Research of commodity recommendation workflow based on LSH algorithm

Abstract

This paper joins the image-matching module into the hybrid recommendation system and constructs its workflow, which fuses image features and hybrid recommendation algorithms to improve diversity of advice result. SIFT feature extracted was used as the standard of image matching, improved LSH algorithm based on p-stable distribution to implement image matching and searching module for high dimensional and huge image set, then redesigned the workflow of existing commodity recommendation system combined with the proposed image matching module. This paper proposed an improved LSH algorithm based on p-stable distribution to finish image searching and matching. The experiment proved that the algorithm has a certain degree of optimization to improve recall rate and error rate at the same time, through the matching time and the length of hash table shows that the algorithm optimizes the memory utilization and search efficiency. The extracted SIFT feature of images is the only foundation we used when comparing different images at present. In the following research, we can try to use a variety of image features as the basis for matching to improve the reliability of the matching results.