Planning scenarios involving user pre-specified items present themselves frequently in recommender system domains. Although next-item and next-basket recommendation has been a focus of prior research, multiple consecutive item or basket approaches are needed for planning. No prior work has leveraged pre-specified future reference items to improve this type of challenging consecutive prediction task at inference time. PLAN-BERT is the first to accommodate this general planning scenario. It does so by contributing novel modifications that take inspiration from the masked training and contextual embedding of self-attention models. To test the model, we use the domain of student academic degree planning, in which students’ past course histories and future pre-specified courses of interest are used to fill in the remainder of their curriculum. Our offline analyses consist of 15 million historic course enrollments at 20 institutions and an online evaluation conducted at one of the institutions. Our results show that PLAN-BERT outperforms existing models including BERT, BiLSTM, and a UserKNN baseline, with small numbers of future reference items substantially improving accuracy. Significant results from our online evaluation show PLAN-BERT to be strongest in students' perceptions of personalization.

Learning generalizable, transferable, and robust representations for molecule data has always been a challenge. The recent success of contrastive learning (CL) for self-supervised graph representation learning provides a novel perspective to learn molecule representations. The most prevailing graph CL framework is to maximize the agreement of representations in different augmented graph views. However, existing graph CL frameworks usually adopt stochastic augmentations or schemes according to pre-defined rules on the input graph to obtain different graph views in various scales (e.g. node, edge, and subgraph), which may destroy topological semantemes and domain prior in molecule data, leading to suboptimal performance. Therefore, designing parameterized, learnable, and explainable augmentation is quite necessary for molecular graph contrastive learning. A well-designed parameterized augmentation scheme can preserve chemically meaningful structural information and intrinsically essential attributes for molecule graphs, which helps to learn representations that are insensitive to perturbation on unimportant atoms and bonds. In this paper, we propose a novel Molecular Graph Contrastive Learning with Parameterized Explainable Augmentations, MolCLE for brevity, that self-adaptively incorporates chemically significative information from both topological and semantic aspects of molecular graphs. Specifically, we apply deep neural networks to parameterize the augmentation process for both the molecular graph topology and atom attributes, to highlight contributive molecular substructures and recognize underlying chemical semantemes. Comprehensive experiments on a variety of real-world datasets demonstrate that our proposed method consistently outperforms compared baselines, which verifies the effectiveness of the proposed framework. Detailedly, our self-supervised MolCLE model surpasses many supervised counterparts, and meanwhile only uses hundreds of thousands of parameters to achieve comparative results against the state-of-the-art baseline, which has tens of millions of parameters. We also provide detailed case studies to validate the explainability of augmented graph views.CCS CONCEPTS• Mathematics of computing → Graph algorithms; • Applied computing → Bioinformatics; • Computing methodologies → Neural networks; Unsupervised learning.

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Individual trajectory generation plays an important role in simulation tasks, which reconstructs fine-grained mobility behaviors that can be used to evaluate epidemic risks, congestion risks, or commercial profit. Previous researches adopt Newton mechanic-based particle model as their core algorithm, such as Social Force model. However, real-world human mobility behaviors hardly follow particle model, especially in interior scene where interactions between pedestrians and environments matter. In this paper, we propose a Social Force-based trajectory simulator for Interior Scenario that improves both trajectory quality and generation speed for interior scenarios. First, we introduce prior scene knowledge to guide the generation process, where pedestrians are armed with exploration behaviors that follows group-level distribution. It provides more flexibility to simulate complicated human behaviors rather than straight line movements, generating high quality individual trajectories. Experiments show the correlation between the aggregated population distribution of generated trajectories and ground-truth distribution is improved by \(11.84\% \) by our method. Second, we optimize the algorithm procedure by introducing a caching mechanism for tensorized intermediate values, alone with graph processing unit-based implementation. Compared with baseline Social Force model, we reduced the time consumption by \(95\% \) . More importantly, based on our simulation paradigm, we quantitatively evaluate several common mobility interventions in our simulation scenario, which can shed light on better policy designs in public spaces.