List of Tables

Chapter 2. Search—under the hood

Table 2.1. Important pieces of data associated with the inverted index

Chapter 3. Debugging your first relevance problem

Table 3.1. Scoring love matches in Sleepless in Seattle versus lego in The Lego Movie. lego is rare and is mentioned only in The Lego Movie, thus yielding a higher score than the love match.

Table 3.2. Term frequency and document frequencies dampened with default formulas (IDF calculated for 1,000 documents)

Chapter 4. Taming tokens

Table 4.1. Semantic expansion at index time

Table 4.2. Tokens used to represent numerical values and ranges

Chapter 5. Basic multifield search

Table 5.1. Primitives working with TMDB and Elasticsearch

Chapter 6. Term-centric search

Table 6.1. Relative document frequencies of the phrase william shatner in two source fields and the combined people custom all field

Chapter 8. Providing relevance feedback

Table 8.1. Completions for a user searching for a Star Trek movie

Chapter 9. Designing a relevance-focused search application

Table 9.1. Yowl information requirements, sources, and uses

Table 9.2. Given the required information, you form fields and determine how to appropriately analyze the content.

Chapter 11. Semantic and personalized search

Table 11.1. Log tabulating users’ purchases

Table 11.2. The first step for determining item co-occurrence is grouping items by user. A dot (•) indicates a purchase.

Table 11.3. Item co-occurrence counts for every item in the purchase history