LiDAR-based world models offer more structured and geometry-aware representations than their image-based counterparts. However, existing LiDAR world models are narrowly trained; each model excels only in the domain for which it was built. This raises a critical question: can we develop LiDAR world models that exhibit strong transferability across multiple domains? To answer this, we conduct the first systematic domain transfer study across three demanding scenarios: (i) outdoor to indoor generalization, (ii) sparse- to dense-beam adaptation, and (iii) non-semantic to semantic transfer. Given different amounts of fine-tuning data, our experiments show that a single pretrained model can achieve up to 11\% absolute improvement (83\% relative) over training from scratch and outperforms training from scratch in 30/36 of our comparisons. This transferability significantly reduces the reliance on manually annotated data for semantic occupancy forecasting: our method exceeds previous baselines with only 5\% of the labeled training data of prior work. We also observed inefficiencies of current generative-model-based LiDAR world models, mainly through their under-compression of LiDAR data and inefficient training objectives. To address these issues, we propose a latent conditional flow matching (CFM)-based framework that achieves state-of-the-art reconstruction accuracy using only half the training data and a compression ratio 6 times higher than that of prior methods. Our model also achieves SOTA performance on semantic occupancy forecasting while being 1.98x-23x more computationally efficient (a 1.1x-3.9x FPS speedup) than previous methods.

Sequential recommendations play a crucial role in modern recommender systems because they capture users’ dynamic interests based on his/her historical interactions. Despite the progress of existing methods in sequential recommendation, they only focus on modeling user interaction sequences but ignore multi-perspective sequences information when modeling user preferences, leading to ineffective modeling user preferences. In other words, these methods lack sufficient semantic information to effectively model the one, who considers multi-perspective factors when purchasing an item. To solve this problem, we propose a multi-perspective information and multi-task contrastive learning framework for sequential recommendation, named DIML. DIML can model user preferences by using multi-perspective information, and multi-task contrastive learning can alleviate data sparsity and enhance data quality. Specifically, we first design the information encoding layer, which enriches item semantic information from multi-perspective sequential information to model user preferences more precisely. Then we design a multi-task contrastive learning module to enhance the data quality of multi-perspective information. Finally, experiments on Taobao, JD and MovieLens datasets show that our model is better than the comparison baselines in terms of evaluation metric NDCG, HR and MRR.

Knowledge graph (KG) as auxiliary information can solve the cold-start and data sparsity problems of recommender systems.However, most existing KG-based recommendation methods focus on how to effectively encode items with that users have interacted into entities and propagate them explicitly, but neglect the relation-level and context-level modeling of collaborative signals.Therefore, it is inevitable to incorporate some unrelated entities while utilizing a propagation strategy, which may weaken part of the recommendation performance.To address this problem, we propose a novel method named Collaborative Relation Context Consistency (CRCC).Compared with other KG-based methods, we model the relation-level and context-level of collaborative signals in a fine-grained manner.Specifically, we segment the user's collaborative knowledge graph to learn related entity information separately to enrich the embedding of users.Moreover, CRCC links the consistency score between the items that users and neighbors have interacted with as the fusion basis, and then we consider the inherent popularity of items while incorporating consistent entities to enhance the embedding representation of items.Extensive experiments on three real-world datasets show that CRCC outperforms several compelling baselines in both CTR prediction and top-K recommendation.