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

Information filtering in sparse online systems: recommendation via semi-local diffusion

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

In: PLoS ONE, 2013, vol. 8, no. 11, p. e79354

With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users’ decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot...

Université de Fribourg

Membership in social networks and the application in information filtering

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

In: The European Physical Journal B, 2013, vol. 86, no. 9, p. 1–7

During the past few years, users’ membership in the online system (i.e. the social groups that online users joined) were widely investigated. Most of these works focus on the detection, formulation and growth of online communities. In this paper, we study users’ membership in a coupled system which contains user-group and user-object bipartite networks. By linking users’ membership...

Université de Fribourg

Negative ratings play a positive role in information filtering

Zeng, Wei ; Zhu, Yu-Xiao ; Lü, Linyuan ; Zhou, Tao

In: Physica A: Statistical Mechanics and its Applications, 2011, vol. 390, no. 23-24, p. 4486-4493

The explosive growth of information asks for advanced information filtering techniques to solve the so-called information overload problem. A promising way is the recommender system which analyzes the historical records of users’ activities and accordingly provides personalized recommendations. Most recommender systems can be represented by user-object bipartite networks where users can...

Université de Fribourg

Empirical comparison of local structural similarity indices for collaborative-filtering-based recommender systems

Zhang, Qian-Ming ; Shang, Ming-Sheng ; Zeng, Wei ; Chen, Yong ; Lü, Linyuan

In: Physics Procedia, 2010, vol. 3, no. 5, p. 1887-1896

Collaborative filtering is one of the most successful recommendation techniques, which can effectively predict the possible future likes of users based on their past preferences. The key problem of this method is how to define the similarity between users. A standard approach is using the correlation between the ratings that two users give to a set of objects, such as Cosine index and Pearson...

Université de Fribourg

Relevance is more significant than correlation: Information filtering on sparse data

Shang, Ming-Sheng ; Lü, Linyuan ; Zeng, Wei ; Zhang, Yi-Cheng ; Zhou, Tao

In: Europhysics Letters, 2009, vol. 88, no. 6, p. 68008

In some recommender systems where users can vote objects by ratings, the similarity between users can be quantified by a benchmark index, namely the Pearson correlation coefficient, which reflects the rating correlations. Another alternative way is to calculate the similarity based solely on the relevance information, namely whether a user has voted an object. The former one uses more information...