South Tyrol Suggests - STS

1. South Tyrol Suggests - STS Matthias Braunhofer and Francesco Ricci Free University of Bozen - Bolzano Faculty of Computer Science {mbraunhofer,fricci}@unibz.it

2. STS (South Tyrol Suggests) • 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 2

3. Intelligent Recommendations!?! 3 Context Recommendations Sunny + Summer Sunny + Winter Rainy

8. Statistics 4 • App usually shown in the top-10 search results • Total installs: 891 • Avg. rating/total #: 4.60 / 15

9. Statistics 4 • App usually shown in the top-10 search results • Total installs: 891 • Avg. rating/total #: 4.60 / 15

10. Software Architecture & Implementation 5 Android Client Presentation Layer Apache Tomcat Server Objects Managed by Spring IoC Container Spring Dispatcher Servlet Spring Controllers JSON HTTP Update Handling Session Handling JPA Entities Hibernate Service / Application Layer Database Web Services

11. Interaction with the System 6

22. Recommendation Task • Core computations of recommender systems: • Collection of user preferences (ratings): collect user feedback (ratings) on items to learn the user preferences • Rating prediction: a model must be built to predict ratings for items not currently rated by the user • Item selection: a model must be built that selects the N most relevant items for the user 7

23. Rating Prediction Algorithm (1/2) • Rating prediction algorithm is based on Matrix Factorization (MF) • Basic idea of MF: predict unknown ratings by discovering some latent features that determine how a user rates an item; features associated with the user should match with the features associated with the item 8 r11 r12 r13 r14 r21 r22 r23 r24 r31 r32 r33 r34 r41 r42 r43 r44 r51 r52 r53 r54 a b c x y z= r q p 5 x 4 matrix 5 x 3 matrix 3 x 4 matrix r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z ȓui = qi Tpu Rating prediction User preference factor vector Item preference factor vector

24. Rating Prediction Algorithm (2/2) • Context-Aware Matrix Factorization (CAMF): extends standard MF by incorporating baseline parameters for each contextual condition and item pair to capture the deviation of the rating for an item produced by the contextual conditions 9 Item average User bias Context bias Preference factor (user- demographics-item-interaction) Rating = 4 ˆruic1,...,ck = qi T (pu + ya ) a∈A(u) ∑ + i + bu + bicj j=1 k ∑ cj contextual condition j qi latent factor vector of item i pu latent factor vector of user u A(u) set of user attributes ya latent factor vector of user attribute a ī average rating of item i bu baseline for user u bicj baseline for item-contextual condition icj Captures the rating deviation due to context (e.g., weather, parking)

25. 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 10

26. 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 11

27. 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 12

28. Questions? Thank you.