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Recommendation 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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Filtering System
An information filtering system is a system that removes redundant or unwanted information from an information stream using (semi)automated or computerized methods prior to presentation to a human user. Its main goal is the management of the information overload and increment of the semantic signal-to-noise ratio. To do this the user's profile is compared to some reference characteristics. These characteristics may originate from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach). Whereas in information transmission signal processing filters are used against syntax-disrupting noise on the bit-level, the methods employed in information filtering act on the semantic level. The range of machine methods employed builds on the same principles as those for information extraction. A notable application can be found in the field of email spam filters. Thus, it is not only the information explosion that necessitates so ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pattie Maes
Pattie Maes (born 1961) is a professor in MIT's program in Media Arts and Sciences. She founded and directed the MIT Media Lab's Fluid Interfaces Group. Previously, she founded and ran the Software Agents group. She served for several years as both the head and associate head of the Media Lab's academic program. Prior to joining the Media Lab, Maes was a visiting professor and a research scientist at the MIT Artificial Intelligence Lab. She holds bachelor's and PhD degrees in computer science from the Vrije Universiteit Brussel in Belgium. Maes' areas of expertise are human–computer interaction, intelligent interfaces and ubiquitous computing. Maes is the editor of three books, and is an editorial board member and reviewer for numerous professional journals and conferences. She has received several awards: Newsweek magazine named her one of the "100 people for the new century"; TIME Digital selected her as a member of the ''Cyber-Elite'' (the top 50 technological pioneers of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multi-armed Bandit
In probability theory and machine learning, the multi-armed bandit problem (sometimes called the ''K''- or ''N''-armed bandit problem) is a problem in which a fixed limited set of resources must be allocated between competing (alternative) choices in a way that maximizes their expected gain, when each choice's properties are only partially known at the time of allocation, and may become better understood as time passes or by allocating resources to the choice. This is a classic reinforcement learning problem that exemplifies the exploration–exploitation tradeoff dilemma. The name comes from imagining a gambler at a row of slot machines (sometimes known as "one-armed bandits"), who has to decide which machines to play, how many times to play each machine and in which order to play them, and whether to continue with the current machine or try a different machine. The multi-armed bandit problem also falls into the broad category of stochastic scheduling. In the problem, each mach ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cold Start (computing)
Cold start in computing refers to a problem where a system or its part was created or restarted and is not working at its normal operation. The problem can be related to initialising internal objects or populating cache or starting up subsystems. In a typical web service system the problem occurs after restarting the server, and also when clearing the cache (e.g., after releasing new version). The first requests to the web service will cause significantly more load due to the server cache being populated, the browser cache being cleared, and new resources being requested. Other services like a caching proxy or web accelerator will also need time to gather new resources and operate normally. Similar problem occurs when creating instances in a hosted environment and instances in cloud computing services. Cold start (or cold boot) may also refer to a booting process of a single computer (or virtual machine). In this case services and other startup applications are executed after reb ... [...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] |
Data Collection
Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical science, physical and social sciences, humanities, and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture quality evidence that allows analysis to lead to the formulation of convincing and credible answers to the questions that have been posed. Data collection and validation consists of four steps when it involves taking a census and seven steps when it involves sampling. Regardless of the field of or preference for defining data (Quantitative method, quantitative or Qualitative method, qualitative), accurate data collection is essential to maintain research integrity. The selectio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Implicit Data Collection
Implicit data collection is used in human–computer interaction to gather data about the user in an implicit, non-invasive way. Overview The collection of user-related data in human–computer interaction is used to adapt the computer interface to the end user. The data collected are used to build a user model. The user model is then used to help the application to filter the information for the end user. Such systems are useful in recommender applications, military applications (implicit stress detection) and others. Channels for collecting data The system can record the user's explicit interaction and thus build an MPEG7 usage history log. Furthermore, the system can use other channels to gather information about the user's emotional state. The following implicit channels have been used so far to get the affective state of the end user: * facial activity * posture activity * hand tension and activity * gestural activity * vocal expression * language and choice of words * ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pearson Correlation
In statistics, the Pearson correlation coefficient (PCC, pronounced ) ― also known as Pearson's ''r'', the Pearson product-moment correlation coefficient (PPMCC), the bivariate correlation, or colloquially simply as the correlation coefficient ― is a measure of linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations. As a simple example, one would expect the age and height of a sample of teenagers from a high school to have a Pearson correlation coefficient significantly greater than 0, but less than 1 (as 1 would represent an unrealistically perfect correlation). Naming and history It was developed by Kar ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
K-nearest Neighbors Algorithm
In statistics, the ''k''-nearest neighbors algorithm (''k''-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression. In both cases, the input consists of the ''k'' closest training examples in a data set. The output depends on whether ''k''-NN is used for classification or regression: :* In ''k-NN classification'', the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its ''k'' nearest neighbors (''k'' is a positive integer, typically small). If ''k'' = 1, then the object is simply assigned to the class of that single nearest neighbor. :* In ''k-NN regression'', the output is the property value for the object. This value is the average of the values of ''k'' nearest neighbors. If ''k'' = 1, then the output is simply assigned to the v ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Matrix Factorization (recommender Systems)
Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices. This family of methods became widely known during the Netflix prize challenge due to its effectiveness as reported by Simon Funk in his 2006 blog post, where he shared his findings with the research community. The prediction results can be improved by assigning different regularization weights to the latent factors based on items' popularity and users' activeness. Techniques The idea behind matrix factorization is to represent users and items in a lower dimensional latent space. Since the initial work by Funk in 2006 a multitude of matrix factorization approaches have been proposed for recommender systems. Some of the most used and simpler ones are listed in the following sections. Funk MF The original algorithm proposed by Simo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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 disli ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
