Ontology-based Matchmaking to Provide Personalized Offers
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Ontology-based Matchmaking to Provide Personalized Offers
- 1. ENTER 2017 Research Track Slide Number 1 Christoph Grün, Julia Neidhardt*), Hannes Werthner E-Commerce Group, TU Wien, Austria *) julia.neidhardt@ec.tuwien.ac.at http://www.ec.tuwien.ac.at Ontology-based Matchmaking to Provide Personalized Offers
- 2. ENTER 2017 Research Track Slide Number 2 Agenda Problem Statement Related Work Matchmaking Process Implementation Evaluation Conclusion & Future Work → → → → → →
- 3. ENTER 2017 Research Track Slide Number 3 Problem Statement
- 4. ENTER 2017 Research Track Slide Number 4 Problem Statement No common view on the tourism space exists Gap between mental model of tourists and model of tourism offers/space (Gretzel et al.*)) Problem: matching of the customers’ view (tourist’s personality and preferences) with the suppliers’ perspective (tourism objects) Approach: ontology-based matchmaking process Focus of this talk: present results of a user study focusing on evaluating the feasibility of the approach *) Gretzel et al. Semantic Representation of Tourism on the Internet. Journal of Travel Research, 2008. → → → → matching
- 5. ENTER 2017 Research Track Slide Number 5 Related Work
- 6. ENTER 2017 Research Track Slide Number 6 Recommendation Systems Types of Recommenders Tourist Typologies Semantic Web Ontologies Similarities MTRS Gavalas & Kenteris, 2011 SPETA García-Crespo et al., 2009 Advisor Suite Jananch et al., 2010 etPlanner Höpken et al., 2006 SigTur Moreno et al., 2012 PixMeAway Neidhardt et al., 2014 Demographic- based Burke, 2007 Collaborative- based Herlocker et al., 2004 Content-based Pazzani & Billsus, 2007 Knowledge-based Felfernig et al., 2006 Hybrid Recommenders Schiaffino & Amandi, 2009 Characteristics and motivation of tourists Cohen, 1972 Tourist Roles Yiannakis & Gibson, 1992 Model of a destination’s attractiveness Plog, 2001 Travel career patterns Pearce & Lee, 2005 Tourist Factors Neidhardt et al., 2014 QALL-ME Ou et al., 2008 CRUZAR Mínguez et al., 2010 SPETA García-Crespo et al., 2009 INREDIS Busquet, 2009 HARMONISE Fodor & Werthner, 2005 GETESS Staab et al., 1999 Path-based Rada et al., 1989 Wu & Palmer, 1994 Zhong et al., 2002 Sussna, 1993 Information- based Resnik, 1998 Seco et al., 2004 Feature-based Tversky, 1977 Knappe et al., 2007 Hybrid Jiang & Conrath, 1997 Lian, 1998 Mazuel et al., 2008 Related Work The research draws on knowledge from different fields
- 7. ENTER 2017 Research Track Slide Number 7 Matchmaking Process
- 8. ENTER 2017 Research Track Slide Number 8 Matchmaking Process The matchmaking comprises two sub-processes Tourist Types Tourist Tourism Object Generic Preferences Generic Characteristics PROCESS1 PROCESS 1 PROCESS 2
- 9. ENTER 2017 Research Track Slide Number 9 Matchmaking Process The matchmaking comprises two sub-processes Tourist Types Tourist Tourism Object Generic Preferences Generic Characteristics PROCESS1 MATCHMAKING PROCESS 1 PROCESS 2
- 10. ENTER 2017 Research Track Slide Number 10 Tourist Matchmaking Process The matchmaking comprises two sub-processes Tourist Types Tourist Tourism Object Generic Preferences Generic Characteristics PROCESS1PROCESS2 Specific Interests Top N Recommendations MATCHMAKING PROCESS 1 PROCESS 2
- 11. ENTER 2017 Research Track Slide Number 11 Tourist Matchmaking Process The matchmaking comprises two sub-processes Tourist Types Tourist Tourism Object Generic Preferences Generic Characteristics PROCESS1PROCESS2 Specific Interests Top N Recommendations MATCHMAKING PROCESS 1 PROCESS 2
- 12. ENTER 2017 Research Track Slide Number 12 Tourist Matchmaking Process The matchmaking comprises two sub-processes Tourist Types Tourist Tourism Object Generic Preferences Generic Characteristics PROCESS1PROCESS2 Specific Interests Learn specific interests’dislike churches’ MATCHMAKING PROCESS 1 PROCESS 2 Specific Interests
- 13. ENTER 2017 Research Track Slide Number 13 Tourist Matchmaking Process The matchmaking comprises two sub-processes Tourist Types Tourist Tourism Object Generic Preferences Generic Characteristics PROCESS1PROCESS2 Tourism Object Specific Attributes Learn specific interests’dislike churches’ MATCHMAKING PROCESS 1 PROCESS 2 Specific Interests
- 14. ENTER 2017 Research Track Slide Number 14 Matchmaking Process The matchmaking comprises two sub-processes Tourist Types Tourist Tourism Object Generic Preferences Generic Characteristics PROCESS1PROCESS2 Tourist Tourism Object Specific Interests Specific Attributes Learn specific interests’dislike churches’ MATCHMAKING MATCHMAKING Tourism Ontology PROCESS 1 PROCESS 2
- 15. ENTER 2017 Research Track Slide Number 15 Fragment of the cDOTT*) ontology depicting the semantic description of the Viennese attraction Schönbrunn Palace Matchmaking Process A tourism ontology is used to drive the matchmaking → PROCESS 1 PROCESS 2 *) cDOTT = core Domain Ontology of Travel and Tourism Barta et al. Covering the semantic space of tourism: An approach based on modularized ontologies. In Proceedings of the 1st Workshop on Context, Information and Ontologies, 2009.
