Context-Aware Points of Interest Suggestion with Dynamic Weather Data Management

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Weather plays an important role in tourists’ decision-making and, for instance, some places or activities must not be even suggested under dangerous weather conditions. In this paper we present a context-aware recommender system, named STS, that computes recommendations suited for the weather conditions at the recommended places of interest (POI) by exploiting a novel model-based context-aware recommendation technique. In a live user study we have compared the performance of the system with a variant that does not exploit weather data when generating recommendations. The results of our experiment have shown that the proposed approach obtains a higher perceived recommendation quality and choice satisfaction.

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  • Context-Aware Points of Interest Suggestion with Dynamic Weather Data Management

    1. 1. Context-Aware Points of Interest Suggestion with Dynamic Weather Data Management Matthias Braunhofer, Mehdi Elahi, Francesco Ricci, and Thomas Schievenin Faculty of Computer Science Free University of Bozen – Bolzano, Italy ENTER 2014 Research Track Slide Number 1
    2. 2. Agenda • Recommender Systems and Context-Awareness • Related Work • Weather-Aware Recommendations • Experimental Evaluation • Conclusions and Future Work ENTER 2014 Research Track Slide Number 2
    3. 3. Recommender Systems (RSs) • Goal: recommend new, relevant items to users based on their feedback on a sample of items (training set) – Explicit feedback (ratings) vs. implicit feedback (purchase / browsing history) • Two basic technical approaches: – Collaborative filtering (CF) – Content-based ENTER 2014 Research Track Slide Number 3
    4. 4. Context is Essential • Main idea: users can experience items differently depending on the current contextual situation (e.g., season, weather, temperature, mood) • Example: ENTER 2014 Research Track Slide Number 4
    5. 5. Context-Aware Recommender Systems (CARSs) • CARSs extend RSs beyond users and items to the contexts in which items are experienced by users – Rating prediction function is: R: Users x Items x Context  Ratings 1 3 2 3 4 5 3 1 5 2 5 2 4 5 ? 5 4 ? 4 2 3 3 4 3 3 ENTER 2014 Research Track 5 5 3 3 5 Slide Number 5
    6. 6. Using Weather in RSs • Hypothesis: weather conditions at places of interest (POIs), together with past ratings for POIs under several distinct weather conditions, can be used to improve the choice satisfaction and perceived recommendation quality • Example: ? 5 1 ? ENTER 2014 Research Track Slide Number 6
    7. 7. Agenda ENTER 2014 Research Track Slide Number 7
    8. 8. Context-Aware Matrix Factorization (CAMF) • CAMF (Baltrunas et al., 2011) extends matrix factorization by incorporating baseline parameters for contextual conditions-item pairs, which capture the deviation of items‘ ratings due to context k ˆ ruic1 ,...,ck = i + bu + ∑ bic j + pu qiT j=1 • Main limitations: ī: average rating of item i bu: baseline for user u bicj: baseline for contextual condition cj and item i pu: latent factor vector of user u qi: latent factor vector of item i – Fails to produce personalized recommendations for new users – Uses the weather condition around the user rather than the weather conditions at POIs ENTER 2014 Research Track Slide Number 9
    9. 9. Mobile CARSs (1/2) • liveCities (Martin et al., 2011): supports tourists by sending them push-based notifications when they enter a certain area and their context matches pre-defined conditions • Main limitations: - Considers only the temperature and not the weather Uses pre-defined recommendations rather than a predictive model Is only a research prototype ENTER 2014 Research Track Slide Number 10
    10. 10. Mobile CARSs (2/2) • VISIT (Meehan et al., 2013): hybrid mobile RS that uses several contextual factors (i.e., location, time, weather, social media sentiment) to support tourist‘s decisionmaking process • Main limitation: - System is only a proposal and not yet implemented ENTER 2014 Research Track Slide Number 11
    11. 11. Agenda ENTER 2014 Research Track Slide Number 12
    12. 12. STS – South Tyrol Suggests ENTER 2014 Research Track Slide Number 13
    13. 13. Weather-Aware Recommendations • Main idea: use CAMF (Baltrunas et al., 2011) as starting point and incorporate additional user attributes (i.e., gender, birth date and personality trait information) • Advantage: can produce personalized recommendations for users without ratings CAMF k ˆ ruic1 ,...,ck = i + bu + ∑ bic j + qiT ( pu + j=1 ∑ ya ) a∈A(u ) A distinct factor vector ya corresponds to each user attribute to describe a user through the set of user-associated attributes ENTER 2014 Research Track Slide Number 14
