The Social Hashtag Recommendation for Image and Video Using Deep Learning Approach

There has been a lot of interest in the recent year in recommending hashtags for images/videos or posts on social media. Several researchers have researched the impact from numerous perspectives. In this paper, we enhance tag recommendation by recommending suitable hashtags considering both contents of the image/video and users’ history of the hashtag. On the social media image/video-sharing websites (such as Facebook, Instagram, Flickr, and Twitter), users can upload images or videos and tag them with tags. The proposed method generates candidate keywords, i.e., hashtag by combining techniques for textual tags, image and video activity/object recognition content, and acoustic data. To this end, this paper examines different methodologies that associate information that is multi-modal and suggests hashtags for image or video uploader users to generate tags for their images or videos. Although a substantial amount of study has been carried out on item/product recommendations for E-commerce websites, video recommendations for YouTube and Netflix, and friend suggestions on social media websites, research has not been carried out as much on hashtag recommendations for images/video on social media platform/app/websites, which have now turned out to be a vital role of these social media platforms. Here, in this paper, glance at hashtag recommendations for image/video has been carried.