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A Computer Science Comprehensive Exercise
Carleton College, Northfield, MN.
About the Project:
Applications of recommender systems can be found outside the online retail trade, although that is one of the most popular places to find them. The comprehensive exercise ("comps") assignment for our group was to build a collaborative filtering system to recommend courses for students at Carleton College. The end product would allow a current student to enter his or her transcript and - based on which classes had been taken and what grades had been earned - a list of classes in which the student would potentially do well would be returned. Clearly, there are some ethical issues at stake here, as the group would have to have access to old transcript data from real Carleton students in order to build a working recommender. Privacy both from the comps group and the end user was a serious challenge. After exploring several anonymity-preserving algorithms, the group expanded to include datasets of movie ratings provided by MovieLens and Netflix. Also available is an account of the challenges we faced in terms of data storage and the solutions we used.
https://www.cs.carleton.edu/cs_comps/0607/recommend/recommender/index.html
#Carleton #Implementation
کانال آموزش کامپیوتر
@Engineer_Computer
A Computer Science Comprehensive Exercise
Carleton College, Northfield, MN.
About the Project:
Applications of recommender systems can be found outside the online retail trade, although that is one of the most popular places to find them. The comprehensive exercise ("comps") assignment for our group was to build a collaborative filtering system to recommend courses for students at Carleton College. The end product would allow a current student to enter his or her transcript and - based on which classes had been taken and what grades had been earned - a list of classes in which the student would potentially do well would be returned. Clearly, there are some ethical issues at stake here, as the group would have to have access to old transcript data from real Carleton students in order to build a working recommender. Privacy both from the comps group and the end user was a serious challenge. After exploring several anonymity-preserving algorithms, the group expanded to include datasets of movie ratings provided by MovieLens and Netflix. Also available is an account of the challenges we faced in terms of data storage and the solutions we used.
https://www.cs.carleton.edu/cs_comps/0607/recommend/recommender/index.html
#Carleton #Implementation
کانال آموزش کامپیوتر
@Engineer_Computer