    14. 14. Phase 1: Training • The system learns the parameters offline (i.e., once every 5 minutes) by minimizing the regularized squared error on the set of known ratings K (the training set): Prediction error minb*,q*, p*,y* ∑ 2 (u,i,c1 ,...,ck )∈K k ˆ (ruic1 ,...,ck − ruic1 ,...,ck ) + λ (b + ∑ b + qi + pu + 2 u j=1 2 ic j 2 2 ∑ 2 ya ) a∈A(u ) Regularization term to avoid overfitting • Minimization performed by stochastic gradient descent ENTER 2014 Research Track Slide Number 15
    15. 15. Phase 2: Recommendation • Retrieve the weather and temperature values for the 116 municipalities of South Tyrol by querying Mondometeo – Note: This information can be cached • For each POI in the database: – Look up the POI‘s location – Assign the weather and temperature values retrieved for the closest municipality – Compute a rating prediction, considering the weather and temperature conditions along with other known contextual conditions • Recommend the top-20 POIs to the user ENTER 2014 Research Track Slide Number 16
    16. 16. Agenda ENTER 2014 Research Track Slide Number 17
    17. 17. Experimental Methodology • Live user study where our proposed weather-aware system (STS) was compared with a variant (STS-S) that has the same graphical UI but does not use the weather context when generating recommendations • We have designed a specific user task and used a questionnaire for assessing the perceived recommendation quality and choice satisfaction (Knijnenburg et al., 2012) • 54 subjects that were randomly divided in two equal groups assigned to STS and STS-S (27 each) ENTER 2014 Research Track Slide Number 18
    18. 18. User Task (1/2) • Users were supposed to: – have an afternoon off and to look for attractions / events in South Tyrol – consider the contextual conditions relevant for them and to specify them in the system settings – browse the attractions / events sections and check whether they could find something interesting for them – browse the system suggestions (recommendations), and select and bookmark the one that they believe fits their needs and wants – fill up a survey (Knijnenburg et al., 2012), which measures perceived recommendation quality and choice satisfaction ENTER 2014 Research Track Slide Number 19
    19. 19. User Task (2/2) • After this initial interaction, users had the opportunity to double check the weather conditions at the bookmarked POI by accessing on a computer the Mondometeo website – Will this knowledge influence the users choice? – Hypothesis: STS has exploited this information so the user should have already chosen items that are "compatible" with the weather • Users were then asked whether they wanted to change their preferred POI and bookmark another one, i.e., if they believed that, because of the weather conditions at the bookmarked POI, their previous choice was not anymore considered to be appropriate ENTER 2014 Research Track Slide Number 20
    20. 20. Results (1/2) Number of unsatisfied users, i.e., those that changed their bookmarked POI after having double checked the weather conditions at the POI ENTER 2014 Research Track Slide Number 21
    21. 21. Results (2/2) Choice satisfaction Perceived rec. quality Statement STS avg. STS-S avg. pvalue 1. I liked the items suggested by the system 4.0 3.7 0.20 2. The suggested items fitted my preference. 3.4 3.4 0.56 3. The suggested items were well-chosen 4. The suggested items were relevant. 3.5 3.7 3.3 3.2 0.13 0.04 5. The system suggested too many bad items. 2.9 2.7 6. I didn’t like any of the suggested items. 3.8 3.3 0.14 < 0.001 7. The items I selected were “the best among the worst”. 8. I like the item I’ve chosen. 3.1 4.6 2.8 4.3 0.20 0.02 9. I was excited about my chosen item. 4.0 3.7 0.03 10. I enjoyed watching my chosen item. 3.7 3.9 0.79 11. The items I watched were a waste of my time. 3.5 3.5 0.42 12. The chosen item fitted my preference. 3.9 4.0 0.71 ENTER 2014 Research Track Slide Number 22
    22. 22. Agenda ENTER 2014 Research Track Slide Number 23
    23. 23. Conclusions • STS = mobile CARS that recommends POIs using a set of contextual factors that include the current weather conditions at the recommended POIs • Novelty is the usage of up-to-date weather data into a matrix factorization algorithm to generate personalized context-aware recommendations • Usage of weather data significantly improves the users‘ perceived recommendation quality and choice satisfaction ENTER 2014 Research Track Slide Number 24
    24. 24. Future Work • Extended analysis – To better understand potential performance differences among the compared CARSs, which may be due to different usage of the weather contextual factors – To test our proposed weather-aware CARS with a larger number of users and a larger rating dataset ENTER 2014 Research Track Slide Number 25
    25. 25. Questions? Thank you. ENTER 2014 Research Track Slide Number 26

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