1. Can social network be used for location-aware recommendation? Pasi Fränti, Karol Waga and Chaitanya Khurana  P. Fränti, K. Waga, and C. Khurana Can social network be used for location-aware recommendation Int. Conf. on Web Information Systems & Technologies (WEBIST'15), 558-565, 2015

2. Location-aware recommendation Results Press here Location Input: User Location Time Keyword (optional) Recommendations: Nearby services Photos of other users

3. Four aspects of relevance Example from practice Last skiing of winter Date: 4.4.2010 Location: N 62.63 E 29.86 User:Pasi Text description Keywords (tags) User profile Social network Recency of data Season (not relevant in July) 1. Content 2. Time 3. Location 4. User and his network Distance to user Arppentie 5, Joensuu

4. Four aspects of relevance Example from practice Last skiing of winter Date: 4.4.2010 Location: N 62.63 E 29.86 Arppentie 5, Joensuu User:Pasi Text description Keywords (tags) User profile Social network Recency of data Season (not relevant in July) 1. Content 2. Time 3. Location 4. User and his network Distance to user

5. Keyword seached frequently (S F ) Keyword searched nearby (S N ) Keyword searched recently (S S ) Keyword searched by the user (S U ) Distance between user and the place (S L ) Normalizing to the scale [0,1] Rating by users in scale of 0-5 (S R ) Search HistoryLocationRating Scoring of services History score: S H = S F + S N + S S + S U Total score: S = N H + 2  N L + N R + 1

6. Utilizing user network

7. Effectiveness of network Popular networks: Facebook, Twitter, Google+, Instagram User activities: Likes, Comments, Retweet, favourite, rating. Activity stats in Facebook: 6 hrs/month, 2.7 billion likes/day Social vs. information sharing Selected friends vs. ad hoc Buddy vs. stranger On-line vs. offline network

8. Small world phenomenon 463 229 55 298 88 298 193 580 142 FB friends: Average=261 261 68,000 17 M4.6 B Entire world reachable in 6 steps (theory) Experiment on Twitter users: 3.43 steps Total reach: world

9. Distribution of information optimistic 261 6,786 17,7484,698 Total reach: 10% 1%0.1% 0.01% 29,493 Friends sharing Reached Efficiency reduces 2668180

10. Distribution of information more realistic 261 783 261 Total reach: 1,305 Efficiency reduces 1% 0.1%0.01% 0% Friends sharing Reached 3100

11. Similarity of users Strength of link?

12. Methods for user similarity Friendship in Facebook Existing link  similar Friend of a friend not considered Pages liked in Facebook More matches  more similar Places visited in Mopsi Visits same places  similar

13. Pages liked in Facebook Category similarity: Both like Fast Food Restaurants Page name Page category Similar AliceBob Page similarity: Both like Hesburger

14. Mikko (14)Radu (19) Philosophiæ Naturalis Principia Mathematica Computers and Intractability: A Guide to the Theory of NP- Completeness Nivan kylä Mopsi Impit Finland Kylpylähotelli Rauhalahti S+SSPR 2014 International Biographical Centre Joensuun Uimaseura Winter Swimming World Championships 2014 / Talviuinnin MM-kisat 2014 East Finland Graduate School in Computer Science and Engineering Joensuun Tiedepuisto Puhutun nykysuomen tutkimushanke Hello Jessie Epic Coders S+SSPR 2014 Team Four Star (Official) PavoCons Graafinen suunnittelija - Pasi Seppänen Tripworks Oy Colegiul National Traian Impit Finland Mopsi East Finland Graduate School in Computer Science and Engineering Innovation Month Photo HD Boohoo Games Dr. James Grime Itä-Suomen yliopiston LUMA-keskus Polkujuoksu 13.9.2014 - Joensuu/Kontiolahti SenzoFit Odyssey 2014 Stomatolog Dr. Sabin Silviu Badea = 14% Page similarity

