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Collaborative filtering (CF) is a technique used by recommender systems. 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).
A recommender system, or a recommendation system (sometimes replacing system with terms such as platform, engine, or algorithm ), is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user. [1] [2] [3] Recommender systems are particularly useful when an individual needs to ...
Ranking in terms of information retrieval is an important concept in computer science and is used in many different applications such as search engine queries and recommender systems. [3] A majority of search engines use ranking algorithms to provide users with accurate and relevant results. [4]
The percentage shows the perceived importance, and the arrows represent hyperlinks. PageRank ( PR) is an algorithm used by Google Search to rank web pages in their search engine results. It is named after both the term "web page" and co-founder Larry Page. PageRank is a way of measuring the importance of website pages.
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. [1] This family of methods became widely known during the Netflix prize ...
The matching pursuit is an example of a greedy algorithm applied on signal approximation. A greedy algorithm finds the optimal solution to Malfatti's problem of finding three disjoint circles within a given triangle that maximize the total area of the circles; it is conjectured that the same greedy algorithm is optimal for any number of circles.
Netflix Prize. ACM Conference on Recommender Systems. v. t. e. 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.
A pseudorandom number generator ( PRNG ), also known as a deterministic random bit generator ( DRBG ), [1] is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by an initial value ...