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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. Explicit 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 (information retrieval), 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 i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Retrieval
Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds. Automated information retrieval systems are used to reduce what has been called information overload. An IR system is a software system that provides access to books, journals and other documents; stores and manages those documents. Web search engines are the most visible IR applications. Overview An information retrieval process begins when a user or searcher enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In inf ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Feedback
Feedback occurs when outputs of a system are routed back as inputs as part of a chain of cause-and-effect that forms a circuit or loop. The system can then be said to ''feed back'' into itself. The notion of cause-and-effect has to be handled carefully when applied to feedback systems: History Self-regulating mechanisms have existed since antiquity, and the idea of feedback had started to enter economic theory in Britain by the 18th century, but it was not at that time recognized as a universal abstraction and so did not have a name. The first ever known artificial feedback device was a float valve, for maintaining water at a constant level, invented in 270 BC in Alexandria, Egypt. This device illustrated the principle of feedback: a low water level opens the valve, the rising water then provides feedback into the system, closing the valve when the required level is reached. This then reoccurs in a circular fashion as the water level fluctuates. Centrifugal governors were ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Relevance (information Retrieval)
In information science and information retrieval, relevance denotes how well a retrieved document or set of documents meets the information need of the user. Relevance may include concerns such as timeliness, authority or novelty of the result. History The concern with the problem of finding relevant information dates back at least to the first publication of scientific journals in the 17th century. The formal study of relevance began in the 20th Century with the study of what would later be called bibliometrics. In the 1930s and 1940s, S. C. Bradford used the term "relevant" to characterize articles relevant to a subject (cf., Bradford's law). In the 1950s, the first information retrieval systems emerged, and researchers noted the retrieval of irrelevant articles as a significant concern. In 1958, B. C. Vickery made the concept of relevance explicit in an address at the International Conference on Scientific Information. Since 1958, information scientists have explored and deb ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
SearchWiki
SearchWiki was a Google Search feature which allowed logged-in users to annotate and re-order search results. The annotations and modified order only applied to the user's searches, but it was possible to view other users' annotation An annotation is extra information associated with a particular point in a document or other piece of information. It can be a note that includes a comment or explanation. Annotations are sometimes presented in the margin of book pages. For anno ...s for a given search query. SearchWiki was released on November 20, 2008 and discontinued on March 3, 2010. All previously created SearchWiki edits are preserved on the logged in user'SearchWiki notes page Google Stars replaced SearchWiki. Users no longer have the option to annotate or re-order search results. Instead, a user clicks on a "star marker" which makes the entry appear in a starred results list at the top of a new search. References SearchWiki Internet search engines {{searche ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Google
Google LLC () is an American multinational technology company focusing on search engine technology, online advertising, cloud computing, computer software, quantum computing, e-commerce, artificial intelligence, and consumer electronics. It has been referred to as "the most powerful company in the world" and one of the world's most valuable brands due to its market dominance, data collection, and technological advantages in the area of artificial intelligence. Its parent company Alphabet is considered one of the Big Five American information technology companies, alongside Amazon, Apple, Meta, and Microsoft. Google was founded on September 4, 1998, by Larry Page and Sergey Brin while they were PhD students at Stanford University in California. Together they own about 14% of its publicly listed shares and control 56% of its stockholder voting power through super-voting stock. The company went public via an initial public offering (IPO) in 2004. In 2015, Google was reor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rocchio Algorithm
The Rocchio algorithm is based on a method of relevance feedback found in information retrieval systems which stemmed from the SMART Information Retrieval System developed between 1960 and 1964. Like many other retrieval systems, the Rocchio algorithm was developed using the vector space model. Its underlying assumption is that most users have a general conception of which documents should be denoted as relevant or irrelevant.Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: ''An Introduction to Information Retrieval'', page 163-167. Cambridge University Press, 2009. Therefore, the user's search query is revised to include an arbitrary percentage of relevant and irrelevant documents as a means of increasing the search engine's recall, and possibly the precision as well. The number of relevant and irrelevant documents allowed to enter a query is dictated by the weights of the a, b, c variables listed below in the Algorithm section. Algorithm The formula and variabl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Figure Of Merit
