Feature engineering

Common feature engineering tasks that you may encounter while developing a learning to rank (LTR) solution are described in the following sections.

Getting raw term statistics

Many LTR solutions use raw term statistics in their training, such as the following:

• Total term frequency (raw_ttf): The total number of times that a term appears across an entire index.
• Document frequency (raw_df): The number of documents in which a term appears.
• Term frequency (raw_tf): The number of times that a term appears in a specific document.
• Classic IDF (classic_idf): The inverse document frequency (IDF) calculation log((NUM_DOCS+1)/(raw_df+1)) + 1.

The Learning to Rank plugin provides a match_explorer query primitive that can extract these statistics for you, as shown in the following example:

POST tmdb/_search
{
    "query": {
        "match_explorer": {
            "type": "max_raw_df",
            "query": {
                "match": {
                    "title": "rambo rocky"
                }
            }
        }
    }
}

The query returns the highest document frequency between the terms rambo and rocky.

You can use operations such as max, min, sum, and stddev with the statistics to get the information you need.

Term position statistics

You can prepend the type with the desired operation (min, max, avg) to calculate the corresponding statistic across the term positions. If the terms are not present in the document, then the result will be 0.

The available statistics include the following:

• min_raw_tp (minimum raw term position): This statistic finds the earliest position of any search term in the document. For example, with the query dance monkey, if dance occurs at positions [2, 5, 9] and monkey occurs at [1, 4], then the minimum is 1.
• max_raw_tp (maximum raw term position): This statistic finds the latest position of any search term in the document. Using the preceding example, the maximum is 9.
• avg_raw_tp (average raw term position): This statistic calculates the average term position for any of the query terms. Using the preceding example, the average for dance is 5.33 [(2+5+9)/3)] and the average for monkey is 2.5 [(1+4)/2], with an overall average of 3.91.
• unique_terms_count: Provides a count of the unique search terms in the query.

Document-specific features

When working on an LTR solution, you may need to incorporate features that are specific to the document rather than to the relationship between the query and the document. These document-specific features can include metrics related to popularity or recency.

The function_score query provides the functionality to extract these document-specific features. The following example query shows how you can use it to incorporate the vote_average field as a feature:

{
    "query": {
        "function_score": {
            "functions": [{
                "field_value_factor": {
                    "field": "vote_average",
                    "missing": 0
                }
            }],
            "query": {
                "match_all": {}
            }
        }
    }
}

In the example, the score of the query is determined by the value of the vote_average field, which could be a measure of document popularity or quality.

Index drift

When working with an index that is regularly updated, it is important to consider that the trends and patterns you observe may not remain constant over time. Your index can drift as user behavior, content, and other factors change. For example, on an e-commerce store, you may find that sandals are popular during summer months but become almost impossible to find in the winter. Similarly, the features that drive purchases or engagement during one time period may not be as important during another.

Next steps

Learn about logging feature scores.