In Recommender system we have similarity search as a key part for making efficient recommendations. Similarity search have always been a tough task in a high dimensional space. Locality Sensitive Hashing which is most suitable for extracting data in a high dimensional data (Multimedia data). The Idea of locality sensitive hashing is that it decreases the high dimensional data to low dimensions using distance functions and then store this data using hash functions which ensures that distant data is placed much further. This technique has been extended to kernelized Locality sensitive hashing (KLSH). One limitation of regular LSH is they require vector representation of data explicitly. This limitation is addressed by kernel functions. Kernel functions are capable of capturing similarity between data points. KLSH is a breakthrough in content based systems. This method takes a kernel function, a high dimensional database for data inputs and size of hash functions to be built. These kernel functions that are being used may give different degree of result precision. Hence we try to combine these kernels with a bootstrap approach to give an optimal result precision. In this paper we present the related work that has been done in locality sensitive hashing and at the end we propose algorithms for data preprocessing and query evaluation.