Response-Recommendation

Recommend response to queries based on a established query-response table

Data Flow

How the program runs?

Different Methods Used

* Cosine Similarity With Tf-idf Vectors
* K-means clustering with doc2vec vectors
* Two LSTM with Tf-idf 

Cosine Similarity with Tf-idf Vectors

1. Create a corpus of query-response words.
2. Create Count Vector for the corpus and from it Tf-idf vectors
3. Iterate through training set to find example with minimum cosine distance and suggest the corresponding response.

K-means clustering with doc2vec vectors

1. Create a Doc2Vec and model and train it.
2. Calculate sentence vectors of Queries from trained model
3. Perform K-Means Clustering on trained data.
4. Give corresponding response of the queries in which test query belongs to.

Link to paper on the two LSTM approach

Extension

The purpose of the extension is so that it sends a POST request of a highlited text(in our case the query for which response is requires) to our server which inturn gives back the suggested response.