- 16. ENTER 2017 Research Track Slide Number 16 Tourist types are a valid means to predict the set of activities in which tourist like to engage*). We use the 7 tourist factors (e.g. culture loving) presented by Neidhardt et al.**) Predefined tourist factors can be used as stereotypical approach to generate a generic profile Orthogonal vectors are suited to model tourist factors Tourist Generic Preferences First Matchmaking Process Generating a high-level tourist profile Tourist Types *) Gretzel et al. Tell me who you are and I will tell you where to go: Use of Travel Personalities in Destination Recommendation Systems. Information Technology & Tourism, 7:3– 12, 2004. **) Neidhardt et al. A picture-based approach to recommender systems. Information Technology & Tourism, 15(1), 2014. → PROCESS 1 PROCESS 2 → → → Sight Seeker Cultural Visitor Nature Lover Avid AthletheAction Seeker Educational Buff Sun Worshipper PROCESS1
- 17. ENTER 2017 Research Track Slide Number 17 Tourist Second Matchmaking Process Exploit ratings to learn specific interests of tourist Tourist TypesPROCESS2 Specific Interests Learn specific interests Specific Interests ’dislike churches’ PROCESS 1 PROCESS 2 Tourist Tourism Object PROCESS1 MATCHMAKING Sight Seeker Cultural Visitor Nature Lover Avid AthletheAction Seeker Educational Buff Sun Worshipper Sight Seeker Cultural Visitor Nature Lover Avid AthletheAction Seeker Educational Buff Sun Worshipper
- 18. ENTER 2017 Research Track Slide Number 18 Tourist Second Matchmaking Process Exploit ratings to learn specific interests of tourist Tourist TypesPROCESS2 Specific Interests Learn specific interests Tourism Object Specific Interests Specific Attributes ’dislike churches’ PROCESS 1 PROCESS 2 Tourist Tourism Object PROCESS1 MATCHMAKING Sight Seeker Cultural Visitor Nature Lover Avid AthletheAction Seeker Educational Buff Sun Worshipper Sight Seeker Cultural Visitor Nature Lover Avid AthletheAction Seeker Educational Buff Sun Worshipper
- 19. ENTER 2017 Research Track Slide Number 19 Tourist Second Matchmaking Process Exploit ratings to learn specific interests of tourist Tourist TypesPROCESS2 Specific Interests Learn specific interests Tourism Object Specific Interests Specific Attributes Tourism Ontology ’dislike churches’ PROCESS 1 PROCESS 2 Tourist Tourism Object PROCESS1 MATCHMAKING Sight Seeker Cultural Visitor Nature Lover Avid AthletheAction Seeker Educational Buff Sun Worshipper Sight Seeker Cultural Visitor Nature Lover Avid AthletheAction Seeker Educational Buff Sun Worshipper
- 20. ENTER 2017 Research Track Slide Number 20 Ratings of objects are used to learn the tourist’s specific interests The specific interests are represented as an overlay of the ontological model Second Matchmaking Process Using an ontology-based approach to model the profile Tourist Specific Interests → → PROCESS 1 PROCESS 2 PROCESS2 Imperial Furniture Collection Identify leaf concepts that describe object Assign numerical score to the leaf concepts Exploit ontological hierarchy to infer interest score for super-concepts (spreading activation) using a propagation function following Sieg et al., 2007. 1 2 3 Sieg et al. Web search personalization with ontological user profiles. In CIKM ’07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, 2007. Ontology Concept( Concept(Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept(Concept(Concept(Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept( Concept(Concept(
- 21. ENTER 2017 Research Track Slide Number 21 Implementation
- 22. ENTER 2017 Research Track Slide Number 22 Prototype for the City of Vienna
- 23. ENTER 2017 Research Track Slide Number 23 Recommended tourism objects
- 24. ENTER 2017 Research Track Slide Number 24 Detailed description of a tourism object
- 25. ENTER 2017 Research Track Slide Number 25 Evaluation
