Recently, extensive attention from researchers has been paid to users referring to product review comments when choosing products while shopping online. These shoppers are also more frequently demanding a visual search facility to identify similar or identical products based on images they input. In this paper, the authors propose a product recommendation method to support a visual product search that combines the similarities of both visual and textual information to recommend products with a high level of satisfaction. The authors first utilize the image-based recognition method to calculate the similarities between user-inputted images and product images based on their SIFT features and the surrounding text. Next, to select satisfying products, the authors perform sentiment analysis on product reviews and combine this with users' repeat purchase behavior to recommend products that have a high level of satisfaction rating. Finally, the authors evaluate and discuss the proposed method using real e-commerce data.

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