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Urban Point-of-Interest Recommendation by Mining User Check-in Behaviors

Urban Point-of-Interest Recommendation by Mining User Check-in Behaviors. 游晟佑 2012.12.5. Outline. Authors Introduction UPOI Mine Algorithm Experimental Results and Discussions. Authors. Josh Jia-Ching Ying, Eric Hsueh -Chan Lu, Wen- Ning Kuo and Vincent S. Tseng

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Urban Point-of-Interest Recommendation by Mining User Check-in Behaviors

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  1. Urban Point-of-Interest Recommendation by Mining UserCheck-in Behaviors 游晟佑 2012.12.5

  2. Outline • Authors • Introduction • UPOI Mine Algorithm • Experimental Results and Discussions

  3. Authors • Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wen-NingKuo and Vincent S. Tseng • Institute of Computer Science and Information Engineering • National Cheng Kung University

  4. Introduction (1/2) • Why use UPOI Mine? • a number of social based recommendation techniques have been proposed in the literature, most of their concepts are only based on the individual or friends' check-in behaviors. (his / her historical data or limited in geographical area) • regression-tree-based predictor, 1st time use in this kind of research (They asserted) • a real dataset from Gowalla!

  5. Introduction (2/2) • What makes it different? • More comprehensive • Steps i) Social Factor, ii) Individual Preference, and iii) POI Popularity for model building For extracting features in i), ii) iii), and feed it into regression tree model -> relevance score -> POI recommendation

  6. UPOI Mine Algorithm(1/11) 1. Social Factor (SF), (朋友在哪邊打卡了?打卡次數?打卡地點是否與該user接近?) CheckSim, DisSim 2. Individual Preference (IP) Descriptive features and semantic tags from user check-in POIs Cpref, Hpref 3. POI Popularity (PP) We employ the popularity of POI to make a "maximum likelihood estimation" of the relative between user and POI RP(relative popularity of POI) 把以上三樣features的來源餵進regression tree model

  7. UPOI Mine Algorithm(2/11)

  8. UPOI Mine Algorithm(3/11) • Features from Social Factor(SF) • given a friend f and a set of POI P, the f’s relative check-ins of a POI p is formulated as: • given a user-POI pair (u, p), the features extracted form Social Factor could be generally formulated as:

  9. UPOI Mine Algorithm(4/11) • Similarity Measurement • In a LBSN data, the most important information is user’s common check-ins and distance among users for user similarity measurement. • Similarity by Common Check-ins (CheckSim) - We employ the χ2 test for testing relation of check-in behaviors of Gowallausers and their friends.

  10. UPOI Mine Algorithm(5/11)

  11. UPOI Mine Algorithm(6/11) • Similarity by Relative Distance (DisSim) where Distance() indicates the Euclidean distance of two base-pointsand F(u) indicates the set of user u’s friends.

  12. UPOI Mine Algorithm(7/11) • Features from Individual Preference(IP) • In Gowalla website, there are two kinds of semantic tags, i.e., category and highlight • where count(t, p) indicates the number of times the tag t is annotated on the POI p ,and T(p) indicates the set of tags of POI p. • the possibility of that atag ’coffee’ is annotated on a POI is2 / (2 + 10 + 88) = 0.22

  13. UPOI Mine Algorithm(8/11) • Accordingly, given a user-POI pair (u, p), the features extracted form Individual Preference could be generally formulated as:

  14. UPOI Mine Algorithm(9/11) • Cpref(preference of category) (note: 1,0,2,5,0 for user i) • The user i’s personal preference of a category tag Ais: • (1+0+0) / (1+0+2+5+0) = 0.125 • Hpref(preference in Highlight) • The user i’s personal preference of a highlight tag ais: (1+2+5) / { (1+2+5) + (1+0)+(0+5)+(0+2) +(0) } = 0.5

  15. UPOI Mine Algorithm(10/11) • Features from POI Popularity(PP) {3, 12, 3, 7, 5} • the set of POIs with category tag Aare p1, p2, and p5. The total check-in of POI p1, p2, and p5 are 3, 12, and 5, respectively. Thus, the popularity of POI p1is • 3 / (3 + 12 + 5) = 0.15

  16. UPOI Mine Algorithm (11/11) • POI recommendation • We choose M5Prime as the relevance score predictor because it has shown excellent performance in similar tasks

  17. Experimental Results and Discussions(1/3) • Normalized Discounted Cumulative Gain (NDCG) to measure the list of recommended POIs. • NDCG 1.0 means the effectiveness of recommender is pretty good • Mean Absolute Error (MAE) to measure the list of recommended POIs as Equation • The lower MAE is, the fewer error is

  18. Experimental Results and Discussions(2/3) All these means this kind of data is good. Also they compared earlier works to proof this method is good.

  19. Experimental Results and Discussions(3/3) • Wecompare the performance of UPOI-Mine with TrustWalker [5] and CF-based model [14] in terms of NDCG and MAE

  20. Thanks for your listening …

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