MOTIVATION: In many biomedical studies, there arises the need to integrate data from multiple directly or indirectly related sources. Collective matrix factorization (CMF) and its variants are models designed to collectively learn from arbitrary collections of matrices. The latent factors learnt are rich integrative representations that can be used in downstream tasks, such as clustering or relation prediction with standard machine-learning models. Previous CMF-based methods have numerous modeling limitations. They do not adequately capture complex non-linear interactions and do not explicitly model varying sparsity and noise levels in the inputs, and some cannot model inputs with multiple datatypes. These inadequacies limit their use on many biomedical datasets. RESULTS: To address these limitations, we develop Neural Collective Matrix Factorization (NCMF), the first fully neural approach to CMF. We evaluate NCMF on relation prediction tasks of gene-disease association prediction and adverse drug event prediction, using multiple datasets. In each case, data are obtained from heterogeneous publicly available databases and used to learn representations to build predictive models. NCMF is found to outperform previous CMF-based methods and several state-of-the-art graph embedding methods for representation learning in our experiments. Our experiments illustrate the versatility and efficacy of NCMF in representation learning for seamless integration of heterogeneous data. AVAILABILITY AND IMPLEMENTATION: https://github.com/ajayago/NCMF_bioinformatics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Quality assurance (QA) and customer satisfaction (C-Sat) analysis are two commonly used practices to measure goodness of dialogues between agents and customers in contact centers. The practices however have a few shortcomings. QA puts sole emphasis on agents’ organizational compliance aspect whereas C-Sat attempts to measure customers’ satisfaction only based on post dialogue surveys. As a result, outcome of independent QA and C-Sat analysis may not always be in correspondence. Secondly, both processes are retrospective in nature and hence, evidences of bad past dialogues (and consequently bad customer experiences) can only be found after hours or days or weeks depending on their periodicity. Finally, human intensive nature of these practices lead to time and cost overhead while being able to analyze only a small fraction of dialogues. In this paper, we introduce an automatic real-time quality assurance system for contact centers — QART (pronounced cart). QART performs multi-faceted analysis on dialogue utterances, as they happen, using sophisticated statistical and rule-based natural language processing (NLP) techniques. It covers various aspects inspired by today’s QA and C-Sat practices as well as introduces novel incremental dialogue summarization capability. QART front-end is an interactive dashboard providing views of ongoing dialogues at different granularity enabling agents’ supervisors to monitor and take corrective actions as needed. We demonstrate effectiveness of different back-end modules as well as the overall system by experimental results on a real-life contact center chat dataset.

In this paper, we describe an automatic real-time quality assurance system QART (pronounced cart) for contact center chats. QART performs multi-faceted analysis on dialogue utterances, as they happen, using sophisticated statistical and rule-based natural language processing (NLP) techniques. It covers various aspects inspired by today's Quality Assurance and Customer Satisfaction Scoring(C-Sat) practices as well as introduces novel components such as incremental dialogue summarization capability. QART front-end is an interactive dashboard providing views of ongoing dialogues at different granularity, enabling contact center supervisors to monitor and take corrective actions as needed. It is developed on state of the art stream computing platform Apache Spark Streaming with HBase datastore and Python Flask front end.