Relevance scores in Zendesk search results are determined by a weighted average per field score. Matches in titles score higher than those in other fields, and labels also have a significant impact. Factors like exact matches, term frequency, and proximity boost the score. Semantic search is also used to improve accuracy based on user intent.Explore more about relevance scores.
Zendesk Help Center search results include a variety of content types. The search covers all knowledge base articles, community posts, and, if configured, external content such as blogs or websites. The search engine attempts to find a relevant…
Zendesk search results are boosted by factors such as article and community post votes, and the use of labels. Articles with higher positive vote percentages appear higher in search results. Labels can be used strategically to influence relevance…
Improving the search experience for Zendesk end users involves customizing search result highlights and using search analytics. By analyzing search data, you can refine your knowledge base content and search results. Additionally, providing search…
Fuzzy search in Zendesk allows for relevant search results even when there isn't an exact match to the search terms. It uses edit distance to identify terms close to the query, accommodating spelling mistakes. For example, 'user segmemt' will…
Zendesk applies language-specific optimizations like stemming and stop words to improve search accuracy. Stemming ensures that variations of a word, like 'films' and 'film', are recognized. Stop words, common words in a language, are excluded to…
Advanced search in Zendesk can be enhanced using operands like double quotes for exact matches and the minus operator to exclude terms. Combining these operands allows for precise search results. For example, 'reporting bugs' -support finds content…