2016
DOI: 10.1016/j.procs.2016.02.002
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User Profiling for University Recommender System Using Automatic Information Retrieval

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Cited by 19 publications
(14 citation statements)
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“…According to [3] [15], Profiling can be defined as the 'process of Extracting, Integrating and Identifying the keywordbased information, in order to produce a structured Profile, and visualize the knowledge outside of these results. It's a major concept for retrieving the user pertinent information and solving difficult problems of a recommender system, such as items classification according to an individual's interest.…”
Section: Student Profilementioning
confidence: 99%
See 3 more Smart Citations
“…According to [3] [15], Profiling can be defined as the 'process of Extracting, Integrating and Identifying the keywordbased information, in order to produce a structured Profile, and visualize the knowledge outside of these results. It's a major concept for retrieving the user pertinent information and solving difficult problems of a recommender system, such as items classification according to an individual's interest.…”
Section: Student Profilementioning
confidence: 99%
“…It's a major concept for retrieving the user pertinent information and solving difficult problems of a recommender system, such as items classification according to an individual's interest. [3].…”
Section: Student Profilementioning
confidence: 99%
See 2 more Smart Citations
“…Often this information is gathered into the profile through something explicit, like a questionnaire that is completed before recommendations can be made [35]. The problem has also been solved through user profiling systems that pull information about the user from social media [26]. Without probing the user for more information, the recommender is left with only two options: the recommender can determine user influences through inferring from behavioral data, or the recommender can assume every user is the same and use weights that are predetermined by the programmer and hard coded into the software.…”
Section: Content-based Recommendationmentioning
confidence: 99%