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A Web crawler, sometimes called a spider or spiderbot and often shortened to crawler, is an Internet bot that systematically browses the World Wide Web and that is typically operated by search engines for the purpose of Web indexing.
Distributed web crawling is a distributed computing technique whereby Internet search engines employ many computers to index the Internet via web crawling. Such systems may allow for users to voluntarily offer their own computing and bandwidth resources towards crawling web pages. By spreading the load of these tasks across many computers, costs that would otherwise be spent on maintaining large computing clusters are avoided.
Collaborative filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.
A recommender system, or a recommendation system, is a subclass of information filtering system that provide 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.
A Bloom filter is a space-efficient probabilistic data structure, conceived by Burton Howard Bloom in 1970, that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not – in other words, a query returns either "possibly in set" or "definitely not in set". Elements can be added to the set, but not removed ; the more items added, the larger the probability of false positives.
Datalog is a declarative logic programming language. While it is syntactically a subset of Prolog, Datalog generally uses a bottom-up rather than top-down evaluation model. This difference yields significantly different behavior and properties from Prolog. It is often used as a query language for deductive databases. Datalog has been applied to problems in data integration, networking, program analysis, and more.
In computer science, consistent hashing is a special kind of hashing technique such that when a hash table is resized, only keys need to be remapped on average where is the number of keys and is the number of slots. In contrast, in most traditional hash tables, a change in the number of array slots causes nearly all keys to be remapped because the mapping between the keys and the slots is defined by a modular operation.
Andrei Zary Broder is a distinguished scientist at Google. Previously, he was a research fellow and vice president of computational advertising for Yahoo!, and before that, the vice president of research for AltaVista. He has also worked for IBM Research as a distinguished engineer and was CTO of IBM's Institute for Search and Text Analysis.
Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values.
In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search. It differs from conventional hashing techniques in that hash collisions are maximized, not minimized. Alternatively, the technique can be seen as a way to reduce the dimensionality of high-dimensional data; high-dimensional input items can be reduced to low-dimensional versions while preserving relative distances between items.
In computer science, a fingerprinting algorithm is a procedure that maps an arbitrarily large data item to a much shorter bit string, its fingerprint, that uniquely identifies the original data for all practical purposes just as human fingerprints uniquely identify people for practical purposes. This fingerprint may be used for data deduplication purposes. This is also referred to as file fingerprinting, data fingerprinting, or structured data fingerprinting.
Reverse image search is a content-based image retrieval (CBIR) query technique that involves providing the CBIR system with a sample image that it will then base its search upon; in terms of information retrieval, the sample image is very useful. In particular, reverse image search is characterized by a lack of search terms. This effectively removes the need for a user to guess at keywords or terms that may or may not return a correct result. Reverse image search also allows users to discover content that is related to a specific sample image or the popularity of an image, and to discover manipulated versions and derivative works.
Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data may, for example, consist of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data.
In computer science and data mining, MinHash is a technique for quickly estimating how similar two sets are. The scheme was invented by Andrei Broder (1997), and initially used in the AltaVista search engine to detect duplicate web pages and eliminate them from search results. It has also been applied in large-scale clustering problems, such as clustering documents by the similarity of their sets of words.
David Ron Karger is an American computer scientist who is professor and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology.
In graph theory and computer science, a dense subgraph is a subgraph with many edges per vertex. This is formalized as follows: let G = (V, E) be an undirected graph and let S = (VS, ES) be a subgraph of G. Then the density of S is defined to be:
In computing, the count–min sketch is a probabilistic data structure that serves as a frequency table of events in a stream of data. It uses hash functions to map events to frequencies, but unlike a hash table uses only sub-linear space, at the expense of overcounting some events due to collisions. The count–min sketch was invented in 2003 by Graham Cormode and S. Muthu Muthukrishnan and described by them in a 2005 paper.
Moses Samson Charikar is an Indian computer scientist who works as a professor at Stanford University. He was previously a professor at Princeton University. The topics of his research include approximation algorithms, streaming algorithms, and metric embeddings. He is known for the creation of the SimHash algorithm used by Google for near duplicate detection.
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.
Hsiao-Wuen Hon is a Taiwanese-US researcher in speech technology, and coauthor of the book Spoken Language Processing. He is Corporate Vice President of Microsoft and Chairman of Microsoft's Asia-Pacific R&D Group.
References
- ↑ Cyphers, Bennett (2021-03-03). "Google's FLoC Is a Terrible Idea". Electronic Frontier Foundation. Retrieved 2021-04-13.
- ↑ Henzinger, Monika (2006), "Finding near-duplicate web pages: a large-scale evaluation of algorithms", Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, p. 284, doi:10.1145/1148170.1148222, ISBN 978-1595933690, S2CID 207160068 .
- ↑ Charikar, Moses S. (2002), "Similarity estimation techniques from rounding algorithms", Proceedings of the 34th Annual ACM Symposium on Theory of Computing, p. 380, doi:10.1145/509907.509965, ISBN 978-1581134957, S2CID 4229473 .
- ↑ Gurmeet Singh, Manku; Jain, Arvind; Das Sarma, Anish (2007), "Detecting near-duplicates for web crawling", Proceedings of the 16th International Conference on World Wide Web (PDF), p. 141, doi:10.1145/1242572.1242592, ISBN 9781595936547 .
- ↑ Das, Abhinandan S.; Datar, Mayur; Garg, Ashutosh; Rajaram, Shyam; et al. (2007), "Google news personalization: scalable online collaborative filtering", Proceedings of the 16th International Conference on World Wide Web, p. 271, doi:10.1145/1242572.1242610, ISBN 9781595936547, S2CID 207163129 .