An advanced search query based Recommendation System built using BERT based sentence_transformer model to recommendations for user input query. We implemented semantic search, question answering, summarization and document ranking and correcting spelling error in the search query.

1. Recommend keywords that are more relevant to user’s search query by improving accuracy relevence and optimising the time taken.
2. Improving the transactional impact by comparative visualization of the dataset scraped and the keywords/analytics provided. Then combining the relevant keys for a better output.
3. Inserting extra features like input auto-correction, similar searches etc.

Stack: Python, Pandas, Numpy, Faiss, Seaborn, django, HTML, CSS

Model: Implementing Bert (Bidirectional Encoder Representations from Transformers) using Sentence Transformers

1. Crawling the given site and scraping the data through Scrapy.

   Scraped Data : (2652,4) 
   

2. Pre-processing the scraped data along with keywords provided and then calculating frequency weightage i.e. finding most searched.

4. Implementing SBert model leading to :

   a) Semantic Search (eg. insurance -> vehicle).

   b) Paraphrase Mining (i.e. Text with identical/similar meaning).

   c) Writing Search function -> Analyzing Query Vector ->Fetch into top k_ids.