Understanding Chat Messages for Sticker Recommendation in Messaging Apps
Table 1: Performance of the message prediction models
['Method', '# of Character to be Typed', '# of times inaccurate predictions shown', 'Fraction of msg retrieved']
The quantized NN model performs better in terms of # of characters to be typed. This is expected because the NN model learns to use both inputs simultaneously whereas the combiner (last function in hybrid model) used is a simple linear combination of just three features. The hybrid model performs slightly better in terms of # of times inaccurate recommendations shown and fraction of messages retrieved. Compared to the quantized model, the hybrid model needs approximately one more character to be typed on an average to get the required predictions. However, the quantized NN is ∼9.2 MB in size, which goes beyond the permissible memory limits set in our use case. The on-device foot print for our hybrid model is below 1 MB. Also, the trie based model can guarantee retrieval of all message clusters, with some prefix of the message. If the message is not interfered by another longer but more frequent message, the message class can be featured in top position itself, with some prefix of the message as input or with full message as input. A pure NN based model can’t make such guarantees. Given that this SR interface is one of the heavily used interfaces for sticker discovery, if a sticker is not retrieved through this recommendation, the user might think that the app doesn’t support the message class or the app doesn’t have such stickers. So, making the retrieval of message cluster close to 100%% is critical for our SR models.