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Recommendations
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 2 ... [...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 dislikes ... [...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] |
Cold Start (recommender Systems)
Cold start is a potential problem in computer-based information systems which involves a degree of automated data modelling. Specifically, it concerns the issue that the system cannot draw any inferences for users or items about which it has not yet gathered sufficient information. Systems affected The cold start problem is a well known and well researched problem for recommender systems. Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (e-commerce, films, music, books, news, images, web pages) that are likely of interest to the user. Typically, a recommender system compares the user's profile to some reference characteristics. These characteristics may be related to item characteristics ( content-based filtering) or the user's social environment and past behavior (collaborative filtering). Depending on the system, the user can be associated to various kinds of interactions: ratings, bookmarks, purchas ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Online Dating
Online dating, also known as Internet dating, Virtual dating, or Mobile app dating, is a relatively recent method used by people with a goal of searching for and interacting with potential romantic or sexual partners, via the internet. An online dating service is a company that promotes and provides specific mechanisms for the practice of online dating, generally in the form of dedicated websites or software applications accessible on personal computers or mobile devices connected to the internet. A wide variety of unmoderated matchmaking services, most of which are profile-based with various communication functionalities, is offered by such companies. Online dating services allow users to become "members" by creating a profile and uploading personal information including (but not limited to) age, gender, sexual orientation, location, and appearance. Most services also encourage members to add photos or videos to their profile. Once a profile has been created, members can view ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Playlist
A playlist is a list of video or audio files that can be played back on a media player either sequentially or in a shuffled order. In its most general form, an audio playlist is simply a list of songs, but sometimes a loop. The term has several specialized meanings in the realms of television broadcasting, radio broadcasting and personal computers. A playlist can also be a list of recorded titles on a digital video disk. On the Internet, a playlist can be a list of chapters in a movie serial; for example, Flash Gordon in the Planet Mongo is available on YouTube as a playlist of thirteen consecutive video chapters. Radio The term originally came about in the early days of top 40 radio formats when stations would devise (and, eventually, publish) a limited list of songs to be played. The term would go on to refer to the entire catalog of songs that a given radio station (of any format) would draw from. Additionally, the term was used to refer to an ordered list of songs played ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pandora Radio
Pandora is a subscription-based music streaming service owned by Sirius XM Holdings based in Oakland, California, United States. The service carries a focus on recommendations based on the "Music Genome Project" — a means of classifying individual songs by musical traits. The service originally launched in the consumer market as an internet radio service, which would generate personalized channels based on these traits and songs liked by the user; this service is available in an advertising-supported tier, and a subscription-based version. In 2017, the service launched ''Pandora Premium'', an on-demand version of the service more in line with contemporary competitors. The company was founded in 2000 as Savage Beast Technologies, and initially conceived as a business-to-business company licensing the Music Genome Project to retailers as a recommendation platform. In 2005, the company shifted its focus to the consumer market by launching Pandora as an internet radio product. P ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Paul Resnick
Paul Resnick is Michael D. Cohen Collegiate Professor of Information and Associate Dean for Research and Faculty Affairs at the School of Information at the University of Michigan. Education Paul Resnick was born in Michigan and attended the University of Michigan for his undergraduate studies. He received a Ph.D. at the Massachusetts Institute of Technology (MIT) in 1992 in Computer Science. After graduating from MIT, Resnick worked at AT&T Labs and AT&T Bell Labs and was an assistant Professor at the MIT Sloan School of Management. Resnick became an assistant professor at the University of Michigan in 1997, and subsequently became Associate Professor, Professor, and then Associate Dean. Awards Resnick was elected to the CHI Academy in 2017. He received the 2010 ACM Software Systems Award for his work on the GroupLens Collaborative Filtering Recommender System which showed how distributed users could personalize recommendations via ratings. He also received the ACM Special Inter ... [...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] |
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] |
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] |
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] |
