When no training data exists to use Supervised job, the cold start solution uses general pre-trained Deep Learning models or provides a possibility to train custom word embeddings for specific domains.
Over time, we suggest capturing signals from document clicks, likes, and downloads. These signals can be used to construct training dataset. After cumulating at least 3000 pairs, that feedback can be used as training data for the Supervised solution.
|Support for the Fusion 4.2 implementation of Smart Answers will be discontinued by end of year 2020. Upgrade to Fusion 5.1 or higher for ongoing Smart Answers support.|
Like the FAQ solution, the cold start solution has two parts:
Model training is performed in a Docker image, which can be downloaded from the Lucidworks Docker hub. The deep learning/word vector training modules and configuration UI are installed in the Docker image, and it can be used on-prem or in the cloud.
After training is finished, a
.zipfile is generated which includes the model and associated files. The model transforms documents into digital vectors which can be used to measure similarities.
Deployment is performed in Fusion. To deploy, you just upload the generated zip file to Fusion and use our provided query pipeline to conduct run-time neural search.