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Today, users have come to expect search results that are tailored to their individual interests. If all the songs we listen to are rock songs, we would expect an Aerosmith song at the top of the results when searching for Crazy, not the one by Gnarls Barkley. In this article, we take a look at ways to personalize search before diving into the specifics of how to do this with learning-to-rank (LTR), using music preferences as an example.
Ranking factors
First, let's recap which factors are important in search ranking in general. Given a user query, a relevance function can take into account one or multiple of the following factors:
- Text similarity can be measured with a variety of methods including BM25, dense vector similarity, sparse vector similarity or through cross-encoder models. We can calculate similarity scores of the query string against multiple fields in a document (title, description, tags, etc.) to determine how well the input query matches a document.
- Query properties can be inferred from the query itself, for example the language, named entities or the user intent. The domain will influence which of these properties can be most helpful to improve relevance..
- Document properties pertain to the document itself, for example its popularity or the price of the product represented by the document. These properties often have a big impact on the relevance when applied with the right weights.
- User and context properties refer to data that is not associated with the query or the document but with the context of the search request, for example the location of the user, past search behavior or user preferences. These are the signals that will help us personalize our search.