Header menu link for other important links
Visual Similarity Using Convolution Neural Network over Textual Similarity in Content- Based Recommender System
Published in SERSC
Volume: 27.0
Issue: 1.0
Pages: 137 - 147
In today’s digital era recommendation system is widely used applications from Netflix to Amazon, from Google to Goodreads, etc. Amazon estimated about 35% incremental revenue from product recommendations every year. Majority of e-commerce companies provides a recommendation system for purchasing product. The most search engine uses text-based search criteria for finding similar products. In this article, a comprehensive study of research contents related to text and image-based recommendation system is mentioned. First, we presented various text pre-processing techniques are used to clean dataset. Second, we described various techniques used for finding textual based similarity. Third, an advanced technique likeConvolution Neural Network(ConvNets) is used for computing image-based similarity of product. Cosine similarity is calculated when word occurrence is important in textual input whereas Euclidean distance is measured when an image or semantic-based text is considered as input. Experimental results depict how recommendations using ConvNets is more exciting than usual classical techniques.
About the journal
JournalInternational Journal of Advanced Science and Technology
Open AccessYes