Koo has rolled out an exciting in-app feature ‘Topics’ across 10 languages. Topics offers a highly personalised experience to multi-lingual users.
With the launch of ‘Topics’ feature, Koo users get to view only the kind of the content which is most relevant to them. The new feature will make it easier for users to pick and choose content as per their interest and preferences, instead of scrolling through the feed on the platform.
For example, if a user is seeking news and information related to ‘health’, he/she can click on the ‘health’ section under the Topics tab to consume all relevant ‘Koos’ pertaining to vaccination, lifestyle diseases, healthcare tips from medical experts, etc.
“We have over 20 millions topic follows every month, showing the relevance of this feature to users. We achieve topic classification through complex machine learning models that have a very high level of precision. We are proud to have mastered such complexity in a short span of our existence. I foresee over 100 million topic follows every month by the end of this year,” said Mayank Bidawatka, co-founder, Koo.
‘Topics’ enables conversations for Koo users across the 10 languages at any given point in time, with the most popular topics making their way under various categories (like health, education, environment, movies, sports), eminent personalities, organisations (like ISRO, IMF), places, (states, cities, countries that are in news) and a host of other trending topics.
Koo has worked to bring this feature to its platform by combining various Machine Learning and Natural Language Processing (NLP) techniques. The Koo Machine Learning team trained LLMs (Large Language Models) and some of the most complex neural network architectures to extract important entities being discussed in a Koo for this feature.
“NLP technologies for Indian languages do not enjoy the extensive ecosystem that is available for English. Koo innovated in a variety of areas to implement Indian language Natural Language Processing (NLP) tasks to build Topics across Indian languages,” explained Harsh Singhal, Head of Machine Learning, Koo.
Source: businessworld.in
