Social Network and Tag Sources Based Augmenting Collaborative Recommender System

By Tinghuai Ma, Jinjuan Zhou, Meili Tang, Yuan Tian, A. Al-Dhelaan, Mznah Al-Rodhaan, Sungyoung Lee. IEICE Trans. Inf. Syst., 2015

313 citations recorded by the source corpus.

SUMMARY Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today’s online business world. However, most recommendation approaches exploit only a single source of input data and su ff er from the data sparsity problem and the cold start problem. To improve recommendation accuracy in this situation, additional sources of information, such as friend relationship and user-generated tags, should be incorporated in recommendation systems. In this paper, we revise the user-based collaborative filtering (CF) technique, and propose two recommendation approaches fusing user-generated tags and social relations in a novel way. In order to evaluate the performance of our approaches, we compare experimental results with two baseline methods: user-based CF and user-based CF with weighted friendship similarity using the real datasets (Last.fm and Movielens). Our experimental results show that our methods get higher accuracy. We also verify our methods in cold-start settings, and our methods achieve more precise recommendations than the compared approaches.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/c3711deaf1c6bcd8c163f0f7de7b45de5e72a10c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Philip S. Yu
2. Ludmila I. Kuncheva

In Computer Vision and Pattern Recognition

1. Philip S. Yu
2. Zhoujun Li
3. Xiaoming Zhang
4. Senzhang Wang
5. Lifang He
6. Charu C. Aggarwal
7. Pavel Berkhin

In Information Systems

1. Philip S. Yu
2. Zhoujun Li
3. Senzhang Wang
4. Lifang He
5. Yongfeng Zhang
6. Jiawei Han
7. B. Liu
8. Bing Liu

Citations in the corpus

• Firefly algorithm with neighborhood attraction (2017, background): “Recommender systems (RS) can provide users with the required information [31].”
• Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data (2017, methodology): “Currently, social media data is widely used to improve many online applications [Ma et al. 2015].”