Image Recommendation Based on ANOVA Cosine Similarity

Online shopping is very popular and has grown exponentially due to revolution in digitization. It is a fundamental requirement all the search engines to provide recommendation to identify user preferences. In this paper, we have proposed an algorithm recommend images based on ANOVA Cosine Similarity where text and visual features are integrated to fill the semantic gap. synonyms of each term are computed using ANOVA p-value by considering image visual features on text-based search. queries are generated for user input query and text based search is performed to get initial result set. Pair-wise image similarity is computed for recommendation of images. Experiments are conducted on product images crawled from domain site. Experiments results shows that the ACSIR outperforms iLike method by providing more relevant products to the user query