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Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm

Liu, Chuang; Zhou, Wei-Xing - In: Physica A: Statistical Mechanics and its Applications 391 (2012) 22, pp. 5704-5711

Network-based recommendation algorithms for user–object link predictions have achieved significant developments in recent years. For bipartite graphs, the resource reallocation in such algorithms is analogous to heat spreading (HeatS) or probability spreading (ProbS) processes. The best...

Persistent link: https://www.econbiz.de/10011057806

Information filtering via collaborative user clustering modeling

Zhang, Chu-Xu; Zhang, Zi-Ke; Yu, Lu; Liu, Chuang; Liu, Hao - In: Physica A: Statistical Mechanics and its Applications 396 (2014) C, pp. 195-203

The past few years have witnessed the great success of recommender systems, which can significantly help users to find out personalized items for them from the information era. One of the widest applied recommendation methods is the Matrix Factorization (MF). However, most of the researches on...

Persistent link: https://www.econbiz.de/10010730333

A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains

Ramezani, Mohsen; Moradi, Parham; Akhlaghian, Fardin - In: Physica A: Statistical Mechanics and its Applications 408 (2014) C, pp. 72-84

Recommender systems seek to find the interesting items by filtering out the worthless items. Collaborative filtering is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items and...

Persistent link: https://www.econbiz.de/10010872047

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 390 (2011) 23, pp. 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...

Persistent link: https://www.econbiz.de/10011060167

Effects of high-order correlations on personalized recommendations for bipartite networks

Liu, Jian-Guo; Zhou, Tao; Che, Hong-An; Wang, Bing-Hong; … - In: Physica A: Statistical Mechanics and its Applications 389 (2010) 4, pp. 881-886

In this paper, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the cosine similarity index, the user–user correlations are obtained by a diffusion process. Furthermore, by...

Persistent link: https://www.econbiz.de/10011060618

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

Zhang, Zi-Ke; Yu, Lu; Fang, Kuan; You, Zhi-Qiang; Liu, … - In: Physica A: Statistical Mechanics and its Applications 399 (2014) C, pp. 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...

Persistent link: https://www.econbiz.de/10010744292

Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems

Huang, Zan; Zeng, Daniel D.; Chen, Hsinchun - In: Management Science 53 (2007) 7, pp. 1146-1164

We apply random graph modeling methodology to analyze bipartite consumer-product graphs that represent sales transactions to better understand consumer purchase behavior in e-commerce settings. Based on two real-world e-commerce data sets, we found that such graphs demonstrate topological...

Persistent link: https://www.econbiz.de/10009191395

Collaborative filtering with diffusion-based similarity on tripartite graphs

Shang, Ming-Sheng; Zhang, Zi-Ke; Zhou, Tao; Zhang, Yi-Cheng - In: Physica A: Statistical Mechanics and its Applications 389 (2010) 6, pp. 1259-1264

Collaborative tags are playing a more and more important role for the organization of information systems. In this paper, we study a personalized recommendation model making use of the ternary relations among users, objects and tags. We propose a measure of user similarity based on his...

Persistent link: https://www.econbiz.de/10010589156

Collaborative filtering based on multi-channel diffusion

Shang, Ming-Sheng; Jin, Ci-Hang; Zhou, Tao; Zhang, Yi-Cheng - In: Physica A: Statistical Mechanics and its Applications 388 (2009) 23, pp. 4867-4871

In this paper, by applying a diffusion process, we propose a new index to quantify the similarity between two users in a user–object bipartite graph. To deal with the discrete ratings on objects, we use a multi-channel representation where each object is mapped to several channels with the...

Persistent link: https://www.econbiz.de/10010591705

My Mobile Music: An Adaptive Personalization System for Digital Audio Players

Chung, Tuck Siong; Rust, Roland T.; Wedel, Michel - In: Marketing Science 28 (2009) 1, pp. 52-68

propose an “Adaptive Personalization System” and illustrate its implementation for digital audio players, a product category … personalization applications. A simulation study shows the Adaptive Personalization System to outperform benchmark approaches. We … implemented the Adaptive Personalization System on Palm PDAs and tested its performance with digital audio users. For actual users …

Persistent link: https://www.econbiz.de/10008787745