Product Recommendation Based on Image Content
Abstract: The technology used to recommend products suffers from the problems such as inability to recommend products unrated by neither target user nor his similar users and ignoring the previous consumptions of users. To address the problems mentioned above, a new recommendation method based on image content is proposed. This recommendation method describes the product by the color, shape and textual of product images, and is able to recommend new products and the products unrated by target user and his similar users by considering the similarities of images and users, and reflects the impact on user’s interest by user’s consumption. Finally, the system gives the recommendation result based on the static feature of the items bought by users. We test our algorithm on an image dataset built by ourselves. The experimental results show that the new method has better capacity for recommendation compared to user-based collaborative filtering, item-based collaborative filtering and two-way method.
文章引用: 夏利民 , 彭东亮 , 张 伟 (2012) 基于图像内容的物品推荐。 软件工程与应用， 1， 1-6. doi: 10.12677/sea.2012.11001
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