Through its 200+ online verticals, Recruit Holdings offers rich experiences to those who use our products. Our research goal is to assist our customers’ decision-making processes when engaging in lifestyle experiences, such as travel, dining, and hairstyling, as well as daily experiences, like education, employment, and housing, by surfacing the most needed information. Megagon develops state-of-the-art technologies in machine learning, natural language processing, and database management. Our projects focus on solving real-world problems.
Customers seek travel experiences that fulfill their desires. However, e-commerce search engines only support queries involving objective attributes, such as location, price, and cuisine. Whereas, experiential data is relegated to text reviews. Therefore, a database system must model subjective data and process queries in the user’s own words to support experiential queries. At the same time, it must specify predicates involving objective attributes. We developed Opine, a subjective database system, that addresses these challenges as well as its frontend, Voyageur, an experiential travel search engine.
Understanding what makes people happy is essential to augment positive experiences. We built HappyDB, a crowd-sourced collection of 100,000 happy moments that we make publicly available. Our goal is to build NLP technology that understands how people express their happiness in text while achieving insights into happiness-leading events and scenarios on a scale. Moreover, we are interested in developing systems that suggest sustainable actions for individuals that lead to an overall improvement in their well-being within these actual moments. HappyDB is an exciting resource for the emerging research field regarding the intersection between NLP and positive psychology.
MegaMiner is a toolbox we are building to extract insights from user-generated text, such as reviews. Reviews are a ubiquitous ingredient of e-commerce applications. Accordingly, Megagon Labs’ core research activity includes developing models and algorithms to leverage the information in a text review. MegaMiner will include a suite of tools. These will include intelligent text labeling for supporting model development, interactive exploratory text analysis, opinion extraction, text summarization, and aggregation.
Dynamic review summarization
We have millions of hotel reviews online. Reading summaries that convey the bottom line without going through each one saves time and energy. However, current text summarization techniques condense reviews with fixed aspects, lacking the capability to personalize. Therefore, we set out to build a dynamic summarization system that caters to individual preferences. Additionally, the system uses natural language extracted from the reviews to substantiate aspects of the review summary. Consequently, both customers and hotel operators would find the summarizations useful.