The field of video recommendation and analysis is moving towards addressing the challenges of cold-start and bias in recommender systems, as well as mitigating prediction drift in social media popularity prediction. Researchers are exploring innovative approaches such as multimodal embeddings, feature clustering, and expansion to overcome these challenges. Additionally, there is a growing interest in understanding the diffusion dynamics and predictive factors of online video content, particularly in the context of short-form videos.

Noteworthy papers include: The paper on Short-Form Video Recommendations with Multimodal Embeddings, which demonstrates the effectiveness of using a fine-tuned multimodal vision-language model to overcome cold-start and bias challenges. The paper on Anchoring Trends, which proposes an Anchored Multi-modal Clustering and Feature Generation framework to mitigate social media popularity prediction drift. The paper on Online hierarchical partitioning of the output space in extreme multi-label data stream, which introduces an online multi-label learning framework that incrementally partitions the label space into disjoint, correlated clusters. The paper on Half-life of Youtube News Videos, which investigates the early-stage diffusion patterns and dispersion rate of news videos on YouTube and provides a quantitative evaluation of the 24-hour half-life. The paper on Efficient Data Retrieval and Comparative Bias Analysis of Recommendation Algorithms for YouTube Shorts and Long-Form Videos, which develops an efficient data collection framework to analyze YouTube's recommendation algorithms and uncovers distinct behavioral patterns in recommendation algorithms across short-form and long-form videos.