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N-screen aware multicriteria hybrid recommender system using weight based subspace clustering.
Farman Ullah1, Ghulam Sarwar1, Sungchang Lee1
1Department of Information & Communication, Korea Aerospace University, Goyang 412-791, Republic of Korea.
Thescientificworldjournal
|August 26, 2014
Summary
This study introduces an N-screen aware recommender system that enhances user experience by considering device attributes and usage patterns. The system improves recommendation accuracy and addresses common issues like sparsity and cold start.
Area of Science:
- Computer Science
- Human-Computer Interaction
Background:
- N-screen services allow users to access content across multiple devices.
- Existing recommender systems do not account for diverse device capabilities or user context.
Purpose of the Study:
- To develop an N-screen aware recommender system for improved user experience.
- To address limitations of current recommender systems in multi-device environments.
Main Methods:
- Introduced a user device profile collaboration agent, manager, and control server.
- Developed a multicriteria hybrid framework incorporating device information, user preferences, and demographics.
- Proposed an individual feature and subspace weight based clustering (IFSWC) algorithm.
Main Results:
- The system significantly improves recommendation accuracy, precision, and scalability.
- Effectively addresses sparsity and cold start issues in N-screen environments.
- Simulation results validate the system's effectiveness.
Conclusions:
- The proposed N-screen aware recommender system enhances user experience by personalizing content delivery across devices.
- The IFSWC algorithm and hybrid framework offer a robust solution for multi-device recommendation challenges.
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