[100621]제안발표

위치공유 SNS와 소셜 어노테이션 서비스를 이용한 소셜 검색 엔진의 설계 및 구현 발 표 자 : 이 동 균 지도교수 : 권 준 희

Aardwolf Enhanced Aardvark, social search engine

Contents Related work Our work Scenario Conclusion

Related Work

Aardvark Aardvark is social search engine $50Million Reference : - http://techcrunch.com/2010/02/11/google-acquires-aardvark-for-50-million

What is Social Search? the incorporation of information about social networks and relationships into the information retrieval process “Social search” is an umbrella term used to describe search acts that make use of social interactions with others. Reference : - Sebastian Marius Kirsch, “Social Information Retrieval”, Rheinische Friedrich-Wilhelms-Universität Bonn, 2005 - Einat Amitay et al, “Social Search and Discover Using Unified Approach”, ACM, 2009 - David Carmel et al, “Personalized Social Search Based on the User’s Social Network”, ACM, 2009 - Meredith Ringel Morris et al, “What Do People Ask Their Social Networks, and Why?”, CHI, 2010 - Brynn M Evans, Ed H. Chi, “Towards a Model of Understanding Social Search”, ACM, 2010

What is Social Network? A social structure made of individual called ‘nodes’, which are connected by one or more specific types of interdependency, such as friendship, common interest, financial exchange, dislike or prestige. Reference : - http://en.wikipedia.org/wiki/Social_network - http://en.wikipedia.org/wiki/Social_networking

Goal of Social Search To use the social information to improve the user’s search experience over regular full text search. Social search(a.k.a social information retrieval) = social networks + information search Reference : - Sebastian Marius Kirsch, “Social Information Retrieval”, Rheinische Friedrich-Wilhelms-Universität Bonn, 2005 - Einat Amitay et al, “Social Search and Discover Using Unified Approach”, ACM, 2009

Previous Studies Social Network Based Search - PeopleRank, SocialRank, SNDocRank, Aardvark Social Annotation Based Search - FolkRank, SocialBookmarkRank, SocialSimRank, SocialPageRank Reference : - Andreas Hotho et al, “Information Retrieval in Folksonomies”, The Semantic Web, 2006 - Yusuke Yanbe et al, “Can Social Bookmarking Enhance Search in the Web?”, ACM, 2007 - Shenghua Bao et, al, “Optimizing Web Search Using Social Annotations”, WWW2007, 2007 - David Carmel et al, “Personalized Social Search Based on the User’s Social Network”, ACM, 2009 - Liang Gou et al, “SNDocRank: a social network-based video search ranking framework”, ACM, 2010 - Abderrahmen Mtibaa et al, “PeopleRank: Social Opportunistic Forwarding”, IEEE, 2010

Aardvark Aardvark is social search engine - Social Network Based Search - Deployed and validated in large-scale

is Social Question-Answering Service

Which one is better? Aardvark vs <Anonymous group> <My Friends (of the friends)>

Social network rocks! • More Reliable • More Satisfactory • Even More Responsible Reference : - Damon Horowitz, “The Anatomy of a Large-Scale Social Search Engine”, WWW2010, 2010 - Meredith Ringel Morris et al, “What Do People Ask Their Social Networks, and Why?”, ACM, 2010 - David Carmel et al, “Personalized Social Search Based on the User’s Social Network”, ACM, 2009

Our work

Aardwolf Enhanced Aardvark, social search engine

Structure of Aardvark Sign-up time DB Importer Index User, Topic, Graph, Content at query time Conversation Routing Gateway Manager Engine Reference : - Damon Horowitz, “The Anatomy of a Large-Scale Social Search Engine”, WWW2010, 2010

First solution : We deal with user’s up-to-date topic Your up-to-date topic does matter Reference : - Xuerui Wang, Andrew McCallum, “Topics over Time”, ACM, 2006 - Andreas Hotho et al, “Trend Detection in Folksonomies”, SAMT, 2006 - http://burak-arikan.com/growth-of-a-twitter-graph, “Growth of a twitter graph”, 2010

Structure of Aardwolf Up-to-date Topics Up-to-date Topic Aggregator Social Annotation Service at query time Up-to-date Topics Conversation Routing Gateway Manager Engine

Why ? Social Annotation Service • Tag(Annotation) as topic • Easiest way to get user’s up-to-date topic Reference : - Andreas Hotho et al, “Information Retrieval in Folksonomies”, The Semantic Web, 2006 - Yusuke Yanbe et al, “Can Social Bookmarking Enhance Search in the Web?”, ACM, 2007 - Shenghua Bao et, al, “Optimizing Web Search Using Social Annotations”, WWW2007, 2007

Ranking process of Aardvark Gateway Query : “what would be perfect for my girlfriend’s present?” Routing Engine Rank users by algorithm of Aardvark Users : Ranked by their social information Conversation Manager

