<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Collaborative filtering is one of the most successful and widely used recommendation systems. A hybrid collaborative filtering method called data sensitive recommendation based on community detection (DSRCD) is proposed as a solution to cold start and data sparsity problems in CF. Data sensitive similarity is combined with Pearson similarity to calculate the similarity between users. α is the control parameter. A predicted rating mechanism is used to solve data sparsity problem and to obtain more accurate recommendation. Both user-user similarity and item-item similarity are considered in predicted rating mechanism. β is the control parameter. Moreover, in the constructed K-nearest neighbour set, both user-community similarity and user-user similarity are considered. The target user is either in the community or has some correlation to the community. Calculating the user-community similarity can cope with cold start problem. To calculate the recommendation, movielens data sets are used in the experiments. First, parameters α and β are tested and DSRCD is compared with traditional collaborative filtering recommendation algorithm (TCF) and Zhao’s algorithm. DSRCD always has better results than TCF. When K = 30, we have better performance results than Zhao’s algorithm.</p></abstract>

Collaborative filtering is one of the most successful and widely used recommendation systems. A hybrid collaborative filtering method called data sensitive recommendation based on community detection (DSRCD) is proposed as a solution to cold start and data sparsity problems in CF. Data sensitive similarity is combined with Pearson similarity to calculate the similarity between users. α is the control parameter. A predicted rating mechanism is used to solve data sparsity problem and to obtain more accurate recommendation. Both user-user similarity and item-item similarity are considered in predicted rating mechanism. β is the control parameter. Moreover, in the constructed K-nearest neighbour set, both user-community similarity and user-user similarity are considered. The target user is either in the community or has some correlation to the community. Calculating the user-community similarity can cope with cold start problem. To calculate the recommendation, movielens data sets are used in the experiments. First, parameters α and β are tested and DSRCD is compared with traditional collaborative filtering recommendation algorithm (TCF) and Zhao’s algorithm. DSRCD always has better results than TCF. When K = 30, we have better performance results than Zhao’s algorithm.

Data Sensitive Recommendation Based On Community Detection

C. SU, Y. YU, X. XIE and Y. WANG are affiliated with the School of Computer Science and Technology, at Chongqing University of Posts and Telecommunications, Chongqing, China. X. XIE, the corresponding author, is also affiliated with Chongqing Key Lab of Mobile Communications Technology at Chongqing University of Posts and Telecommunications, Chongqing, China.

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Data Sensitive Recommendation Based On Community Detection"}

Collaborative filtering is one of the most successful and widely used recommendation systems. A hybrid collaborative filtering method called data sensitive...

Data Sensitive Recommendation Based On Community Detection

Published Online: May 16, 2015

Page range: 143 - 159

Received: Oct 21, 2014

Accepted: Mar 06, 2015

DOI: https://doi.org/10.1515/fcds-2015-0010

Keywords
Community detection, Collaborative filtering algorithm, Cold start, Predicted rating mechanism

© Chang Su et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Data Sensitive Recommendation Based On Community Detection

Published Online: May 16, 2015

Page range: 143 - 159

Received: Oct 21, 2014

Accepted: Mar 06, 2015

DOI: https://doi.org/10.1515/fcds-2015-0010

KeywordsCommunity detection, Collaborative filtering algorithm, Cold start, Predicted rating mechanism

© Chang Su et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Community detection, Collaborative filtering algorithm, Cold start, Predicted rating mechanism