kumar, and Andrew Tomkins. Pig Latin: a not-so-foreign language for data processing. In Proceedings of the SIGMOD, 2008. Google ScholarDigital Library
- Stephen Robertson. Understanding inverse document frequency: On theoretical arguments for IDF. Journal of Documentation, 60 (5), 2004.Google ScholarCross Ref
- Robert E. Schapire. A brief introduction to boosting. In Proceedings of the IJCAI, 1999. Google ScholarDigital Library
- Joseph Sill, Gábor Takács, Lester Mackey, and David Lin. Feature-weighted linear stacking. CoRR, abs/0911.0460, 2009.Google Scholar
- Yang Song, Dengyong Zhou, and Li-wei He. Query suggestion by constructing term-transition graphs. In Proceedings of the WSDM, 2012. Google ScholarDigital Library
- Amanda Spink, Dietmar Wolfram, Major B. J. Jansen, and Tefko Saracevic. Searching the web: The public and their queries. Journal of American Society for Information Science and Technology, 2001. Google ScholarDigital Library
- Xiaofei Su and Taghi M. Khoshgoftaar. A survey of collaborative filtering techniques. Advances in AI, 2009: 4:1--4:19, 2009. Google ScholarDigital Library
- Roshan Sumbaly, Jay Kreps, Lei Gao, Alex Feinberg, Chinmay Soman, and Sam Shah. Serving Large-scale Batch Computed Data with Project Voldemort. In Proceedings of the FAST, 2012. Google ScholarDigital Library
- Ellen M. Voorhees. Query expansion using lexical-semantic relations. In Proceedings of the SIGIR, 1994. Google ScholarDigital Library
- David H. Wolpert. Stacked generalization. Neural Networks, 5: 241--259, 1992. Google ScholarDigital Library
- Jinxi Xu and W. Bruce Croft. Query expansion using local and global document analysis. In Proceedings of the SIGIR, 1996. Google ScholarDigital Library
- Zhiyong Zhang and Olfa Nasraoui. Mining search engine query logs for query recommendation. In Proceedings of the WWW, 2006. Google ScholarDigital Library
Index Terms
Metaphor: a system for related search recommendations
Recommendations
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