Orian Sharoni &
Nataly Shrits
Poster – Intent based search engine suggestions API using Tensorflow, clustering and kubernetes.
Soluto
Orian Sharoni &
Nataly Shrits
Poster – Intent based search engine suggestions API using Tensorflow, clustering and kubernetes.
Soluto
Bio
Nataly is a senior engineer @ Soluto
Orian is a DS @ Soluto
Bio
Nataly is a senior engineer @ Soluto
Orian is a DS @ Soluto
Abstract
At Soluto we have a chat platform for users that need technical support.
The project’s aim was to help the chat users to better express their needs. In order to do so, we used Tensorflow Universal Sentence Encoder to cluster our chat support applications into “families” of common issues. In each cluster, the question nearest to the center of the cluster was chosen as a representative of the entire cluster.
Nowadays, when a user first types a question into the service, the input is being processed into a vector, and the nearest cluster is located using cosine similarity. As a result, the first 3 cluster centers are being recommended for an opening sentence.
The feature is one of many micro-services the app uses, which are deployed using kubernetes, takes less than 0.5 seconds to process, and has caused more users to use the chat as they now can articulate their intent better.
Abstract
At Soluto we have a chat platform for users that need technical support.
The project’s aim was to help the chat users to better express their needs. In order to do so, we used Tensorflow Universal Sentence Encoder to cluster our chat support applications into “families” of common issues. In each cluster, the question nearest to the center of the cluster was chosen as a representative of the entire cluster.
Nowadays, when a user first types a question into the service, the input is being processed into a vector, and the nearest cluster is located using cosine similarity. As a result, the first 3 cluster centers are being recommended for an opening sentence.
The feature is one of many micro-services the app uses, which are deployed using kubernetes, takes less than 0.5 seconds to process, and has caused more users to use the chat as they now can articulate their intent better.