IDENTIFYING SALIENT TOPICS FOR PERSONALIZED PLACE SIMILARITY

Transcription

1 IDENTIFYING SALIENT TOPICS FOR Benjamin Adams 1 and Martin Raubal 2 1 Centre for eresearch, The University of Auckland, New Zealand 2 Institute for Cartography and Geoinformation, ETH Zürich, Switzerland

2 I NTRODUCTION B ACKGROUND M ETHOD E VALUATION C ONCLUSION I NTRODUCTION A RE N EW Y ORK C ITY AND S YDNEY S IMILAR P LACES? P ERSONALIZED P LACE S IMILARITY B. A DAMS & M. R AUBAL

3 INTRODUCTION CROWDSOURCING KNOWLEDGE ABOUT PLACES Many applications for finding similar places Geographic information retrieval and recommender systems Social science research and education Environmental comparison Location-based advertising and marketing Common representation of places in GIS Spatial footprint Simple attributes (e.g., population) Operationalise knowledge about places from unstructured descriptions Place similarity is context-dependent need personalisation

4 BACKGROUND TRAVEL BLOG TOPICS USING LDA [BLEI ET AL. 2003]

5 BACKGROUND PLACE TOPIC SIGNATURE mountain view hill top valley site built year wall stone festival people dance celebration parade town old small local little temple monk Buddah Buddhist statue Places represented as probability vectors in a multidimensional topic space Place similarity is a function of topic vector similarity JSD(P Q) = 1 2 D KL(P M) D KL(Q M)

6 METHOD RANKING CITIES BY TOPIC SIMILARITY City topic 1 topic 2 topic 3 New York City Chicago Los Angeles Houston

7 METHOD RANKING CITIES BY TOPIC SIMILARITY City topic 1 topic 2 topic 3 New York City Chicago Los Angeles Houston user ordering topic 1 topic 2 topic 3 1. Chicago Chicago (0.0) L.A. (0.22) L.A. (0.0) 2. L.A. L.A. (0.22) Chicago (0.4) Houston (0.1) 3. Houston Houston (0.6) Houston (0.5) Chicago (0.4) Kendall s τ Relative weight

8 METHOD PERSONALISED SIMILARITY CALCULATION Use Kendall s τ relative weight to refactor topic values based on user ranking Calculate new similarity values for other cities based on personalised values city JS weighted JS B Chicago Los Angeles Houston

9 EVALUATION USER STUDY OF PLACE SIMILARITY RANKINGS Similar to Los Angeles ranking 1 1. Miami active nightlife 2. Minneapolis-St. Paul skyscrapers 3. Dallas home to corporations 4. Austin hot weather 5. Salt Lake City big houses 6. Cleveland busy 7. Portland, OR tourist towns Similar to Los Angeles ranking 2 1. Miami beautiful people; emphasis on appearance 2. Dallas very spread out 3. Austin music and performing arts 4. Portland, OR music and performing arts 5. Minneapolis-St. Paul music and performing arts 6. Salt Lake City western US 7. Cleveland large areas in economic decline

10 EVALUATION CONCORDANCE BETWEEN USER AND SYSTEM RANKINGS Weighted Wiki topics Weighted T.B. topics Distance Popula;on Very Somewhat Not Wikipedia topics Travel blog topics

11 CONCLUSION CONCLUSION Place similarity and difference is highly contextual Personalisation based on user interests / salient topics Algorithm that re-weights topics based on a small sample ranking of places User study found increased correspondence between user rankings and automatically re-weighted rankings Future work investigating other representations of place (e.g., sentiment) and new sources (e.g., social media)