LRMM: Learning to Recommend with Missing Modalities

Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.Comment: 11 pages, EMNLP 201

Shared-use of railway infrastructure in South Africa: the case of coal and citrus production in Mpumalanga

Thesis (M.Com. (Development Theory and Policy))--University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic and Business Sciences, 2014.Economic activities such as mining extraction and farming have in the past been supported by railway infrastructure, which continues to provide a cheap transportation option for the movement of freight. This research paper looks at the apparent bias that exists in the shared-use of railway infrastructure in South Africa between coal miners and citrus growers in Mpumalanga. The study is specifically concerned with the regulatory regime governing access and the extent to which it enables or hinders the shared-use of rail infrastructure, which is critical in the movement of freight for different sectors of the economy. The paper uses literature on regulatory practices and a case study of Mpumalanga’s coal miners and citrus growers, to investigate South Africa’s regulatory regime and its role in creating particular biases in the use of rail infrastructure