A Benchmark Dataset for Micro-video Thumbnail Selection

TL;DR

This paper introduces a large-scale benchmark dataset for micro-video thumbnail selection, emphasizing user interest alignment to improve click-through rates, and evaluates baseline methods to demonstrate its utility.

Contribution

It provides the first large-scale dataset focused on user-interest-based micro-video thumbnail selection, facilitating future research in this area.

Findings

Baseline methods show improved performance using the dataset.

The dataset effectively captures user preferences for thumbnail selection.

Demonstrates the importance of user interest modeling in thumbnail selection.

Abstract

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.