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TraMSNet - A Mobile Social Network Application for Tourism-

TraMSNet - A Mobile Social Network Application for Tourism-. 4th International Workshop on Location Based Social Networks Pittsburgh, Pennsilvania, USA 8 th September 2012. Jorge Gaete-Villegas, Dongman Lee, Meeyoung Cha, In-Young Ko Korean Advance Institute of Science and Technology

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TraMSNet - A Mobile Social Network Application for Tourism-

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  1. TraMSNet-A Mobile Social Network Application for Tourism- 4th International Workshop on Location Based Social Networks Pittsburgh, Pennsilvania, USA 8th September 2012 Jorge Gaete-Villegas, Dongman Lee, Meeyoung Cha, In-Young Ko Korean Advance Institute of Science and Technology Department of computer science

  2. Presentation Outline 1)Introduction Motivation, hypothesis & research question • 2) Related Work Tourist, Existing solutions & Matching algorithms 3) Approach Ranking Function 4) Evaluation Methodology, results & analysis 5) Results • Findings • 6) Final words • Conclusions, future work 2:11

  3. Introduction_motivation Place constraints certain activities: Some goals are more likely Is it meaningful to always match co-located users based on their similarities? How does the location affect (or should affect) the matching process? Similar users link by a mobile social network User .Users share a location and purpose .Different profiles .Possible synergies 3:11

  4. Introduction_Hypothesis What is important for users? Hypothesis Online questionnaire, open answer: “Top 5 concerns when looking for a travel partner" Sample “In a LBSN applications focused on tourism, matching users in terms of their similarities does not fulfill the needs of tourists” Under 20.....3 20-25 .........11 26-30...........4 31-30...........2 America...9 Europe.....5 Africa........4 Asia..........2 Age Female... 9 Male....... 11 Gender Where? Results 4:11

  5. Related Work_tourism domain Commercial tourism apps • Basically “Cyber tour guides”: • Such as “Cyberguide”, “GUIDE”, “LoL@”, “Deep Map”, “SmartKom”, “ REAL”, “TellMaris”, “CRUMPET” • Leveraging Location information and Geo referred information To provide onsite information, recommendationss based on similar users, tours • Social network analysis Users Matching Similarities between users to generate a match Leveraging Profile information : User similarities Location history: User similar behaviors 6:11

  6. Approach_Complementary skills Rranks user jto user i Users Afinity given the place l user similarity complementary user to i in the given location Done by matching users characteristics Similarity between j and complementary user to i If i(n) and j(n) are numeric values If i(n) and j(n) are numeric range If i(n) and j(n) are lists of string 7:11

  7. Evaluation_Design What to evaluate? Focus on evaluating the recommendation function: It is the contribution and differentiation from existing solutions Why survey? Survey Mechanism i) Analysis: User’s perception on the correctness of a suggestion ii) Opinion when the user is exposed to both suggestions under the same circumstance i) Analysis: User’s perception on the correctness of a suggestion ii) Opinion when the user is exposed to both suggestions under the same circumstance Procedure 1. On-line questionnaire asking for his/her profile information 2. After completion, a fictional scenario is presented to the user 3. After reading, simultaneously two list of users are displayed. Lists showing user profile 4. Finally, the surveyed will be asked: "Which one of the lists would you preferred to received in the given situation.” 8:11

  8. Results There is no difference between the 3 mayor preffered options Overseas prefer combined model. Locals prefer homophilly 10:11

  9. To conclude Conclusions • Users want to be matched with others for multiple reasons • Out of the exploratory survey, at least two Homophilly is not always enough Its appropiatness depends on the relationship between users and locations • Our approach gives a general model for including similarities and differences between users • Our ranking algorithm includes both drivers for matching, therefore is a general form of the homophilly based matching algorithms 11:11

  10. Questions? Thank You! 감사합니다! Muchas gracias! 11:11

  11. TraMSNet-A Mobile Social Network application for tourism- 4th International Workshop on Location Based Social Networks Pittsburgh, Pennsilvania, USA 8th September 2012 Jorge Gaete-Villegas, Dongman Lee, Meeyoung Cha, In-Young Ko Korean Advance Institute of Science and Technology Department of computer science

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