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The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users being identified except by numbers assigned for the contest.
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.
A dataset for NLP and climate change media researchers The dataset is made up of a number of data artifacts (JSON, JSONL & CSV text files & SQLite database) Climate news DB, Project's GitHub repository [395] ADGEfficiency Climatext Climatext is a dataset for sentence-based climate change topic detection. HF dataset [396] University of Zurich ...
The effectiveness of recommendation approaches is then measured based on how well a recommendation approach can predict the users' ratings in the dataset. While a rating is an explicit expression of whether a user liked a movie, such information is not available in all domains.
The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings. The prize would be awarded to the team achieving over 10% improvement over Netflix's own Cinematch algorithm. The team "Gravity" was the front runner during January—May 2007. [2]
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In a recommendation system where everyone can give the ratings, people may give many positive ratings for their own items and negative ratings for their competitors'. It is often necessary for the collaborative filtering systems to introduce precautions to discourage such manipulations.
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