I am currently exploring the principles and workings of faceted search, particularly focusing on the difference between explicit and implicit faceted search.

My findings till now is that explicit faceted search is straightforward and involves the user explicitly selecting facets to refine search results. This can be easily implemented using open-source search engines like Elasticsearch or Apache Solr.

However, I am more interested in implicit faceted search, which involves an AI system that can interpret natural language queries and automatically translate them into explicit search instructions. This requires a deeper level of natural language understanding and possibly machine learning techniques to recognize and extract relevant facets from the user’s input.
For example : I am looking for a new job particularly remote working in softwares development . I live in New York and would like to work in and around the city.

So, the facet which will be fetched are , skills: software development; work type: remote ; location : New York.

I would appreciate any research papers, articles, or resources that delve into the implementation and challenges of the above implicit faceted search. I’m particularly interested in how AI and NLP can be leveraged to enhance search engines in this regard.