Entity Recommendations in Web Search

While some web search users know exactly what they are looking for, others are willing to explore topics related to an initial interest. Often, the user's initial interest can be uniquely linked to an entity in a knowledge base. In this case, it is natural to recommend the explicitly linked entities for further exploration. In real world knowledge bases, however, the number of linked entities may be very large and not all related entities may be equally relevant. Thus, there is a need for ranking related entities. In this paper, we describe Spark, a recommendation engine that links a user's initial query to an entity within a knowledge base and provides a ranking of the related entities. Spark extracts several signals from a variety of data sources, including Yahoo! Web Search, Twitter, and Flickr, using a large cluster of computers running Hadoop. These signals are combined with a machine learned ranking model in order to produce a final recommendation of entities to user queries. This system is currently powering Yahoo! Web Search result pages.

[1]  Abraham Bernstein,et al.  The Semantic Web - ISWC 2009, 8th International Semantic Web Conference, ISWC 2009, Chantilly, VA, USA, October 25-29, 2009. Proceedings , 2009, SEMWEB.

[2]  Lora Aroyo,et al.  The Semantic Web - ISWC 2011 - 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part I , 2011, SEMWEB.

[3]  Roi Blanco,et al.  Effective and Efficient Entity Search in RDF Data , 2011, SEMWEB.

[4]  Hongyuan Zha,et al.  A General Boosting Method and its Application to Learning Ranking Functions for Web Search , 2007, NIPS.

[5]  Ian Horrocks,et al.  The Semantic Web – ISWC 2010: 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I , 2010, SEMWEB.

[6]  Benjamin Rey,et al.  Generating query substitutions , 2006, WWW '06.

[7]  Milan Stankovic,et al.  Linked Data-Based Concept Recommendation: Comparison of Different Methods in Open Innovation Scenario , 2012, ESWC.

[8]  Roelof van Zwol,et al.  Ranking Entity Facets Based on User Click Feedback , 2010, 2010 IEEE Fourth International Conference on Semantic Computing.

[9]  Lora Aroyo,et al.  The Semantic Web: Research and Applications , 2009, Lecture Notes in Computer Science.

[10]  Peter Mika,et al.  Ad-hoc object retrieval in the web of data , 2010, WWW '10.

[11]  Yi Chang,et al.  Ranking related entities for web search queries , 2011, WWW.

[12]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[13]  Michael Gamon,et al.  Active objects: actions for entity-centric search , 2012, WWW.

[14]  Marco Brambilla,et al.  A revenue sharing mechanism for federated search and advertising , 2012, WWW.

[15]  Edgar Meij,et al.  Investigating the Semantic Gap through Query Log Analysis , 2009, SEMWEB.

[16]  J. Friedman.  Stochastic gradient boosting , 2002 .

[17]  Jaana Kekäläinen,et al.  IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.

[18]  Alexandre Passant,et al.  dbrec - Music Recommendations Using DBpedia , 2010, SEMWEB.