Urban Point-of-Interest Recommendation by Mining

UserCheck-in Behaviors

游晟佑2012.12.5

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Outline

1. Authors

2. Introduction

3. UPOI Mine Algorithm

4. Experimental Results and Discussions

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Authors

• Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wen-Ning Kuo and Vincent S. Tseng

• Institute of Computer Science and Information Engineering

• National Cheng Kung University

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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!

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Introduction (2/2)

• What makes it different?–More comprehensive

• Stepsi) 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

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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 POIsCpref, Hpref

3. POI Popularity (PP)

We employ the popularity of POI to make a "maximum likelihood estimation" of the relative between user and POIRP(relative popularity of POI)

把以上三樣 features的來源餵進 regression tree model

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UPOI Mine Algorithm(2/11)

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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:

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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 Gowalla users and their friends.

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UPOI Mine Algorithm(5/11)

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UPOI Mine Algorithm(6/11)

– Similarity by Relative Distance (DisSim)

where Distance() indicates the Euclidean distance of two

base-points and F(u) indicates the set of user u’s friends.

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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 a tag ’coffee’ is annotated on a POI is 2 / (2 + 10 + 88) = 0.22

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UPOI Mine Algorithm(8/11)

– Accordingly, given a user-POI pair (u, p), the features extracted form Individual Preference could be generally formulated as:

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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 A is:

• (1+0+0) / (1+0+2+5+0) = 0.125

– Hpref(preference in Highlight)• The user i’s personal preference of a highlight tag a is:

(1+2+5)

/ { (1+2+5) + (1+0)+(0+5)+(0+2)

+(0) } = 0.5

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UPOI Mine Algorithm(10/11)

• Features from POI Popularity(PP)

{3, 12, 3, 7, 5}• the set of POIs with category tag A are 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 p1 is

• 3 / (3 + 12 + 5) = 0.15

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UPOI Mine Algorithm (11/11)

• POI recommendation–We choose M5Prime as the relevance score

predictor because it has shown excellent performance in similar tasks

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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

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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.

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Experimental Results and Discussions(3/3)

• We compare the performance of UPOI-Mine with TrustWalker [5] and CF-based model [14] in terms of NDCG and MAE

Thanks for your listening …