15. CategoryMikko (A)Radu (B) A  B Community211 Comm. Org.121 Education242 Consulting111 Attractions111 Total6 = 22% Mikko Book (2) Community (2) Attractions (1) Education (2) Travel (1) Community Organization (1) Company (1) Sports team (1) Amateur Sports team (1) Consulting (1) Business services (1) Radu Internet (1) Community organization (2) Tv show (1) Consulting (1) Media (1) Professional services (1) Education (4) Attractions (1) Website (1) Video game (1) Teacher (1) Non-profit organization (1) Sports event (1) Community (1) Health (1) Category similarity

16. Select first word Media/News/Publishing → MediaTV channel → TV Games/Toys → Games Games → Game Pre-processing categories Convert plural to singular

17. Location similarity visit statistics 1 0 0 0 0 0 0 0 3 0 0 2 2 2 0 1 1 1 4 3 1 9 7 6 Places Visit frequencies

18. Similarity calculations Bhattacharyya distance 4 3 1 2 2 0 0 0 3 1 1 1 0 0 2 1 0 0 0 0 0 9 6 7 8 4 3 3 2 1 1 0 0.44 0.50 0.14 0.22 0.33 0.00 0.00 0.00 0.43 0.11 0.17 0.14 0.00 0.00 0.28 0.11 0.00 0.00 0.00 0.00 0.00 0.47 0.27 0.00 0.14 0.00  = 0.88 -ln = 0.13 0.26 0.00 0.15 0.00 0.41 0.89

19. Collected data Municipalities:JoensuuLiperiOutokumpuPolvijärviKontiolahtiIlomantsiJuuka 63.44N 28.65E 62.25N 31.58E Joensuu sub-region bounding box 293 places (Mopsi services) User activities until 31.12.2014 ‒ Photos taken ‒ Tracking started or ended

20. Experimental results

21. Nine test persons MopsiFacebook photostracksfriendspages Andrei67696463285 Julinka3850122229154 Mikko190845514 Oili646716429863 Pasi97162088867 Radu141712229819 Rezaei7168519316 Chait6322580195 Jukka991126142120

22. Survey questions Q1: How similar you find the person is to you? Q2: How useful you find his/her Mopsi photos? Context for Q2: Does he recommend, via his/her Mopsi postings, useful and interesting places to visit in future.

23. User similarity Influential users Not friends in Facebook Everyone is like Radu

24. Expected usefulness Mostly the same rankings (as with similarity) Ranking of Pasi and Julinka improved Expected vs. reality?

25. Most popular FB pages 2 University of Eastern Finland Joensuu This is Finland Stieg Larsson Phd, Masters and Postdoc Intern. Scholarships Joensuun Jääkarhut - Joensuu Polar Bears Joensuun Susi University of Eastern Finland (UEF) Vatakka Fotoaurinko Scientific Writing Assistant (SWAN) Carlson Ilosaarirock Festival Suomen Luonto House Sauna Jenni Vartiainen Official Hello Jessie Itä-Suomen yliopiston LUMA-keskus ABBA Facebook for Every Phone Hannes Hynönen - Fanisivu Jukolan viesti 10MILA The Herajärvenkierros Trail Kuopio Maraton 8 Impit Finland S+SSPR 2014 ECSE 7 Mopsi 6 Joensuu Science Park 5 UEF - School of Computing Odyssey 2014 4 SciFest Joensuu 3 Kaisa Mäkäräinen Jobs in Finland Joensuu - kaupunki idässä IMPDET-Le Polkujuoksu 13.9.2014

26. Page likes similarity Correlates with user evaluations: Similarity:0.47 Usefulness: 0.17

27. Similarity Graph page similarities Radu Pasi Andrei Oili Mikko Chait Rezaei Julinka Jukka 0.25 0.14 0.16 0.05 0.04 0.060.08 0.05 0.03 0.07 0.04

28. Location data example

29. Correlates with user evaluations: Similarity:0.28 Usefulness: 0.17

30. Conclusions FB likes correlates to similarity Location history has weaker correlation Understanding of similarity interesting findings  Answer: YES, but question remains HOW. To be continued…