The proprietary search algorithm of AddSearch is built on top of the leading search platform, Elasticsearch. We score each document based on various factors. AddSearch also provides you with tools you can use to adjust ranking manually.
For example, the following factors affect document ranking:
- Self-learning algorithm: Giving a higher score to documents with better click-through rates
- Sorting: Ranking boosted by defined categories like date, relevancy or custom fields
- Pinned Results: Possibility to curate results manually for specific keywords
- Site Areas: Boosting positively or negatively a section of a website
- Phrase match: Giving a higher score to exact phrase matches. Returning weaker results with lower priority
- Field weights: Documents with keyword-match in the title or H1 elements get a higher score than documents with a match in the main content. Settings can be changed manually
- Typo-tolerance: Handling misspelled keywords with fuzzy matching. E.g. “contai information” returns results with the word “contact” properly spelled
- Bigram matching and tokenization: For example, the URL /contact-information/ matches keywords “contact-information”, “contact information” and “contactinformation”
- Stemming and plurals: When a language-specific stemming is enabled, the keyword “phones” matches exactly to “phone” or “filters” matches “filtering”
- Synonyms: Possibility to define document-specific or global synonyms. E.g. “coat” matching the word “jacket”
- Partial March, or Wildcards: Return results with a partial search term. E.g. “auto” matching to “automatic”. With an optional prefix wildcard, “auto” would match to “semiautomatic” as well
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