A figure of merit is a quantity used to characterize the performance of a device, system or method, relative to its alternatives. Examples *Clock rate of a CPU * Calories per serving *Contrast ratio of an LCD *Frequency response of a speaker * Fill factor of a solar cell *Resolution of the image sensor in a digital camera *Measure of the detection performance of a sonar system, defined as the propagation loss for which a 50% detection probability is achieved *Noise figure of a radio receiver *The thermoelectric figure of merit, ''zT'', a material constant proportional to the efficiency of a thermoelectric couple made with the material *The figure of merit of digital-to-analog converter, calculated as (power dissipation)/(2ENOB × effective bandwidth) /Hz*Luminous efficacy of lighting * Profit of a company *Residual noise remaining after compensation in an aeromagnetic survey *Battery life of a laptop computer [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithm
In mathematics and computer science, an algorithm () is a finite sequence of rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algorithms are used as specifications for performing calculations and data processing. More advanced algorithms can perform automated deductions (referred to as automated reasoning) and use mathematical and logical tests to divert the code execution through various routes (referred to as automated decision-making). Using human characteristics as descriptors of machines in metaphorical ways was already practiced by Alan Turing with terms such as "memory", "search" and "stimulus". In contrast, a Heuristic (computer science), heuristic is an approach to problem solving that may not be fully specified or may not guarantee correct or optimal results, especially in problem domains where there is no well-defined correct or optimal result. As an effective method, an algorithm ca ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Discounted Cumulative Gain
Discounted cumulative gain (DCG) is a measure of ranking quality. In information retrieval, it is often used to measure effectiveness of web search engine algorithms or related applications. Using a graded relevance scale of documents in a search-engine result set, DCG measures the usefulness, or ''gain'', of a document based on its position in the result list. The gain is accumulated from the top of the result list to the bottom, with the gain of each result discounted at lower ranks. Kalervo Järvelin, Jaana Kekäläinen: Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems 20(4), 422–446 (2002) Overview Two assumptions are made in using DCG and its related measures. # Highly relevant documents are more useful when appearing earlier in a search engine result list (have higher ranks) # Highly relevant documents are more useful than marginally relevant documents, which are in turn more useful than non-relevant documents. DCG originates f ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Precision (information Retrieval)
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Both precision and recall are therefore based on relevance. Consider a computer program for recognizing dogs (the relevant element) in a digital photograph. Upon processing a picture which contains ten cats and twelve dogs, the program identifies eight dogs. Of the eight elements identified as dogs, only five actually are dogs (true positives), while the other three are cats (false positives). Seven dogs were missed (false negatives), and seven cats were correctly excluded (true negatives). The program's precision is then 5/8 (true positives / sel ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mean Average Precision
Evaluation measures for an information retrieval (IR) system assess how well an index, search engine or database returns results from a collection of resources that satisfy a user's query. They are therefore fundamental to the success of information systems and digital platforms. The success of an IR system may be judged by a range of criteria including relevance, speed, user satisfaction, usability, efficiency and reliability. However the most important factor in determining a systems effectiveness for users is the overall relevance of results retrieved in response to a query. Evaluation measures may be categorised in various ways including offline or online, user-based or system-based and include methods such as observed user behaviour, test collections, precision and recall, and scores from prepared benchmark test sets. Evaluation for an information retrieval system should also include a validation of the measures used, i.e. an assessment of how well they measure what they are i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dwell Time (information Retrieval)
In information retrieval, dwell time denotes the time which a user spends viewing a document after clicking a link on a search engine results page (SERP). Dwell time is the duration between when a user clicks on a search engine result, and when the user returns from that result, or is otherwise seen to have left the result. It is a relevance indicator of the search result correctly satisfying the intent of the user. Short dwell times indicate the user's query intent was not satisfied by viewing the result. Long dwell times indicate the user's query intent was satisfied. Google Google LLC () is an American Multinational corporation, multinational technology company focusing on Search Engine, search engine technology, online advertising, cloud computing, software, computer software, quantum computing, e-commerce, ar ... has used dwell time in page ranking. References Information retrieval evaluation Internet search engines {{web-software-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
