PERSONALIZING SEARCH USING SOCIALLY ENHANCED INTEREST MODEL, BUILT FROM THE STREAM OF USER’S ACTIVITY
Keywords:
search personalization, implicit feedback, social networks, query expansion, metadata, user activity, personalized proxyAbstract
write short queries, unconsciously trying to minimize the cognitive load. However, as these short queries are very ambiguous, search engines tend to find the most popular meaning – someone who does not know anything about cascading stylesheets might search for a music band called css and be very surprised about the results. In this paper we propose a method which can infer additional keywords for a search query by leveraging a social network context and a method to build this network from the stream of user’s activity on the Web. The approach was evaluated on real users using a personalized proxy server platform. The query expansion method was integrated into Google search engine and where possible, the original query was expanded and additional search results were retrieved and displayed. 70% of the expanded results were clicked and we observed a significant increase of time that the users spent on the expanded results when compared to the time spent on standard results.
Downloads
References
G. Marchionini. Interfaces for end-user information seeking. J. of the American Society for
Information Science, 43:156–163, 1992.
B. J. Jansen, A. Spink, and T. Saracevic. Real life, real users, and real needs: a study and analysis
of user queries on the web. Inf. Process. Management, 36(2):207–227, January 2000.
J. Pitkow, H. Schütze, T. Cass, R. Cooley, D. Turnbull, A. Edmonds, E. Adar, and T. Breuel.
Personalized search. Commun. ACM, 45:50–55, September 2002.
T. Kramár, M. Barla, and M. Bieliková. Peweproxy: A platform for ubiquitous personalization of
the “wild” web. In UMAP ’11: Adjunct Proc. of the 19th Int. Conf. on User Modeling, Adaptation,
and Personalization, pages 7–9, Girona, Spain, 2011.
M. Holub and M. Bieliková. An inquiry into the utilization of behavior of users in personalized
web. J. of Universal Computer Science, 17:1830–1853, 2011.
Návrat P., T. Taraba, A. Bou Ezzedine, and D. Chudá. Context search enhanced by readability
index. In IFIP Series. Vol. 276: Artificial Intelligence in Theory and Practice II, IFIP, pages
–382. Springer-Verlag, 2008.
J. Teevan, S. T. Dumais, and E. Horvitz. Characterizing the value of personalizing search. In
Proc. of the 30th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval,
SIGIR ’07, pages 757–758, New York, NY, USA, 2007. ACM.
E. Agichtein and Guo Q. Towards inferring web searcher intent from behavior data. In Proc. of
the 28th Int. Conf. on Human Factors in Computing Systems, CHI ’10, Atlanta, GA, USA, 2010.
ACM.
S. Lawrence. Context in web search. IEEE Data Engineering Bulletin, 23(3):25–32, 2000.
Taher H. Haveliwala. Topic-sensitive pagerank. In WWW ’02: Proc. of the 11th Int. Conf. on
World Wide Web, pages 517–526, New York, NY, USA, 2002. ACM.
F. Qiu and J. Cho. Automatic identification of user interest for personalized search. In WWW
’06: Proc. of the 15th Int. Conf. on World Wide Web, pages 727–736, New York, NY, USA, 2006.
ACM.
N. Kanhabua and K. Nørvåg. Quest: query expansion using synonyms over time. In Proc. of the
European conference on Machine learning and knowledge discovery in databases: Part III,
ECML PKDD’10, pages 595–598, Berlin, Heidelberg, 2010. Springer-Verlag.
J. Gao, X. Li, D. Micol, C. Quirk, and X. Sun. A large scale ranker-based system for search query
spelling correction. In Proc. of the 23rd Int. Conf. on Computational Linguistics, COLING ’10,
pages 358–366, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics.
A. Anagnostopoulos, L. Becchetti, C. Castillo, and A. Gionis. An optimization framework for
query recommendation. In Proc. of the third ACM Int. Conf. on Web Search and Data Mining,
WSDM ’10, pages 161–170, New York, NY, USA, 2010. ACM.
C. Biancalana, A. Micarelli, and C. Squarcella. Nereau: a social approach to query expansion. In
Proc. of the 10th ACM workshop on Web information and data management, WIDM ’08, pages
–102, New York, NY, USA, 2008. ACM.
D. Carmel, E. Farchi, Y. Petruschka, and A. Soffer. Automatic query refinement using lexical
affinities with maximal information gain. In Proc. of the 25th Int. ACM SIGIR Conf. on Research
and Development in Information Retrieval, SIGIR ’02, pages 283–290, New York, NY, USA, 2002.
ACM.
S. Liu, F. Liu, C. Yu, and W. Meng. An effective approach to document retrieval via utilizing
wordnet and recognizing phrases. In Proc. of the 27th Int. ACM SIGIR Conf. on Research and
Development in Information Retrieval, SIGIR ’04, pages 266–272, New York, NY, USA, 2004.
ACM.
P. A. Chirita, C. S. Firan, and W. Nejdl. Personalized query expansion for the web. In Proc. of
the 30th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, SIGIR
’07, pages 7–14, New York, NY, USA, 2007. ACM.
C. D. Manning, P. Raghavan, and H. Schtze. Introduction to Information Retrieval. Cambridge
University Press, New York, NY, USA, 2008.
R. Wetzker, C. Zimmermann, C. Bauckhage, and S. Albayrak. I tag, you tag: translating tags
for advanced user models. In Proc. of the third ACM int. conf. on Web search and data mining,
WSDM ’10, pages 71–80, New York, NY, USA, 2010. ACM.
F. Carmagnola, F. Cena, O. Cortassa, C. Gena, and I. Torre. Towards a tag-based user model:
How can user model benefit from tags? In Proc. of the 11th int. conf. on User Modeling, UM ’07,
pages 445–449, Berlin, Heidelberg, 2007. Springer-Verlag.
