An efficient mobile social search method
Pages 82 - 85
Abstract
With the rapid development of internet technology, social networks and social searches are becoming popularly. Unlike traditional web searches, the social search provides optimal search results according to user preferences. In this paper, we propose a mobile social search method based on popularities and user preferences. The popularity is calculated by collecting the visiting records of users. The user preferences are generated by the actual visiting information among the search results. We process a skyline query to extract the meaningful information from the candidate objects with multiple features. The proposed method ranks social search results by combining user preferences and popularity with the skyline query processing mechanism. To show the superiority of the proposed method, we compare it with the existing method through performance evaluation.
References
[1]
Shankar, P. Huang, Y. Castro, P. Nath, B. and Ifto, L. 2012. Crowds Replace Experts: Building Better Location-based Services using Mobile Social Network Interactions. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (2012).
[2]
Khodaei, A. and Shahabi, C. 2012. Social-Textual Search and Ranking. In Proceedings of the International Workshop on Crowdsourcing Web Search (2012).
[3]
Joung, Y. Chen, S. M. Wu, C. and Chiu, T. H. 2013. A Comparative Study of Expert Search Strategies in Online Social Networks. In Proceedings of the International Conference on Advanced Information Networking and Applications (2013).
[4]
Smith, I. Consolvo, S. Lamarca, A. Hightower, J. Scott, J. Sohn, T. Hughes, J. Iachello, G. and Abowd, G. D. 2005. Social Disclosure of Place: From Location Technology to Communication Practice. In Proceedings of the International Conference on Pervasive Computing (2005).
[5]
Malik, A. K. and Dustdar, S. 2011. Sharing and Privacy Aware RBAC in Online Social Networks. In Proceedings of the International Conference on Social Computing (2011).
[6]
Tang, K. P. Lin, J. Hong, J. I. Siewiorek, D. P. and Sadeh, N. 2010. Rethinking Location Sharing: Exploring the Implication of Social-Driven vs. Purpose-Driven Location Sharing. In Proceedings of the International Conference on Ubiquitous Computing (2010).
[7]
Nagpal, A. Hangal, S. Joyee, R. R. and Lam, M. S. 2012. Friends, Romans, Countrymen: Lend me your URLs. Using Social Chatter to Personalize Web Search. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (2012).
[8]
Kashyap, A. Arnim, R. and Hristidis, V. 2012. SonetRank-Leveraging Social Networks to Personalize Search. In Proceedings of the International Conference on Information and Knowledge Management (2012).
[9]
Vu, T. and Baid, A. 2012. Ask, Don't Search: A Social Help Engine for Online Social Network Mobile Users. In Proceedings of the IEEE Sarnoff Symposium (2012).
Index Terms
- An efficient mobile social search method
Recommendations
Developing a Search Engine for Social Networks
ICFET '22: Proceedings of the 8th International Conference on Frontiers of Educational TechnologiesThis paper describes the development of a search engine especially designed for retrieving information from social networking sites. The users of social networking sites, such as Facebook, post large number of texts, photos, and videos on their ...
Evolving social search based on bookmarks and status messages from social networks
CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge managementSocial search is a variant of information retrieval where a document or website is considered relevant if individuals from the searcher's social network have interacted with it. Our ranking metric Social Relevance Score (SRS) is based on two factors. ...
The value of social search: seeking collective personal experience in social Q&A
ASIST '13: Proceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information BoundariesIn this paper, we examine the value of social question-answering (Q&A) services as a platform for social search. We present a quasi-field study where we instructed 20 study participants to use a social Q&A service, Yahoo! Answers, for a period of one ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2014
386 pages
ISBN:9781450330602
DOI:10.1145/2663761
- Conference Chairs:
- Chao Lu,
- Esmaeil Nadimi,
- Program Chairs:
- Sung-Ryul Kim,
- Wei Wang
Copyright © 2014 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
- Winway Systems: Winway Systems, Inc.
- SIGAPP: ACM Special Interest Group on Applied Computing
- ACCT: Association of Convergent Computing Technology
- TU: Towson University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 October 2014
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
RACS '14
Sponsor:
- Winway Systems
- SIGAPP
- ACCT
- TU
RACS '14: International Conference on Research in Adaptive and Convergent Systems
October 5 - 8, 2014
Maryland, Towson
Acceptance Rates
RACS '14 Paper Acceptance Rate 59 of 251 submissions, 24%;
Overall Acceptance Rate 393 of 1,581 submissions, 25%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 59Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 13 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in