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Pseudo-relevance Feedback
Relevance feedback is a feature of some information retrieval and recommender 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 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 rele ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Retrieval
Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an Information needs, information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text search, 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; it also stores and manages those documents. Web search engines are the most visible IR applications. Overview An information retrieval process begins when a user enters a query into the sys ... [...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 normalized so that it is comparable across queries, giving Normalized DCG (nDCG or NDCG). NDCG is often used to measure effectiveness of search engine algorithms and related applications. Using a graded relevance scale of documents in a search-engine result set, DCG sums the usefulness, or ''gain'', of the results discounted by their position in the result list. Kalervo Järvelin, Jaana Kekäläinen, "Cumulated gain-based evaluation of IR techniques". ''ACM Transactions on Information Systems'' 20(4), 422–446 (2002) NDCG is DCG normalized by the maximum possible DCG of the result set when ranked from highest to lowest gain, thus adjusting for the different numbers of relevant results for different queries. 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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Internet Search Algorithms
The Internet (or internet) is the global system of interconnected computer networks that uses the Internet protocol suite (TCP/IP) to communicate between networks and devices. It is a network of networks that consists of private, public, academic, business, and government networks of local to global scope, linked by a broad array of electronic, wireless, and optical networking technologies. The Internet carries a vast range of information resources and services, such as the interlinked hypertext documents and applications of the World Wide Web (WWW), electronic mail, internet telephony, streaming media and file sharing. The origins of the Internet date back to research that enabled the time-sharing of computer resources, the development of packet switching in the 1960s and the design of computer networks for data communication. The set of rules (communication protocols) to enable internetworking on the Internet arose from research and development commissioned in the 19 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Query Expansion
Query expansion (QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding. In the context of search engines, query expansion involves evaluating a user's input (what words were typed into the search query area, and sometimes other types of data) and expanding the search query to match additional documents. Query expansion involves techniques such as: * Finding synonyms of words, and searching for the synonyms as well * Finding semantically related words (e.g. antonyms, meronyms, hyponyms, hypernyms) * Finding all the various morphological forms of words by stemming each word in the search query * Fixing spelling errors and automatically searching for the corrected form or suggesting it in the results * Re-weighting the terms in the original query Query expansion is a methodology studied in the field of computer science, particularly within the realm of natural lan ... [...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, 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 (information retrieval), relevance indicator of the search result correctly satisfying the Information needs, 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 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] |
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 most important factor in determining a system's effectiveness for users is the overall relevance of results retrieved in response to a query. The success of an IR system may be judged by a range of criteria including relevance, speed, user satisfaction, usability, efficiency and reliability. 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 intende ... [...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. Written as a formula: \text = \frac Recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Written as a formula: \text = \frac 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 excl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithm
In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically 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 use Conditional (computer programming), conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning). In contrast, a Heuristic (computer science), heuristic is an approach to solving problems without well-defined correct or optimal results.David A. Grossman, Ophir Frieder, ''Information Retrieval: Algorithms and Heuristics'', 2nd edition, 2004, For example, although social media recommender systems are commonly called "algorithms", they actually rely on heuristics as there is no truly "correct" recommendation. As an e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Recommender System
A recommender system (RecSys), or a recommendation system (sometimes replacing ''system'' with terms such as ''platform'', ''engine'', or ''algorithm'') and sometimes only called "the algorithm" or "algorithm", is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer. Modern recommendation systems such as those used on large social media sites make extensive use of AI, machine learning and related techniques to learn the behavior and preferences of each user and categorize content to tailor their feed individually. Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read. Recommender systems are used in a variety of areas, with commonly recognised ex ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Figure Of Merit
A figure of merit (FOM) is a performance metric that characterizes the performance of a device, system, or method, relative to its alternatives. Examples *Absolute alcohol content per currency unit in an alcoholic beverage *accurizing, Accuracy of a rifle *Audio amplifier figures of merit such as gain or efficiency *Battery life of a laptop computer New York Times, June 25, 2009 *Calories per serving *Clock rate of a CPU is often given as a figure of merit, but is of limited use in comparing between different architectures. FLOPS may be a better figure, though these too are not completely representative of the performance of a CPU. *Contrast ratio of an LCD *Frequency response of a Loudspeaker, speaker *Fill factor (solar cell), Fill factor of a solar cell *Image resolutio ... [...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 so called weights, i.e. the variables a, b and c listed below in the Algorithm section. Algorithm The form ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
