Just a girl in Tech

"If an user A has liked the movies "Matrix " and "The Lord of the Rings" and many other users that have liked these two movies also liked "Memento", then it is likely that "Memento" will be recommended to user A." Collaborative Filtering is a type of recommender system widely implemented, and it is known for giving more accurated predictions than other approaches. The basic idea of the algorithms in the collaborative filtering area is to provide recommendations based on what people with similar taste have liked in the past. These people, the neighbors, are selected by comparing the user's past preferences (usually presented as ratings on items). So, by measuring the ratings similarity its possible to recommend items liked by the neighborhood. There are two major techniques to compare ratings. User-Based Let us consider a user as an N-dimensional vector of ratings, where each cell represents the rating...

"Suggest new items that fit the user’s preference."   Introduction The increasing amount of information in the web has promoted the advance of the recommender systems research area.  These systems help users by offering useful suggestions to them . The aim of Recommender Systems is to provide personalized recommendations, representing a fundamental role on e-commerce (widely used by companies such as Amazon , Netflix and Google ). They highlight items that the users have not yet seen and may appreciate. Such items include books, restaurants, webpages or even lifestyles. A suggestion is usually made based on the user's historical preferences. These preferences may be collected implicitly or explicitly . When a user is buying an item, or entering a web-page, for example, he is giving an implicit preference feedback. In the case of a user giving a rating to an article, he is providing an explicit feedback. A substantial challenge in this ar...