Context-Aware Recommender Systems for Mobile Devices

1. Context-Aware Recommender Systems for Mobile Devices Matthias Braunhofer ! Free University of Bozen - Bolzano Dominikanerplatz 3 - Piazza Domenicani 3, 39100 Bozen-Bolzano mbraunhofer@unibz.it Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano

2. Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano Outline 2 • Introduction: What is a Recommender System? • Mobile and Context-Aware Recommendations • A practical example: South Tyrol Suggests • Conclusions

3. Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano Outline 2 • Introduction: What is a Recommender System? • Mobile and Context-Aware Recommendations • A practical example: South Tyrol Suggests • Conclusions

4. Information Overload • The Internet is only 23 years old, but already every 60 seconds 1,500 blog entries are created, 98,000 tweets are shared, and 600+ videos are uploaded to YouTube - BBC News, August 2012 • By 2015, media consumption will raise to 74 GB a day - UCSD Study, 2013 Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 3

5. Solution: Recommender Systems • Recommender systems are (web, mobile, standalone) tools that are becoming more and more popular for supporting the user in finding and selecting relevant products, services, or information • Examples: Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 4

6. Basics of a Recommender System Recommender System Background data Algorithm Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 5 Input data Recommendations ? ? 3 2 5 4 ? 3 4

7. • Introduction: What is a Recommender System? Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano Outline 6 • Mobile and Context-Aware Recommendations • A practical example: South Tyrol Suggests • Conclusions

8. Mobile Systems and Context-Awareness (1/2) • Mobile devices have exceeded PC sales for the first time in 2012 - Digital Trends, February 2012 • Many people have moved several activities (e.g., Internet browsing, content consumption, engaging with apps and services) from their PC to their smartphone or tablet • Smaller screens and (virtual) keyboards require users to make more effort to search and get what they need • Users are often forced to use the device in particular situations or in stressful moments Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 7

9. Mobile Systems and Context-Awareness (2/2) • By exploiting the information extracted from the user’s context (e.g., season, weather, temperature, mood) it is possible to find the right items to recommend in that specific moment • Example: Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 8

10. Context-Aware Recommendations • Three types of architecture for using context in recommendation (Adomavicius and Tuzhilin, 2008): • Contextual pre-filtering: context is used to select relevant portions of data • Contextual post-filtering: context is used to filter/constrain/re-rank final set of recommendations • Contextual modelling: context is used directly as part of learning preference models Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 9

11. 2-D Model → N-D Model Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 10 3 ? 4 2 5 4 ? 3 4 1 ? 1 2 5 ? 3 3 ? 5 2 5 ? 3 5 ? 5 4 5 4 ? 3 5

12. Challenges • Identification of contextual factors (e.g., weather) that are worth considering when generating recommendations • Acquisition of a representative set of contextually-tagged ratings • Development of a predictive model for predicting the user’s ratings for items under various contextual situations • Design and implementation of a human-computer interaction (HCI) layer on top of the predictive model Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 11

14. South Tyrol Suggests (STS) • Let’s look at a concrete example - STS - our Android app on Google Play that supports the following functionalities: • Intelligent recommendations for POIs in South Tyrol that are adapted to the current contextual situation of the user (e.g., weather, location, parking status) • Eco-friendly routing to selected POIs by public or private transportation means • Search for various types of POIs across different data sources (i.e., LTS, Municipality of Bolzano) • User personality questionnaire for preference elicitation support Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 13

15. Intelligent Recommendations!?! Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 14 Context Recommendations Sunny + Summer Sunny + Winter Rainy

20. Why Android? • Ultimate goal: support both Android and iOS platforms • Since we couldn’t afford to simultaneously develop for iOS and Android, we decided Android to target for an initial release: • Developers (UNIBZ students) are familiar with Android • Very easy to publish to Google Play Store • No concrete tablet plans as of yet • Android dominates the global smartphone market - 84.7% market share during Q2 2014 - IDC, August 2014 Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 15

21. • App usually shown in the top-10 search results • Current/total installs: 165 / 712 • Avg. rating/total #: 4.77 / 13 Statistics Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 16

24. Interaction with the System Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 17

35. Software Architecture and Implementation Apache Tomcat Server Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 18 Android Client Spring Dispatcher Servlet Spring Controllers Service / Application Layer JPA Entities Hibernate Objects managed by Spring IoC Container Database JSON HTTP Web Services

40. Recommendation Algorithm Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 19 User model Openness to experience Conscientiousness Extraversion Agreeableness Emotional stability Age Gender User ratings User’s context Budget Companion Feeling Travel goal Transport Knowledge of travel aDrueraation of stay Place model Item ratings Place’s context Weather Season Daytime Weekday Crowdedness Temperature Distance Recommend places!

41. Evaluation • Several user studies involving > 100 test users • Test users were students, colleagues, or other people recruited at the Klimamobility Fair and Innovation Festival • Obtained results: • Recommendation model successfully exploits the weather conditions at POIs and leads to a higher user’s perceived recommendation quality and choice satisfaction • Implemented active learning strategy increases the number of acquired ratings and recommendation accuracy • Users largely accept to follow the supported human-computer interaction and find the user interface clear, user-friendly and easy to use Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 20

42. A/B Testing • Purpose: reliably determine which system version (A or B) is more successful • Prerequisite: you have a system up and running • Some users see version A, which might be the currently used version • Other users see version B, which is new and improved in some way • Evaluate with “automatic” measures (time spent on screens, clicks on a button, etc.) or surveys (SUS, CSUQ, etc.) • Allows to see if the new version (B) does outperform the existing version (A) • Probably the most reliable evaluation methodology Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 21

43. Planned Features • Integration of a multimodal routing system • Usage of Facebook profile • Allow users to plan future visits to POIs • Provide users with push recommendations • Exploit activity and emotion information inferred from wearable devices in the recommendation process Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 22

45. Conclusions • Recommender systems have become increasingly important as a tool to overcome the information overload problem • The mobile scenario opens new opportunities but also new challenges to the application of recommender systems • The future will see the development of virtual personal assistants that will watch users’ actions - what they read, what they ignore, whom they listen to, what they say, which meetings they go to and which they skip, etc. - to learn what they might do to make those users more productive and satisfied Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano 24

46. Questions? Thank you. Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano