| Summary: | 碩士 === 國立成功大學 === 工程科學系 === 104 === Recently, it is more and more difficult to make a decision because we receive a large amount of information through the Internet every day. Therefore, the recommender system becomes more and more popular. It can help users making decisions effectively by providing suitable suggestions to users, and those suggestions were processed according to users’ browsing history and transaction. However, most of the content-based recommended systems are designed for text-based content, such as news and books. The reason is that image-based content cannot be extracted automatically and cannot learn the attributes by text mining or natural language processing.
In this research, we propose a method for personal recommender system based on image recognition by deep learning. To get optimal model and parameters, the system uses the public clothing database — has over 80,000 images and 15 categories — to train the deep learning network. We create a standard content-based recommender system by using training model and similar algorithm including content analyzer, profile learners, and filter component. Our result has 54.6% accuracy rate, higher than the result of random forest by 13.3%. In this experiment, we use the product database of Uniqlo and Lativ, taking 26 pictures from 5 testers and generating a purchasing recommendation for the testers. Most of the testers commented that the top three recommended items perfect matched their needs. It shows that this system is very efficient.
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