M. Shepherd, C. Watters, J. Duffy, and R. Kaushik. Browsing and keyword-based profiles: A
cautionary tale. In Proc. of the 34th Annual Hawaii Int. Conf. on System Sciences, HICSS ’01,
pages 4011–, Washington, DC, USA, 2001. IEEE Computer Society.
M. Claypool, P. Le, M. Wased, and D. Brown. Implicit interest indicators. In Proc. of the 6th
international conference on Intelligent user interfaces, IUI ’01, pages 33–40, New York, NY, USA,
ACM.
B. R. Barricelli, M. Padula, and P. L. Scala. Personalized web browsing experience. In Proc. of
the 20th ACM conf. on Hypertext and hypermedia, HT ’09, pages 345–346, New York, NY, USA,
ACM.
Y. Yesilada, S. Bechhofer, and B. Horan. COHSE: Dynamic linking of web resources. Technical
report, Sun Microsystems, Inc., Mountain View, CA, USA, 2007.
J. Teevan, S. T. Dumais, and E. Horvitz. Personalizing search via automated analysis of interests
and activities. In Proc. of the 28th Int. ACM SIGIR Conf. on Research and Development in
Information Retrieval, SIGIR ’05, pages 449–456, New York, NY, USA, 2005. ACM.
M. Barla. Towards social-based user modeling and personalization. Information Sciences and
Technologies Bulletin of the ACM Slovakia, 3:52–60, 2011.
J. Teevan, M. R. Morris, and S. Bush. Discovering and using groups to improve personalized
search. In Proc. of the Second ACM Int. Conf. on Web Search and Data Mining, WSDM ’09,
pages 15–24, New York, NY, USA, 2009. ACM.
P. Bhattacharyya, A. Garg, and S. F. Wu. Social network model based on keyword categorization.
In Proc. of the 2009 Int. Conf. on Advances in Social Network Analysis and Mining, ASONAM
’09, pages 170–175, Washington, DC, USA, 2009. IEEE Computer Society.
R. Schifanella, A. Barrat, C. Cattuto, B. Markines, and F. Menczer. Folks in folksonomies: social
link prediction from shared metadata. In Proc. of the third ACM int. conf. on Web search and
data mining, WSDM ’10, pages 271–280, New York, NY, USA, 2010. ACM.
M. R. Morris and E. Horvitz. Searchtogether: an interface for collaborative web search. In Proc.
of the 20th annual ACM symposium on User interface software and technology, UIST ’07, pages
–12, New York, NY, USA, 2007. ACM.
K. McNally, M. P. O’Mahony, M. Coyle, P. Briggs, and B. Smyth. A case study of collaboration
and reputation in social web search. ACM Trans. Intell. Syst. Technol., 3(1):4:1–4:29, 2011.
T. Kramár, M. Barla, and M. Bieliková. Disambiguating search by leveraging the social network
context based on the stream of user’s activity. In UMAP ’10: Proc. of the 18th Int. Conf. on User
Modeling, Adaptation, and Personalization, pages 387–392, Hawaii, USA, 2010. Springer-Verlag.
E. Wilde and A. Roy. Web site metadata. J. of Web Engineering, 9(4):283–301, December 2010.
A. Lancichinetti, S. Fortunato, and J. Kertesz. Detecting the overlapping and hierarchical community
structure of complex networks. New J. of Physics, March 2009.
D. Gayo-Avello. A survey on session detection methods in query logs and a proposal for future
evaluation. Inf. Sci., 179:1822–1843, May 2009.
R. W. White, P. N. Bennett, and S. T. Dumais. Predicting short-term interests using activity-based
search context. In Proc. of the 19th ACM Int. Conf. on Information and Knowledge Management,
CIKM ’10, pages 1009–1018, New York, NY, USA, 2010. ACM.
J. Zhang and A. A. Ghorbani. Gumsaws: A generic user modeling server for adaptive web systems.
In Proc. of the 5th Annual Conf. on Communication Networks and Services Research, pages 117–
, Washington, DC, USA, 2007. IEEE Computer Society.
M. Trella, C. Carmona, and R. Conejo. MEDEA: an Open Service-Based Learning Platform
for Developing Intelligent Educational Systems for the Web. In P. Brusilovsky, R. Conejo, and
E. Millan, editors,Workshop on Adaptive Systems for Web-Based Education: Tools and Reusability,
pages 27–34, Amsterdam, The Netherlands, 2005.
M. Agosti and G. M. Di Nunzio. Gathering and mining information from web log files. In Proc. of
the 1st international conference on Digital libraries: research and development, DELOS’07, pages
–113, Berlin, Heidelberg, 2007. Springer-Verlag.
A. Tappenden and J. Miller. A survey of cookie technology adoption amongst nations. J. of Web
Engineering, 8(3):211–244, September 2009.
M. Barla and M. Bieliková. Ordinary web pages as a source for metadata acquisition for open
corpus user modeling. In Proc. of IADIS WWW/Internet 2010., pages 227–233. IADIS, 2010.
M. T. Keane, M. O’Brien, and B. Smyth. Are people biased in their use of search engines?
Commun. ACM, 51(2):49–52, February 2008.
M. Barla and M. Bieliková. On deriving tagsonomies: Keyword relations coming from crowd.
In Ngoc Nguyen, Ryszard Kowalczyk, and Shyi-Ming Chen, editors, Computational Collective
Intelligence. Semantic Web, Social Networks and Multiagent Systems, volume 5796 of Lecture
Notes in Computer Science, pages 309–320. Springer-Verlag, 2009.