Ranking Algorithm User’s answerable probability Intimacy Expertise Topic Assignment Topic Expertise Reference : - Damon Horowitz, “The Anatomy of a Large-Scale Social Search Engine”, WWW2010, 2010

Ranking process of Aardwolf Gateway Query : “what would be perfect for my girlfriend’s present?” Up-to-date Topic Aggregator Up-to-date Topics Social Annotation Service Routing Engine Rank users by algorithm of Aardwolf Users : Ranked by their social information Conversation Manager

Ranking Algorithm Aardvark Topic Expertise Aardwolf Topic Up-to-date Topic Reference : - Damon Horowitz, “The Anatomy of a Large-Scale Social Search Engine”, WWW2010, 2010

Second solution : We deal with user’s knowledge of location • User’s knowledge of location

Structure of Aardwolf Location History Location Manager Location sharing SNS at query time Conversation Routing Up-to-date Gateway Topic Manager Engine Aggregator

What is the ? Location Sharing SNS User’s knowledge of Location

Ranking process of Aardvark Gateway Query : “where is the best restaurant in Kang-Nam, Seoul ?” Routing Engine 1. Select users if their profile mentions Seoul 2. Rank users by algorithm of Aardvark Users : Ranked by their social information Conversation Manager

Re-ranking process of Aardwolf Gateway Query : “where is the best restaurant in Kang-nam, Seoul ?” Location Location history Manager Routing Engine 1. Select users if they have been Kang-nam recetly. 2. Rank users by algorithm of Aardwolf 3. Re-rank users by their location score Users : re-ranked by their location score Conversation Manager

Re-ranking Algorithm Re-rank score(RRs) = Rs(Ranking Score) + (1- ) Ls(Location Score) e.g : Rs = {A: 0.2, B: 0.5, C:0.3} Ls = {A: 0.4, B: 0.4, C:0.2} RRs = {A: 0.3, B: 0.45, C: 0.25} Ls = LUC / Sum of LUC(Location Use Count) e.g : LUC = {Seoul: 20, Incheon: 30, Suwon: 50} Ls(Seoul) = 20 / 100 = 0.2

Scenario

Scenario and his girlfriend have been seeing B A A each other for 3 years. Me has been restaurant reviewer for several C years. D C Me a big fan of Aardwolf. “where is the best restaurant for Japanese Ramen in Kang-nam, Seoul?”

Ranking process of Aardwolf Gateway Query : “where is the best Japanese Ramen restaurant in Kang-nam, Seoul ?” Location Location history Manager Routing Engine 1. Select users if they have been Kang-nam recetly. Users : re-ranked by their location history Conversation Manager

Select users by their location, Kang-nam, Seoul Profile Foursquare B A A Seoul Me B Suwon Kang-nam, Seoul C Seoul D C D Seoul Aardvark Aardwolf A C D A B C D

Ranking process of Aardwolf Gateway Query : “where is the best Japanese Ramen restaurant in Kang-Nam, Seoul ?” 1. Select users if they have been kang-nam recetly. Routing Up-to-date Topic Engine Aggregator Recent User Topics Social Annotation Service 2. Rank users by algorithm of Aardwolf Users : re-ranked by their location history Conversation Manager

Rank : Consider Change of Topic , Ramen Up-to-date Aardvark Aardwolf B A All Topic Topic Score Score A 26 8 0.5 0.30 Me B 5 2 N/A 0.06 D C C 17 26 0.33 0.39 D 8 20 0.17 0.25 Aardvark Aardwolf A C D C A D B

Re-ranking process of Aardwolf Gateway Query : “where is the best Japanese Ramen restaurant in Kang-Nam, Seoul ?” Location Location history Manager Routing Engine 1. Select users if they have been kang-nam recetly. 2. Rank users by algorithm of Aardwolf 3. Re-rank users by their location use count Users : re-ranked by their location history Conversation Manager

Re-Rank : Consider Location History, Kang-nam Location Use Rank score Re-rank score B A Count, Kang-nam A 5 0.3 0.18 Me B 20 0.6 0.13 C 50 0.39 0.44 D C D 120 0.25 0.74 Ranked Re-ranked C A D B D C A B

Aardwolf rocks! Up-to-date Aardvark Aardwolf Profile Foursquare Topic LUC Topic score score A Seoul Kang-nam 26 8 5 0.5 0.18 B Suwon Kang-nam 5 2 20 N/A 0.13 C Seoul Kang-nam 17 26 50 0.33 0.44 D Seoul Kang-nam 8 20 120 0.17 0.74 Aardvark Aardwolf A C D D C A B

Conclusion

Work in Progress • Modified ranking algorithm to consider user’s up-to-date topic • Added re-ranking algorithm to deal with user’s knowledge of location Future work • Implement the Aardwolf social search engine • Experiment with the real data of Twitter, Delicious and Foursquare

@haandol Discussion Everything is connected to everything else.

[100621]제안발표

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