Personalized recommendation algorithm based on weighted bipartite network

2013 ◽  
Vol 32 (3) ◽  
pp. 654-657 ◽  
Author(s):  
Xin-meng ZHANG ◽  
Sheng-yi JIANG
Keyword(s):  
Personalized Recommendation ◽  
Bipartite Network ◽  
Recommendation Algorithm

A Network-Based Multi-Dimensional Recommendation Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 1218-1222
Author(s):  
Xiang Yun Xiong ◽  
Yu Chen Fu ◽  
Zhao Qing Liu
Keyword(s):  
Collaborative Filtering ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
Bipartite Network ◽  
Recommendation Algorithm ◽  
Better Than

Personalized recommendation based on bipartite network has attracted more and more attention. Its obviously better than CF (Collaborative Filtering). In this paper, we propose a multi-dimensional recommendation algorithm called BNPM. It combines item-based, user-based and category-based recommendation model to improve recommendation quality. The experimental results show that the algorithm can improve the diversity and reduce the popularity on the base of holding the accuracy of the recommendation

scholarly journals DEGREE CORRELATION OF BIPARTITE NETWORK ON PERSONALIZED RECOMMENDATION

2010 ◽  
Vol 21 (01) ◽  
pp. 137-147 ◽  
Author(s):  
JIAN-GUO LIU ◽  
TAO ZHOU ◽  
BING-HONG WANG ◽  
YI-CHENG ZHANG ◽  
QIANG GUO
Keyword(s):  
Hamming Distance ◽  
Similarity Index ◽  
Product Distribution ◽  
Personalized Recommendation ◽  
Bipartite Network ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Statistical Property ◽  
Degree Correlation ◽  
Ranking Score

In this paper, the statistical property, namely degree correlation between users and objects, is taken into account and be embedded into the similarity index of collaborative filtering (CF) algorithm to improve the algorithmic performance. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the presented algorithm, measured by the average ranking score, is improved by 18.19% in the optimal case. The statistical analysis on the product distribution of the user and object degrees indicate that, in the optimal case, the distribution obeys the power-law and the exponential is equal to -2.33. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%. Since all of the real recommendation data evolving with time, this work may shed some light on the adaptive recommendation algorithm which could change its parameter automatically according to the statistical properties of the user-object bipartite network.

scholarly journals EFFECTS OF USER'S TASTES ON PERSONALIZED RECOMMENDATION

2009 ◽  
Vol 20 (12) ◽  
pp. 1925-1932 ◽  
Author(s):  
JIAN-GUO LIU ◽  
TAO ZHOU ◽  
BING-HONG WANG ◽  
YI-CHENG ZHANG ◽  
QIANG GUO
Keyword(s):  
Power Distribution ◽  
Mass Diffusion ◽  
Numerical Results ◽  
Average Degree ◽  
Personalized Recommendation ◽  
Bipartite Network ◽  
Recommendation Algorithm ◽  
Ranking Score

In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. We argue that the initial recommendation power located on the objects should be determined by both of their degree and the user's tastes. By introducing a tunable parameter, the user taste effects on the configuration of initial recommendation power distribution are investigated. The numerical results indicate that the presented algorithm could improve the accuracy, measured by the average ranking score. More importantly, we find that when the data is sparse, the algorithm should give more recommendation power to the objects whose degrees are close to the user's tastes, while when the data becomes dense, it should assign more power on the objects whose degrees are significantly different from user's tastes.

Personal Recommendation Via Heterogeneous Diffusion on Bipartite Network

2014 ◽  
Vol 23 (03) ◽  
pp. 1450003 ◽  
Author(s):  
Chunhua Ju ◽  
Chonghuan Xu
Keyword(s):  
Numerical Analysis ◽  
Information Overload ◽  
High Accuracy ◽  
Personalized Recommendation ◽  
Bipartite Network ◽  
Dynamic Feature ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
Personal Recommendation ◽  
Benchmark Datasets

Recommender systems have proven to be an effective method to deal with the problem of information overload in finding interesting products. It is still a challenge to increase the accuracy and diversity of recommendation algorithms to fulfill users' preferences. To provide a better solution, in this paper, we propose a novel recommendation algorithm based on heterogeneous diffusion process on a user-object bipartite network. This algorithm generates personalized recommendation results on the basis of the physical dynamic feature of resources diffusion which is influenced by objects' degrees and users' interest degrees. Detailed numerical analysis on two benchmark datasets shows that the presented algorithm is of high accuracy, and also generates more diversity.

CLUSTERING EFFECT OF USER-OBJECT BIPARTITE NETWORK ON PERSONALIZED RECOMMENDATION

2010 ◽  
Vol 21 (07) ◽  
pp. 891-901 ◽  
Author(s):  
QIANG GUO ◽  
JIAN-GUO LIU
Keyword(s):  
Hamming Distance ◽  
Clustering Coefficient ◽  
Personalized Recommendation ◽  
Bipartite Network ◽  
Recommendation Algorithm ◽  
Data Set ◽  
User Similarity ◽  
Statistical Property ◽  
Ranking Score ◽  
Improved Algorithm

In this paper, the statistical property of the bipartite network, namely clustering coefficient C4 is taken into account and be embedded into the collaborative filtering (CF) algorithm to improve the algorithmic accuracy and diversity. In the improved CF algorithm, the user similarity is defined by the mass diffusion process, and we argue that the object clustering C4 of the bipartite network should be considered to improve the user similarity measurement. The statistical result shows that the clustering coefficient of the MovieLens data approximately has Poisson distribution. By considering the clustering effects of object nodes, the numerical simulation on a benchmark data set shows that the accuracy of the improved algorithm, measured by the average ranking score and precision, could be improved 15.3 and 13.0%, respectively, in the optimal case. In addition, numerical results show that the improved algorithm can provide more diverse recommendation results, for example, when the recommendation list contains 20 objects, the diversity, measured by the hamming distance, is improved by 28.7%. Since all of the real recommendation data are evolving with time, this work may shed some light on the adaptive recommendation algorithm according to the statistical properties of the user-object bipartite network.

Optimized Collaborative Filtering Recommendation Based on User’s Social Relationships

2014 ◽  
Vol 1044-1045 ◽  
pp. 1484-1488
Author(s):  
Yue Kun Fan ◽  
Xin Ye Li ◽  
Meng Meng Cao
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Digital Libraries ◽  
Social Relationship ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Personalized Recommendation ◽  
Recommendation Algorithm

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

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