Models, such as BERT, have made a significant breakthrough in the Natural Language Processing (NLP) domain solving 11+ tasks. This is achieved by training on a large scale of unlabelled text resources and leveraging Transformers architecture making it the “Jack of all NLP trades”. However, one of the popular and challenging tasks in Sequence Classification is Short Text Classification (STC). Short Texts face the problem of being short, equivocal, and non-standard. In this paper, we address two major problems: 1. Improving STC tasks performance in Japanese language which consists of many varieties and dialects. 2. Building a light-weight Japanese BERT model with cross-domain functionality and comparable accuracy with State of the Art (SOTA) BERT models. To solve this, we propose a novel cross-domain scalable model called JLBert, which is pre-trained on a rich, diverse and less explored Japanese e-commerce corpus. We present results from extensive experiments to show that JLBert is outperforming SOTA Multilingual and Japanese specialized BERT models on three Short Text datasets by approx 1.5% across various domain.

Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base. Challenges associated to these models reside on the heterogeneity of the user base and its sparsity. In this work, we propose a novel framework that unifies the customers' different behaviors or features such as demographics, buying behaviors on different platforms, customer loyalty behaviors and build a lookalike model to improve customer targeting for Rakuten Group, Inc. Extensive experiments on real e-commerce and travel datasets demonstrate the effectiveness of our proposed lookalike model for user targeting task.

We consider a smart phone scenario with a number of apps used by a user. The app usage data provides information about the user behavior, which can be used to identify the user demographics and interest and in turn is used to find similar users. In this paper, we propose a method to generate a latent space user embedding using the user app usage data, which is a dense low-dimensional representation of the user. This representation is used for low latency user similarity computation and acts as the user feature representation in user demographics prediction models.

Geosocial Networking is the new hotness, with social networks providing services and capabilities to the users to associate location to their profiles. But, because of privacy and security reasons, most of the people on social networking sites like Twitter are unwilling to provide locations in their

This paper describes a framework, SNODSOC (Stream based novel class detection for social network analysis), that detects evolving patterns and trends in social microblogs. SNODSOC extends our powerful data mining system, SNOD (Stream-based Novel Class Detection) for now detecting novel patterns and trends within microblogs.