The field of computer science is witnessing significant developments in sorting algorithms and recommender systems. Researchers are focusing on improving the efficiency and stability of sorting algorithms, such as Mergesort, to handle large amounts of data. Additionally, there is a growing interest in developing recommender systems that can effectively model user preferences and provide accurate recommendations.

Recent studies have proposed novel approaches to address the challenges in these areas. For instance, the development of in-place Mergesort algorithms that are stable and efficient has the potential to improve the performance of various applications. Similarly, the use of discrete diffusion models in recommender systems has shown promising results in alleviating the sparsity of user preference data.

Noteworthy papers in this area include:

• A novel in-place Mergesort algorithm that is stable and efficient, with a running time of O(n log^2 n) and optimal number of O(n log n) comparisons.
• PreferGrow, a discrete diffusion-based recommender system that models preference ratios by fading and growing user preferences over the discrete item corpus, demonstrating consistent performance gains over state-of-the-art diffusion-based recommenders.