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User Selectable Interactive Recommendation System In Mobile Environment

This paper presents a user-selectable recommendation system tailored for mobile environments, addressing the limitations of digital content access on smartphones. By allowing users to choose their similar groups and content preferences, the system enhances interactivity and aligns with social networking features. It leverages explicit and implicit user profiles through content-based, collaborative filtering, and hybrid recommendations. The system's design is evaluated using the MovieLens dataset, showcasing its potential for personalization and scalability in ubiquitous computing scenarios. Future enhancements aim to improve system effectiveness.

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User Selectable Interactive Recommendation System In Mobile Environment

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  1. User Selectable Interactive Recommendation System In Mobile Environment Jung-Min Oh NamMee Moon Presented by: Dhanashree Lale

  2. 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.

  3. Limitations of digital content for mobile environment • Screen too small • Information retrieval limited by hardware • Causes an information need for users to have a personalized service and relevant information • To answer all these challenges recommendation systems are developed

  4. 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

  5. Collaborative filtering process

  6. Performance Evaluation

  7. 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

  8. User selectable Recommendation System flow chart

  9. User selectable Recommendation System • PG the preference Genre is derived by analyzing the content the user has watched and/or rated already. • SG the similar groups are derived by cross analysis of PG analysis of group based on user’s personal information

  10. 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 10.6.3 Snow Leopard, Windows 7

  11. 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

  12. Cases of SG based on user choice

  13. 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.

  14. 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.

  15. Prototype

  16. Conclusion • In this paper, a flexible and interactive recommendation system in terms of Web 2.0 using collaborative filtering is described. • This paper finds it to be significant that in a mobile environment a user can select and change the interesting group easily to affect their own recommendation. • This proposed system has scalability to ready a future ubiquitous environment. • Further improvements still required to make them effective

  17. Thank you!

  18. Questions are welcome!

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