A Benchmark Dataset for Micro-video Thumbnail Selection
This work addresses the need for better thumbnail selection in micro-video platforms to improve user engagement, but it is incremental as it builds on prior efforts by focusing on user interests.
The paper tackles the problem of selecting micro-video thumbnails that align with user interests to increase click-through rates, resulting in the creation of a large-scale benchmark dataset and demonstration of its effectiveness through baseline evaluations.
The thumbnail, as the first sight of a micro-video, plays a pivotal role in attracting users to click and watch. Although several pioneer efforts have been dedicated to jointly considering the quality and representativeness for selecting the thumbnail, they are limited in exploring the influence of users` interests. While in the real scenario, the more the thumbnails satisfy the users, the more likely the micro-videos will be clicked. In this paper, we aim to select the thumbnail of a given micro-video that meets most users` interests. Towards this end, we construct a large-scale dataset for the micro-video thumbnails. Ultimately, we conduct several baselines on the dataset and demonstrate the effectiveness of our dataset.