Understanding Chat Messages for Sticker Recommendation in Hike Messenger

Stickers are popularly used in messaging apps such as Hike to visually express a nuanced range of thoughts and utterances to convey exaggerated emotions. However, discovering the right sticker from a large and ever expanding pool of stickers while chatting can be cumbersome. In this paper, we describe a system for recommending stickers in real time as the user is typing based on the context of conversation. We decompose the sticker recommendation problem into two steps. First, we predict the message that the user is likely to send in the chat. Second, we substitute the predicted message with an appropriate sticker. Majority of Hike's messages are in the form of text which is transliterated from users' native language to the Roman script. This leads to numerous orthographic variations of the same message and complicates message prediction. To address this issue, we learn dense representations of chat messages and use them to cluster the messages that have same meaning. In the subsequent steps we predict the message cluster instead of the message. Our model employs a character level convolution network to capture the similar intents in orthographic variants of chats. We validate our approach using manually labelled data on two tasks. We also propose a novel hybrid message prediction model, which can run with low latency on low end phones that have severe computational limitations.