Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms

Question categorization and expert retrieval methods have been crucial for information organization and accessibility in community question & answering (CQA) platforms. Research in this area, however, has dealt with only the text modality. With the increasing multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images. Specifically, we leverage the success of representation learning for text and images in the visual question answering (VQA) domain, and adapt the underlying concept and architecture for automated category classification and expert retrieval on image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of Yahoo! Answers. To the best of our knowledge, this is the first work to tackle the multimodality challenge in CQA, and to adapt VQA models for tasks on a more ecologically valid source of visual questions. Our analysis of the differences between visual QA and community QA data drives our proposal of novel augmentations of an attention method tailored for CQA, and use of auxiliary tasks for learning better grounding features. Our final model markedly outperforms the text-only and VQA model baselines for both tasks of classification and expert retrieval on real-world multimodal CQA data.Comment: Submitted for review at CIKM 201

SCSMiner: mining social coding sites for software developer recommendation with relevance propagation

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. With the advent of social coding sites, software development has entered a new era of collaborative work. Social coding sites (e.g., GitHub) can integrate social networking and distributed version control in a unified platform to facilitate collaborative developments over the world. One unique characteristic of such sites is that the past development experiences of developers provided on the sites convey the implicit metrics of developer’s programming capability and expertise, which can be applied in many areas, such as software developer recruitment for IT corporations. Motivated by this intuition, we aim to develop a framework to effectively locate the developers with right coding skills. To achieve this goal, we devise a generativ e probabilistic expert ranking model upon which a consistency among projects is incorporated as graph regularization to enhance the expert ranking and a perspective of relevance propagation illustration is introduced. For evaluation, StackOverflow is leveraged to complement the ground truth of expert. Finally, a prototype system, SCSMiner, which provides expert search service based on a real-world dataset crawled from GitHub is implemented and demonstrated

Exploración de modelos translacionales para recomendación de ítems

Los Sistema de Recomendación son herramientas software que desde hace un par de décadas asisten a los usuarios de sistemas informáticos para el consumo de productos y/o servicios. Han ganado una tremenda popularidad debido al auge del comercio electrónico y a la vastísima oferta actual en la web. Por este motivo la investigación básica en este tipo de herramientas es cada vez más popular e importante. En este trabajo se propone profundizar en una nueva aproximación a los modelos de recomendación que se vie ne empleando desde hace relativamente poco tiempo. Consiste en emplear algoritmos basados en Knowledge Graph Embbedings para representar entidades y relaciones entre ellas y su aplicación a la recomendación de ítems. En particular se desea explorar ciertos modelos denominados translacionales que se han demostrado muy útiles en esta tarea de recomendación. Con este objetivo, se pretende reproducir la experimentación realizada en un estudio previo en el que se utilizan estos modelos como métodos de recomenda ción para la base de datos MovieLens , comparando su rendimiento frente a algunos modelos base de recomendación. Adicionalmente, se incluye la experimentación con un nuevo con la particularidad de que en este usuarios. Como solución, se emplea dataset no se conoce n una técnica que calcula usuarios del conjunto a partir de estadísticas relacionadas conjunto de datos, los ratings LastFM explícitos , de los los ratings implícitos de los con la escucha de canciones Por último, se presenta un posible mode . lo translacional cuya función de pérdida se basa en la topol o gía del grafo. Esta propuesta se realiza de manera puramente teórica sin poder certificar su validez de forma empírica, que se plantea como trabajo futuro