B-Rank: A top N Recommendation Algorithm

Details B-Rank: A top N Recommendation Algorithm B-Rank: A top N Recommendation Algorithm

2009-08-19

arxiv.org/abs/0908.2741v2

Physics

Data Analysis, Statistics and Probability

6 pages, 1 figure

Scientific paper

In this paper I propose B-Rank, an efficient ranking algorithm for recommender systems. B-Rank is based on a random walk model on hypergraphs. Depending on the setup, B-Rank outperforms other state of the art algorithms in terms of precision, recall ~(19% - 50%) and inter list diversity ~(20% - 60%). B-Rank captures well the difference between popular and niche objects. The proposed algorithm produces very promising results for sparse and dense voting matrices. Furthermore, I introduce a recommendation list update algorithm to cope with new votes. This technique significantly reduces computational complexity. The algorithm implementation is simple, since B-Rank needs no parameter tuning.

Affiliated with

Also associated with

No associations

Rating

B-Rank: A top N Recommendation Algorithm does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with B-Rank: A top N Recommendation Algorithm, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and B-Rank: A top N Recommendation Algorithm will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-216369