Building or Finding a “relevant terms” suggestion feature
Given a few words of input, I want to have a utility that will return a diverse set of relevant terms, phrases, or concepts. A caveat is that it would need to have a large grap
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Take a look at the following two papers:
- Clustering User Queries of a Search Engine [pdf]
- Topic Detection by Clustering Keywords [pdf]
Here is my attempt at a very simplified explanation:
If we have a database of past user queries, we can define a similarity function between two queries. For example: number of words in common. Now for each query in our database, we compute its similarity with each other query, and remember the k most similar queries. The non-overlapping words from these can be returned as "related terms".
We can also take this approach with a database of documents containing information users might be searching for. We can define the similarity between two search terms as the number of documents containing both divided by the number of documents containing either. To decide which terms to test, we can scan the documents and throw out words that are either too common ('and', 'the', etc.) or that are too obscure.
If our data permits, then we could see which queries led users to choosing which results, instead of comparing documents by content. For example if we had data that showed us that users searching for "Celtics" and "Lakers" both ended up clicking on espn.com, then we could call these related terms.
If you're starting from scratch with no data about past user queries, then you can try Wikipedia, or the Bag of Words dataset as a database of documents. If you are looking for a database of user search terms and results, and if you are feeling adventurous, then you can take a look at the AOL Search Data.
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