Language
Country/Area
No result found
The secret weapon of search engines: Why expanding queries can make results more accurate?
In the field of information retrieval, Query Expansion (QE) is a compelling technology. This process aims to restructure the user's query to improve retrieval performance. Search engines improve retrieval performance and user satisfaction by evaluating user input and expanding queries to match more relevant documents. How to obtain better search results by expanding queries has become the focus of both academia and industry.
Extended queries include a variety of techniques, such as finding synonyms, semantically related words, and correcting spelling errors. These can effectively improve the recall rate of retrieval, but may also lead to a decrease in accuracy.
Basic principles of expanded queries
The rationale for expanded queries is that when many users enter search terms, they don't use the best words to express their needs. This may be because the user entered a word that does not exist in the database. Through stemming technology, the system can match more related documents and increase the overall recall rate. However, this may also come at the expense of accuracy. When the user query is expanded to synonyms, recall will also increase, but precision may decrease.
The reason for this is that when the recall rate increases, the results may contain many irrelevant documents, which affects the overall retrieval quality. Many users don't want to see too many results, but rather get exactly what they need.
Methods to expand queries
Extended queries can be implemented in a variety of ways. As early as 1960, Maron and Kuhns proposed the method of automatic query expansion. Today's technologies often rely on analysis of document collections, and this analysis can be global or local, as well as extended methods based on dictionaries or ontologies.
Global analysis involves finding the correlation between words, while local analysis was proposed by Rocchio, which uses manual marking of certain retrieved documents to determine their relevance and then expand the query.
An important concept in this is Pseudo-Relevance Feedback (PRF), which means to select expansion candidate words based on the first few retrieved documents as relevant documents. Although PRF can usually improve the effect of query expansion, in some difficult queries, the top retrieved documents are often not relevant, which may harm the accuracy of the results.
Technology and Application
In today's technology, query expansion is integrated with document expansion into the implementation of vector databases, which use various encoding schemes based on deep learning to handle the relationship between queries and documents. Such technology can not only improve the quality of queries, but also allow complex semantic associations to be better understood.
Conclusion
With the increasing demand for information by humans, expanded query, as an important means to improve the accuracy of search engines, is gradually becoming an industry standard. In the future, through more intelligent and flexible extended query technology, can the accuracy and relevance of user queries be improved to a new level?
