Abstract
Over the last few years, Web has changed significantly. Emergence of social networksSocial network and Web 2.0 have enabled people to interact with Web document in new ways not possible before. In this paper, we present PERSOSE Personalized search engine (PERSOSE) a new search engineSearch engine that personalizes the search results based on users’ social actions.Social actions Although the users’ social actions may sometimes seem irrelevant to the search, we show that they are actually useful for personalization.Personalization We propose a new relevance modelRelevance model called persocial relevance model utilizing three levels of social signals to improve the Web search.Web search We show how each level of persocial model (users’ social actions, friends’ social actions and social expansion) can be built on top of the previous level and how each level improves the search results. Furthermore, we develop several approaches to integrate persocial relevance model into the textual Web search process. We show how PERSOSE Personalized search engine (PERSOSE) can run effectively on 14 million WikipediaWikipedia articles and social data from real FacebookFacebook@Facebook users and generate accurate search results. Using PERSOSE, we performed a set of experiments and showed the superiority of our proposed approaches. We also showed how each level of our model improves the accuracy of search results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
Commercialized and more complicated examples of this measure include Klout (klout.com) and PeerIndex (peerindex.com).
- 4.
To be more precise, set U\('\) of users such that \(\forall u'_l \in U' | W'(u'_l,u_i) > \delta \).
- 5.
Many existing approaches and definitions can be used to measure connections between documents. Here, we do not go into details of such approaches.
- 6.
- 7.
- 8.
HB stands for hybrid.
- 9.
mturk.com.
- 10.
Each volunteer allowed us to read/access his/her Facebook data for this experiment.
- 11.
- 12.
For instance, you may have a lot of mutual friends with your high school classmate, without being close or related to that person. On the other hand, you may not have a lot of mutual friends with your spouse or sister, and still be close to them.
References
Craig AS, Gregory RG (2005) Connections: using context to enhance file search, 20th ACM symposium on operating systems principles. ACM Press, New York, pp 119–132
McDonnell M, Ali S (2011) Social search: a taxonomy of, and a user-centred approach to, social web search. Progr: Electron Libr Inf Syst 45.1, pp 6–28
Evans B et al (2009) Exploring the cognitive consequences of social search. In: Proceedings of computer human interaction
Vuorikari R et al (2009) Ecology of social search for learning resources. Campus-wide Inf Syst 26(4):272–286
Amitay E et al (2009) Social search and discovery using a unified approach. In: Proceedings of hyptertext and hypermedia conference
Evans B et al (2008) Towards a model of understanding social search. In: SSM
Konstas I et al (2009) On social networks and collaborative recommendation. In: SIGIR
Freeman LC et al (1979) Centrality in social networks: conceptual clarification. Soc Netw 1(3):215–239
Salton G et al (1987) Term weighting approaches in automatic text retrieval. Technical report. Cornell University
Robertson SE et al (1994) Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In: SIGIR
Page L et al (1999) The PageRank citation ranking: bringing order to the web
Horowitz D et al (2010) The anatomy of a large-scale social search engine. In: WWW
Franklin MJ et al (2011) CrowdDB: answering queries with crowdsourcing. In: SIGMOD
Yan T, Vikas K, Deepak G (2010) Crowdsearch: exploiting crowds for accurate real-time image search on mobile phones. In: Proceedings of the 8th international conference on mobile systems, applications, and services. ACM
Smyth B, Briggs P, Coyle M, O’Mahony MP (2009) Google shared! a case-study in social search. In: User modeling, adaptation and personalization. Springer, pp 283–294
Bozzon A, Brambilla M, Ceri S (2012) Answering search queries with crowdsearcher. In: Proceedings of the 21st international conference on World Wide Web. ACM
Bozzon A et al (2012) Extending search to crowds: a model-driven approach. Search Comput 7538:207–222
Fraternali P et al (2012) CrowdSearch: Crowdsourcing Web search
Chirita PA, Nejdl W, Paiu R, Kohlschütter C (2005) Using odp metadata to personalize search. In: SIGIR’05: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 178–185
Ferragina P, Gulli A (2005) A personalized search engine based on web-snippet hierarchical clustering. In: WWW’05: special interest tracks and posters of the 14th international conference on World Wide Web. ACM, New York, pp 801–810
Chirita P-A, Firan CS, Nejdl W (2007) Personalized query expansion for the web. In: 30th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR 2007). ACM, Amsterdam, pp 7–14
Xu S, Bao S, Fei B, Su Z, Yu Y (2008) Exploring folksonomy for personalized search. In: SIGIR’08: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 155–162
Noll M, Meinel C (2008) Web search personalization via social bookmarking and tagging. The semantic web, pp 367–380
Wang Q, Jin H (2010) Exploring online social activities for adaptive search personalization. In: Proceedings of the 19th ACM international conference on information and knowledge management. ACM
Carmel D et al (2009) Personalized social search based on the users social network. In: CIKM
Yin P et al (2010) On top-k social web search. In: CIKM
Gou L et al (2010) SNDocRank: document ranking based on social networks. In: WWW
Gou L et al (2010) SNDocRank: a social network-based video search ranking framework. In: MIR
Gou L et al (2010) Social network document ranking. In: JCDL
Rawashdeh M et al (2011) Folksonomy-boosted social media search and ranking. In: ICMR
Schenkel R et al (2008) Efficient top-k querying over social-tagging networks. In: SIGIR
Yahia SA et al (2008) Efficient network aware search in collaborative tagging sites. In: VLDB
Gulli A, Cataudella S, Foschini L (2009) Tc-socialrank: ranking the social web. Algorithms Models Web-graph 5427:143–154
Hotho A, Ja”schke R, Schmitz C, Stumme G (2006) Information retrieval in folksonomies: Search and ranking. In: Sure Y, Domingue J (eds) The semantic web: research and applications, volume 4011 of LNAI. Springer, Heidelberg, pp 411–426
Bao S, Xue G, Wu X, Yu Y, Fei B, Su Z (2007) Optimizing web search using social annotations. In: Proceedings of WWW. ACM, pp 501–510
Mizzaro S, Vassena L (2011) A social approach to context-aware retrieval. World Wide Web 14(4):377–405
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Khodaei, A., Sohangir, S., Shahabi, C. (2015). Personalization of Web Search Using Social Signals. In: Ulusoy, Ö., Tansel, A., Arkun, E. (eds) Recommendation and Search in Social Networks. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-14379-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-14379-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14378-1
Online ISBN: 978-3-319-14379-8
eBook Packages: Computer ScienceComputer Science (R0)