Item-item Collaborative Filtering
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Item-item Collaborative Filtering
Item-item collaborative filtering, or item-based, or item-to-item, is a form of collaborative filtering for recommender systems based on the similarity between items calculated using people's ratings of those items. Item-item collaborative filtering was invented and used by Amazon.com in 1998. It was first published in an academic conference in 2001. Earlier collaborative filtering systems based on rating similarity between users (known as user-user collaborative filtering) had several problems: * systems performed poorly when they had many items but comparatively few ratings * computing similarities between all pairs of users was expensive * user profiles changed quickly and the entire system model had to be recomputed Item-item models resolve these problems in systems that have more users than items. Item-item models use rating distributions ''per item'', not ''per user''. With more users than items, each item tends to have more ratings than each user, so an item's average rat ...
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Collaborative Filtering
Collaborative filtering (CF) is a technique used by recommender systems.Francesco Ricci and Lior Rokach and Bracha ShapiraIntroduction to Recommender Systems Handbook Recommender Systems Handbook, Springer, 2011, pp. 1-35 Collaborative filtering has two senses, a narrow one and a more general one. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person ''A'' has the same opinion as a person ''B'' on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person. For example, a collaborative filtering recommendation system for preferences in television programming could make predictions about which television show a user should like given a partial list of that user's tastes (likes or dislikes ...
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Recommender Systems
A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. 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 particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer. Recommender systems are used in a variety of areas, with commonly recognised examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders.Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Bosagh ZadeWTF:The who-to-follow system at Twitter Proceedings of the ...
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Amazon
Amazon most often refers to: * Amazons, a tribe of female warriors in Greek mythology * Amazon rainforest, a rainforest covering most of the Amazon basin * Amazon River, in South America * Amazon (company), an American multinational technology company Amazon or Amazone may also refer to: Places South America * Amazon Basin (sedimentary basin), a sedimentary basin at the middle and lower course of the river * Amazon basin, the part of South America drained by the river and its tributaries * Amazon Reef, at the mouth of the Amazon basin Elsewhere * 1042 Amazone, an asteroid * Amazon Creek, a stream in Oregon, US People * Amazon Eve (born 1979), American model, fitness trainer, and actress * Lesa Lewis (born 1967), American professional bodybuilder nicknamed "Amazon" Art and entertainment Fictional characters * Amazon (Amalgam Comics) * Amazon, an alias of the Marvel supervillain Man-Killer * Amazons (DC Comics), a group of superhuman characters * The Amazon, a ' ...
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Association For Computing Machinery
The Association for Computing Machinery (ACM) is a US-based international learned society for computing. It was founded in 1947 and is the world's largest scientific and educational computing society. The ACM is a non-profit professional membership group, claiming nearly 110,000 student and professional members . Its headquarters are in New York City. The ACM is an umbrella organization for academic and scholarly interests in computer science ( informatics). Its motto is "Advancing Computing as a Science & Profession". History In 1947, a notice was sent to various people: On January 10, 1947, at the Symposium on Large-Scale Digital Calculating Machinery at the Harvard computation Laboratory, Professor Samuel H. Caldwell of Massachusetts Institute of Technology spoke of the need for an association of those interested in computing machinery, and of the need for communication between them. ..After making some inquiries during May and June, we believe there is ample interest to ...
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Star (classification)
Star classification is a type of rating scale utilizing a star glyph or similar typographical symbol. It is used by reviewers for ranking things such as films, TV shows, restaurants, and hotels. For example, a system of one to five stars is commonly used in hotel ratings, with five stars being the highest rating. Historical usage Repeated symbols used for a ranking date to Mariana Starke's 1820 guidebook, which used exclamation points to indicate works of art of special value: ...I have endeavored... to furnish Travellers with correct lists of the objects best worth notice...; at the same time marking, with one or more exclamation points (according to their merit), those works which are deemed peculiarly excellent. ''Murray's Handbooks for Travellers'' and then the ''Baedeker Guides'' (starting in 1844) borrowed this system, using stars instead of exclamation points, first for points of interest and later for hotels. The Michelin restaurant guide introduced a star as a ...
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Similarity Measure
In statistics and related fields, a similarity measure or similarity function or similarity metric is a real-valued function that quantifies the similarity between two objects. Although no single definition of a similarity exists, usually such measures are in some sense the inverse of distance metrics: they take on large values for similar objects and either zero or a negative value for very dissimilar objects. Though, in more broad terms, a similarity function may also satisfy metric axioms. Cosine similarity is a commonly used similarity measure for real-valued vectors, used in (among other fields) information retrieval to score the similarity of documents in the vector space model. In machine learning, common kernel functions such as the RBF kernel can be viewed as similarity functions. Use in clustering In spectral clustering, a similarity, or affinity, measure is used to transform data to overcome difficulties related to lack of convexity in the shape of the data distribut ...
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Normalization (statistics)
In statistics and applications of statistics, normalization can have a range of meanings. In the simplest cases, normalization of ratings means adjusting values measured on different scales to a notionally common scale, often prior to averaging. In more complicated cases, normalization may refer to more sophisticated adjustments where the intention is to bring the entire probability distributions of adjusted values into alignment. In the case of normalization of scores in educational assessment, there may be an intention to align distributions to a normal distribution. A different approach to normalization of probability distributions is quantile normalization, where the quantiles of the different measures are brought into alignment. In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets i ...
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Recommender System
A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. 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 particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer. Recommender systems are used in a variety of areas, with commonly recognised examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders.Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Bosagh ZadeWTF:The who-to-follow system at Twitter Proceedings of the ...
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Weight Function
A weight function is a mathematical device used when performing a sum, integral, or average to give some elements more "weight" or influence on the result than other elements in the same set. The result of this application of a weight function is a weighted sum or weighted average. Weight functions occur frequently in statistics and analysis, and are closely related to the concept of a measure. Weight functions can be employed in both discrete and continuous settings. They can be used to construct systems of calculus called "weighted calculus" and "meta-calculus".Jane Grossma''Meta-Calculus: Differential and Integral'' , 1981. Discrete weights General definition In the discrete setting, a weight function w \colon A \to \R^+ is a positive function defined on a discrete set A, which is typically finite or countable. The weight function w(a) := 1 corresponds to the ''unweighted'' situation in which all elements have equal weight. One can then apply this weight to various concep ...
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Linear Regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called '' simple linear regression''; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable. In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on ...
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