Advertisement and Document Recommendation based on Content in the Image

Abstract

In recent years, more and more images have been uploaded and published on the web. Along with text web pages, images have become an important media for various social media platforms to place relevant advertisements. However, conventional image advertising primarily uses text content rather than image content to match relevant advertisements. There is no existing system to automatically monetize the opportunities brought by individual image. As a result, the advertisements are only generally relevant to the entire web page rather than specific to images it contained. To overcome this, advertisements in the proposed system are recommended based on images. The objects are detected from the image using TensorFlow API Model and based on those objects (keywords) advertisements are recommended. An additional application is provided, were based on the detected objects (keywords) relevant documents are recommended using Term Frequency-Inverse Document Frequency algorithm. From the experimental results, it is seen that system could recognize over 90 percent of objects and could recommend relevant advertisement with mean average precision of 0.66.