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Amazon Rekognition is a cloud-based software as a ... and algorithms that a user can train on a custom dataset. ... instead of adhering to the recommendation. [15]
The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ...
It is a fairly modern technique inspired by the growing amount of textual information. For application in recommendation system, a common case is the Amazon customer review. Amazon will analyze the feedbacks comments from each customer and report relevant data to other customers for reference.
MovieLens ratings datasets: In the early days of recommender systems, research was slowed down by the lack of publicly available datasets. In response to requests from other researchers, GroupLens released three datasets: [ 32 ] the MovieLens 100,000 rating dataset, the MovieLens 1 million rating dataset, and the MovieLens 10 million rating ...
The recommendations on movies cannot contain any marketing values that can tackle the large number of movie ratings as a "seed dataset". [1] In addition to movie recommendations, MovieLens also provides information on individual films, such as the list of actors and directors of each film.
On September 12, 2018, it was announced that Neptune achieved HIPAA eligibility [7] enabling it to process data sets containing protected health information . On December 12, 2018, it was announced that Amazon Neptune was in-scope for Payment Card Industry Data Security Standard, ISO 9001, ISO 27001, ISO 27017, and ISO 27018 compliance programs ...
In practice, many commercial recommender systems are based on large datasets. As a result, the user-item matrix used for collaborative filtering could be extremely large and sparse, which brings about challenges in the performance of the recommendation. One typical problem caused by the data sparsity is the cold start problem. As collaborative ...
The primary motivation for employing lazy learning, as in the K-nearest neighbors algorithm, used by online recommendation systems ("people who viewed/purchased/listened to this movie/item/tune also ...") is that the data set is continuously updated with new entries (e.g., new items for sale at Amazon, new movies to view at Netflix, new clips ...