Tags Recommendation Based on Social Context

Abstract

Social media platforms are not just a source of communicating with each other but also the main source of generating web content like images. Users upload the images along with the tags describing the content of the image or the context in which the image has been uploaded. But these user pro vided tags are not enough for successfully retrieving this web content due to incorrect or noisy tags . To tackle this problem of incorrect or noisy tags and in some cases the absence of tags, we develop a pipeline to assign tags to images by using image content, provided tags and social context associated with the images. In out framework we propose a deep architecture for assigning new tags to the images by using Darknet based pre-trained YOLO V3 find image content, ResNet-18 to fetch the image features and two layered neural network based Word2Vec’s skipgram to perform the task of tags generation. We use YFCC100M dataset for performing experiments. Results shows that our model performs well in performing the task of tag recommendation on YFCC100M dataset and has shown a 46% improved accuracy as compared to already available tags.