context-aware recommender systems for mobile devices

Computer Science Research Meets Business: App and Mobile Development - October 2014, Bolzano

Context-Aware Recommender Systems for Mobile Devices

Matthias Braunhofer!

Free University of Bozen - BolzanoDominikanerplatz 3 - Piazza Domenicani 3, 39100 Bozen-Bolzano

[email protected]

mailto:[email protected]


Outline

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• Introduction: What is a Recommender System?

• Mobile and Context-Aware Recommendations

• A practical example: South Tyrol Suggests

• Conclusions


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

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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:

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Basics of a Recommender System

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Input data Recommendations

Recommender System

Background data Algorithm

? ? 3

2 5 4

? 3 4


Outline

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• Conclusions



• 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

Mobile Systems and Context-Awareness (1/2)

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• 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:

Mobile Systems and Context-Awareness (2/2)

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• 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

Context-Aware Recommendations

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2-D Model → N-D Model

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3 ? 4

2 5 4

? 3 4

1 ? 1

2 5

? 3

3 ? 5

2 5

? 3

5 ? 5

4 5 4

? 3 5


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

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Outline

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• Conclusions



• 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

South Tyrol Suggests (STS)

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Intelligent Recommendations!?!

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Context Recommendations

Sunny + Summer

Sunny + Winter

Rainy


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

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• App usually shown in the top-10 search results

• Current/total installs: 165 / 712

• Avg. rating/total #: 4.77 / 13

Statistics

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Interaction with the System

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Software Architecture and Implementation

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Android Client

Spring Dispatcher Servlet Spring Controllers

Apache Tomcat Server

Service / Application Layer

JPA Entities Hibernate

Objects managed by Spring IoC Container

Database

JSON HTTP

Web Services


Recommendation Algorithm

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User modelOpenness to experienceConscientiousnessExtraversionAgreeablenessEmotional stabilityAgeGenderUser ratings

User’s contextBudgetCompanionFeelingTravel goalTransportKnowledge of travel areaDuration of stay

Place modelItem ratings

Place’s contextWeatherSeasonDaytimeWeekdayCrowdednessTemperatureDistance

Recommend places!


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

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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

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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

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Outline

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• Conclusions



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

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Questions?

Thank you.

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