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Context-Aware Points of Interest Suggestion with Dynamic Weather

Data Management

Matthias Braunhofer, Mehdi Elahi, Francesco Ricci, and Thomas Schievenin

Faculty of Computer ScienceFree University of Bozen – Bolzano, Italy

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Agenda

• Recommender Systems and Context-Awareness

• Related Work

• Weather-Aware Recommendations

• Experimental Evaluation

• Conclusions and Future Work

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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

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Context is Essential

• Main idea: users can experience items differently depending on the current contextual situation (e.g., season, weather, temperature, mood)

• Example:

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• 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 1

2 5 2

? 3 3

3 5

2 5 3

? 3 3

Context-Aware Recommender Systems (CARSs)

3 4

2 5 4

3 4

5 5

4 5 4

3 5

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1

• 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?

Using Weather in RSs

?

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Agenda

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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

• Main limitations:– Fails to produce personalized recommendations for new users– Uses the weather condition around the user rather than the

weather conditions at POIs

ī: average rating of item ibu: baseline for user ubicj

: baseline for contextual

condition cj and item ipu: latent factor vector of user uqi: latent factor vector of item i

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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

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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 decision-making process

• Main limitation:- System is only a proposal and

not yet implemented

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Agenda

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STS – South Tyrol Suggests

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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

A distinct factor vector ya corresponds to each user attribute to describe a user through the set of user-associated attributes

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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):

• Minimization performed by stochastic gradient descentRegularization term to avoid overfitting

Prediction error

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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

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Agenda

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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)

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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

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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

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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

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Results (2/2)Statement

STS avg.

STS-S avg.

p-value

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 3.5 3.3 0.13

4. The suggested items were relevant. 3.7 3.2 0.04

5. The system suggested too many bad items. 2.9 2.7 0.14

6. I didn’t like any of the suggested items. 3.8 3.3<

0.001

7. The items I selected were “the best among the worst”.

3.1 2.8 0.20

8. I like the item I’ve chosen. 4.6 4.3 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

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Agenda

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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

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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

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Questions?

Thank you.