Université de Fribourg

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering

Zeng, Wei ; Zeng, An ; Liu, Hao ; Shang, Ming-Sheng ; Zhang, Yi-Cheng

In: PLoS ONE, 2014, vol. 9, no. 10, p. e111005

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs...

Université de Fribourg

Website-oriented recommendation based on heat spreading and tag-aware collaborative filtering

Zhang, Zi-Ke ; Yu, Lu ; Fang, Kuan ; You, Zhi-Qiang ; Liu, Chuang ; Liu, Hao ; Yan, Xiao-Yong

In: Physica A: Statistical Mechanics and its Applications, 2014, vol. 399, p. 82–88

Recently, Recommender Systems has been widely applied in helping users find potentially interesting items from the era of big data. However, most of researches on this topic have focused on estimating the direct relationships between users and items, neglecting other available information. In this paper, we discuss about mining webs with information extracted from search and browser logs of...