When no FAQ exists for training, the cold start solution uses our word vector (Word2vec) training module in the Docker image to learn about the vocabulary in the search results. Then it uses our provided query pipeline to combine Solr and document vector similarity scores at query time.
We suggest capturing signals from document clicks, likes, and downloads. These signals form Q&A pairs. After cumulating at least 3,000 Q&A pairs, that feedback can be used as training data for the FAQ solution.
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.