User Selectable Interactive
Recommendation System In
Mobile Environment
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Design and Evaluation of Mobile Interactive Recommendation System, Slides of Applications of Computer Sciences
The challenges of digital content in mobile environments and proposes a user-selectable recommendation system to address these issues. The system allows users to choose similar groups based on their preferences and personal information, extending interactivity and reflecting social networking features. A user-selectable recommendation system flowchart, system design, and performance evaluation using the movielens dataset.
Typology: Slides
2012/2013
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User Selectable Interactive
Recommendation System In
Mobile Environment
Mobile Environments used widely
- Smart phones are popular
- Digital content is accessible more to people these days
- Variety of content has increased
- The web connectivity of the phone to the wireless networks is powerful.
Recommender System
- A user profile is created between explicit as well as implicit forms
- Divided into 3 types
- Content-based recommendation
- Collaborative filtering recommendation
- Hybrid recommendation
Collaborative filtering process
Proposed user-selectable
recommendation system
- Users can choose similar groups by themselves
- Advantages:
- Extends interactivity
- Reflects the feature of social networking an user context
- Beyond the desktop experience, this approach causes dynamic components of SGs on the user’s social context
User selectable Recommendation
System flow chart
System Design and Experiment
- MovieLens Dataset used
- Website has user data and movie data
- The data set modified by adding PG to the user data.
- Server : Apache Tomcat 5.5, JSP+XML
- Client : iPhone SDK (Xcode, Interface Builder) 3.2.1, iPhone Simulator V3.1, Android SDK(Android 2.1, Platform 2.1, API Level 7), Android DDMS
- Generator : JAVA SDK 1.6, Eclipse 3.5.2, My-Sql 5.1, Mac OS X 10.6. Snow Leopard, Windows 7
User Similar Group Design
2 step process
- Pull out PGs of all users
Pull out top 3 genres which have more than 25% rate
- Utilize the PG and user’s personal information at the same time
SG similarity calculation
- The closer the value of Pearson correlation coefficient is to 1, the higher the similarity is.
- The closer to -1, the bigger difference there is with preferences.
Performance Evaluation
- MAE is used as a measure.
- Best performance : age, gender, occupation together
- Worst performance : age, location, occupation together
- Good performance when occupation is considered.