Paper Info
Title
Towards Efficient Collaborative Filtering Using Parallel Graph Model and Improved Similarity Measure
Abstract
The recommender system can partially alleviate user's difficulty on information filtering and discover valuable information for the active user. Collaborative filtering has been widely used for recommendation. Because of facing the large-scale and sparse user-item rating matrix, high precision and better performance are always the big challenges for recommender system. In this paper, we model the rating data as a graph. We also make an improvement for similarity measure. Through the message propagation in the graph, candidate similar users will be selected firstly before the calculation of users similarity. Based on the topology of the user-item bipartite graph, the candidate items can be quickly located through the shortest path algorithm. Building on the parallel graph framework, the proposed approach is efficient and scalable. The experiments on real world cluster show that compared with the traditional collaborative filtering, our approach can better improve recommendation precision and the rating accuracy. It also has good scalability and real-time performance.
Year
DOI
Venue
2016
10.1109/HPCC-SmartCity-DSS.2016.0036
2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
Keywords
Field
DocType
collaborative filtering,social networks,parallel graph model,shortest path
Recommender system,Data mining,Algorithm design,Collaborative filtering,Similarity measure,Computer science,Bipartite graph,Filter (signal processing),Scalability,Distributed computing,Dijkstra's algorithm
Conference
ISBN
Citations 
PageRank 
978-1-5090-4298-2
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Huanyu Meng100.68
Zhen Liu283.48
Fang Wang301.69
Jiadong Xu4196.71