DEGREE CORRELATION OF BIPARTITE NETWORK ON PERSONALIZED RECOMMENDATION
نویسندگان
چکیده
منابع مشابه
Degree Correlation of Bipartite Network on Personalized Recommendation
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 im...
متن کاملDegree correlation effect of bipartite network on personalized recommendation
In this paper, by introducing a new user similarity index base on the diffusion process, we propose a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the proposed algorithm, the degree correlation between users and objects is taken into account and embedded into the similarity index by a tunable parameter. The ...
متن کاملA Parallel Personalized Recommendation Algorithm using Bipartite Graphs
BDM-NBI algorithm is proposed in this paper. It focuses on the analysis of a personalized recommendation algorithm that utilizes a weighted bipartite graph suitable for processing big data. To improve the performance of this recommendation algorithm through parallel processing techniques, a sparse matrix partitioning algorithm is then developed that uses the bipartite graph as input. Our algori...
متن کاملQuery-URL Bipartite Based Approach to Personalized Query Recommendation
Query recommendation is considered an effective assistant in enhancing keyword based queries in search engines and Web search software. Conventional approach to query recommendation has been focused on query-term based analysis over the user access logs. In this paper, we argue that utilizing the connectivity of a query-URL bipartite graph to recommend relevant queries can significantly improve...
متن کاملBipartite network projection and personal recommendation.
One-mode projecting is extensively used to compress bipartite networks. Since one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method which can be directly applied in extracting the hid...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Modern Physics C
سال: 2010
ISSN: 0129-1831,1793-6586
DOI: 10.1142/s0129183110014999