Relevance feedback

Relevance feedback is a feature of some information retrieval systems. The idea behind relevance feedback is to take the results that are initially returned from a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types of feedback: explicit feedback, implicit feedback, and blind or 'pseudo' feedback. Relevance feedback is a feature of some information retrieval systems. The idea behind relevance feedback is to take the results that are initially returned from a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types of feedback: explicit feedback, implicit feedback, and blind or 'pseudo' feedback. Explicit feedback is obtained from assessors of relevance indicating the relevance of a document retrieved for a query. This type of feedback is defined as explicit only when the assessors (or other users of a system) know that the feedback provided is interpreted as relevance judgments. Users may indicate relevance explicitly using a binary or graded relevance system. Binary relevance feedback indicates that a document is either relevant or irrelevant for a given query. Graded relevance feedback indicates the relevance of a document to a query on a scale using numbers, letters, or descriptions (such as 'not relevant', 'somewhat relevant', 'relevant', or 'very relevant'). Graded relevance may also take the form of a cardinal ordering of documents created by an assessor; that is, the assessor places documents of a result set in order of (usually descending) relevance. An example of this would be the SearchWiki feature implemented by Google on their search website. The relevance feedback information needs to be interpolated with the original query to improve retrieval performance, such as the well-known Rocchio algorithm. A performance metric which became popular around 2005 to measure the usefulness of a ranking algorithm based on the explicit relevance feedback is NDCG. Other measures include precision at k and mean average precision. Implicit feedback is inferred from user behavior, such as noting which documents they do and do not select for viewing, the duration of time spent viewing a document, or page browsing or scrolling actions . There are many signals during the search process that one can use for implicit feedback and the types of information to provide in response The key differences of implicit relevance feedback from that of explicit include : An example of this is dwell time, which is a measure of how long a user spends viewing the page linked to in a search result. It is an indicator of how well the search result met the query intent of the user, and is used as a feedback mechanism to improve search results.Another example of this is the Surf Canyon browser extension, which advances search results from later pages of the result set based on both user interaction (clicking an icon) and time spent viewing the page linked to in a search result.