- 26. ENTER 2017 Research Track Slide Number 26 Evaluation 54 users completed the questionnaire Period of 3 months from June to September 2015 Target group Tourists who visited or plan to visit Vienna in near future Persons who know this city well (live or work here) User sessions identified from Weblog information 70 users started to fill out questionnaire 54 users completed the questionnaire → final dataset Age distribution of users within final dataset (n=54) → → →
- 27. ENTER 2017 Research Track Slide Number 27 Evaluation Users identify themselves with a mixture of factors Participants identify themselves with a mixture of all 7 factors about 75% chose at least 5 factors → users tend to select more than 1 tourist factor if they have the choice The factors Sight Seeker, Cultural Visitor and Nature Lover were most often selected → Vienna is a city destination 1 1 Number of factors selected by the users (n=54) Distribution of the tourist factors on average (n=54) 2 2 →
- 28. ENTER 2017 Research Track Slide Number 28 Evaluation 50% of the users executed 1-3 recommendation cycles This figure shows the number of recommendation cycles About 70% of the users explored the recommendations proposed by the system within 1 to 6 recommendation cycles The median has the value 3 → 50% of the users executed 1-3 cycles 1 2 1 2 → (n=54)
- 29. ENTER 2017 Research Track Slide Number 29 Evaluation About 75% of the objects were added in the first 3 cycles to the favourites On average, 8.5 ratings had been given in each user session (ca. 5.3 positive and 3.2 negative ratings), 40% ratings were stated in the 1 cycle → →
- 30. ENTER 2017 Research Track Slide Number 30 Evaluation Measuring the relevance of the recommendations As in our case no dataset is available, we decided to show the users 10 objects that were randomly chosen from the whole dataset and had not been recommended before let them decide if one or more objects are relevant for them (by adding them to the top-N list) About 70 % of the users added at maximum 1 of the randomly shown object to their favorites → Feedback from the users to the randomly shown objects, n=54 this might be an indication that they were quite satisfied with the recommendations Why had these objects not been recommended before? • Object is related to a tourist factor not selected by the user • Other objects of same category already recommended • Objects of other categories positively rated → user profile more aligned with profiles of those objects 1 2 →
- 31. ENTER 2017 Research Track Slide Number 31 Evaluation Participants’ feedback Information collected from the questionnaire→
- 32. ENTER 2017 Research Track Slide Number 32 Conclusion & Future Work
- 33. ENTER 2017 Research Track Slide Number 33 Conclusion & Future Work The goal was to close the gap between users’ needs and suppliers’ perspective by developing a matchmaking process A Web-based prototype was implemented for the city Vienna A first evaluation was conducted which aimed to investigate the feasibility of the approach Overall, the evaluation shows that the two-step matchmaking process and the feedback cycle work In future we will define automated procedures to annotate the tourism objects semantically → → → → →
- 35. ENTER 2017 Research Track Slide Number 35 Appendix
- 36. ENTER 2017 Research Track Slide Number 36 Evaluation Correlation between the tourist factors
- 37. ENTER 2017 Research Track Slide Number 37 With score propagation Evaluation Score propagation improves recommendations Propagation of user interests within the semantic model affects the position of relevant tourism objects in the Top-N list Without score propagation 3 • Objects might be relevant but not included in Top-N list3 ghotic/romanesque architecturestyle 2 • On the next positions are 4 churches as they belong to the «ghotic/architeture» style as «St. Stephen‘s Cathedral» 2 Positive Rating of St. Stephen’s Cathedral • Positive rating «St. Stephen‘s Cathedral»1 1 • Objects are now directly placed on subsequent positions within the list. Due to score propagation in the semantic model, the user profile gets more similar to the profiles of the objects (larger overlap) 2 2 Positive Rating of St. Stephen’s Cathedral • Positive rating «St. Stephen‘s Cathedral» → first position 1 1 →