Product bundling, offering a combination of items to customers, is one of the marketing strategies commonly used in online ecommerce and offline retailers. A high-quality bundle generalizes frequent items of interest, and diversity across bundles boosts the user-experience and eventually increases transaction volume. In this paper, we formalize the personalized bundle list recommendation as a structured prediction problem and propose a bundle generation network (BGN), which decomposes the problem into quality/diversity parts by the determinantal point processes (DPPs). BGN uses a typical encoder-decoder framework with a proposed feature-aware softmax to alleviate the inadequate representation of traditional softmax, and integrates the masked beam search and DPP selection to produce high-quality and diversified bundle list with an appropriate bundle size. We conduct extensive experiments on three public datasets and one industrial dataset, including two generated from co-purchase records and the other two extracted from real-world online bundle services. BGN significantly outperforms the state-of-the-art methods in terms of quality, diversity and response time over all datasets. In particular, BGN improves the precision of the best competitors by 16% on average while maintaining the highest diversity on four datasets, and yields a 3.85x improvement of response time over the best competitors in the bundle list recommendation problem.

The World Health Organization (WHO) has collected information on policies on sexual, reproductive, maternal, newborn, child and adolescent health (SRMNCAH) over many years. Creating a global survey that works for every country context is a well-recognized challenge. A comprehensive SRMNCAH policy survey was conducted by WHO from August 2018 through May 2019. WHO regional and country offices coordinated with Ministries of Health and/or national institutions who completed the questionnaire. The survey was completed by 150 of 194 WHO Member States using an online platform that allowed for submission of national source documents. A validation of the responses for selected survey questions against content of the national source documents was conducted for 101 countries (67%) for the first time in the administration of the survey. Data validation draws attention to survey questions that may have been misunderstood or where there was a lot of missing data, but varying methods for validating survey responses against source documents and separate analysis of laws from policies and guidelines may have hindered the overall conclusions of this process. The SRMNCAH policy survey both provided a platform for countries to track their progress in adopting WHO recommendations in national SRMNCAH-related legislation, policies, guidelines and strategies and was used to create a global database and searchable document repository. The outputs of the SRMNCAH policy survey are resources whose importance will be enriched through policy dialogues and wide utilization. Lessons learned from the methodology used for this survey can help to improve future updates and inform similar efforts.

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Motivation The integration of single-cell multi-omics data can uncover the underlying regulatory basis of diverse cell types and states. However, contemporary methods disregard the omics individuality, and the high noise, sparsity, and heterogeneity of single-cell data also impact the fusion effect. Furthermore, available single-cell clustering methods only focus on the cell type clustering, which can not mine the alternative clustering to comprehensively analyze cells. Results We propose a single-cell data fusion based multiple clustering (scMCs) approach that can jointly model single-cell transcriptomics and epigenetic data, and explore multiple different clusterings. scMCs first mines the omics-specific and cross-omics consistent representations, then fuses them into a co-embedding representation, which can dissect cellular heterogeneity and impute data. To discover the potential alternative clustering embedded in multi-omics, scMCs projects the co-embedding representation into different salient subspaces. Meanwhile, it reduces the redundancy between subspaces to enhance the diversity of alternative clusterings and optimizes the cluster centers in each subspace to boost the quality of corresponding clustering. Unlike single clustering, these alternative clusterings provide additional perspectives for understanding complex genetic information such as cell types and states. Experimental results show that scMCs can effectively identify subcellular types, impute dropout events, and uncover diverse cell characteristics by giving different but meaningful clusterings. Availability The code is available at www.sdu-idea.cn/codes.php?name=scMCs. Supplementary information Supplementary data are available at Bioinformatics online.