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Learning to rank [1] 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. [2] Training data may, for example, consist of lists of items with some partial order specified between items in ...
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 ...
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.
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).
Nearly a year after Elon Musk first floated the idea of making Twitter’s recommendation algorithm public, the company has posted the source code for its recommendation algorithm on GitHub. In a ...
A simple illustration of the Pagerank algorithm. 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 ...
Meta is also inviting researchers to study its algorithms through a new Content Library and API, which includes public posts, pages, groups and events from Facebook as well as public posts and ...
Discounted cumulative gain ( DCG) is a measure of ranking quality in information retrieval. It is often normalized so that it is comparable across queries, giving Normalized DCG (nDCG or NDCG). NDCG is often used to measure effectiveness of search engine algorithms and related applications. Using a graded relevance scale of documents in a ...