Short-firm video (SFV) has been exploding on digital platforms recently. The vast amount of videos and fast-evolving trends on digital platforms pose technical challenges in making personalized recommendations. In this work, we introduce a new pure exploration problem on SFV platforms. We propose an adaptive data acquisition method, called Adaptive Acquisition Tree (AAT), to jointly account for heterogeneity in user preferences and high-dimensional product characteristics. We adaptively divide users based on preference similarity and then learn a personalized transductive bandit policy that can be used on partially or even unobserved arms to accommodate the fast-evolving and emerging trends on SFV platforms. We analytically characterize the prediction error, which is determined by both the sample size and the impurity of parameters within a group. We further derive the sample complexity for identifying an optimal set for a single user and for all users. We evaluate the algorithm via numerical experiments on data collected from the NetEase platform. Our result demonstrates that the proposed policy, compared with several state-of-the-art benchmarks, performs significantly better in four transductive scenarios for both spotlight recommendation (i.e., best-arm identification) and top-K recommendations. With the potential to improve the expected view time by 20-25\%, our method pertains to both academic and practical values, given the increasing popularity of short-form videos and, more broadly, online user-content generation platforms.