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Stickers are popularly used in messaging apps such as Hike to visuallyexpress a nuanced range of thoughts and utterances to convey exaggeratedemotions. However, discovering the right sticker from a large and everexpanding pool of stickers while chatting can be cumbersome. In this paper, wedescribe a system for recommending stickers in real time as the user is typingbased on the context of the conversation. We decompose the stickerrecommendation (SR) problem into two steps. First, we predict the message thatthe user is likely to send in the chat. Second, we substitute the predictedmessage with an appropriate sticker. Majority of Hike's messages are in theform of text which is transliterated from users' native language to the Romanscript. This leads to numerous orthographic variations of the same message andmakes accurate message prediction challenging. To address this issue, we learndense representations of chat messages employing character level convolutionnetwork in an unsupervised manner. We use them to cluster the messages thathave the same meaning. In the subsequent steps, we predict the message clusterinstead of the message. Our approach does not depend on human labelled data(except for validation), leading to fully automatic updation and tuningpipeline for the underlying models. We also propose a novel hybrid messageprediction model, which can run with low latency on low-end phones that havesevere computational limitations. Our described system has been deployed formore than $6$ months and is being used by millions of users along with hundredsof thousands of expressive stickers.

Understanding Chat Messages for Sticker Recommendation in Messaging Apps