000			01789nam a22001217a 4500
999			_c65935 _d65932
100			_aShabab Akram _a15MSSE13 _aSupervisor - Dr. Imtiaz Ali Halepoto
245			_aRecommender System Using Collaborative Filtering
260			_aNawabshah: _bQUEST, _c2019.
500			_aABSTRACT A recommender system is a subclass of information filtering system that is used to predict the value or priority of an item, for example in shopping online. One of the famous classes to recommend an item or value is the collaborative filtering, which presents the product according to the interest/preferences of the active user. Unfortunately, this technique suffers from many problems like sparsity and scalability for huge databases, which contains huge number of users and items. To design an efficient recommender systems the techniques of union and intersection are most widely used. This work presents an analysis about collaborative filtering techniques. The techniques used are union, -intersection and neighborhood. For the implementation a local database of movies is created. After the implementation these three techniques, the neighborhood technique provides greater accuracy when compared with union and intersection. The collaborative filtering through neighborhood method also recommends all of the unwatched movies. However in terms of diversity of choice the union based technique provided a better user experience through recommendation of movies from different categories. In terms of popularity, the neighborhood collaborative filtering also includes the more number of less popular movies in the recommendations when compared with the union and intersection techniques.
700			_aDepartment of Information Technology
856			_uhttps://tinyurl.com/y7cw25bk
942			_cTHESIS