Goal-based filtering approach for recommender system

Recommender system is a sub part of information retrieval. It decreases the content searching time, increases the user?s interest, and provides recommendations relevant to user?s goals or interests. The major drawback of recommender system is user-based cold-start problem, which has two causes: new-...

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Bibliographic Details
Main Author:	Chughtgai, Muhammad Waseem
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/47969/25/MuhammadWaseemChughtgaiMFC2014.pdf
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id	my-utm-ep.47969
record_format	uketd_dc
spelling	my-utm-ep.479692017-08-11T05:42:35Z Goal-based filtering approach for recommender system 2014-05 Chughtgai, Muhammad Waseem QA75 Electronic computers. Computer science Recommender system is a sub part of information retrieval. It decreases the content searching time, increases the user?s interest, and provides recommendations relevant to user?s goals or interests. The major drawback of recommender system is user-based cold-start problem, which has two causes: new-user zero-rated profile recommendations and average-user low-rated profile recommendations. This research proposes goal-based filtering approach consisting of two hybrid parts; first is content-based filtering with collaborative features to overcome the first cause of user-based cold-start problem. The second is collaborative filtering with k-nearest neighbor scheme features to improve the second cause of user-based cold-start problem. The famous „MovieLens? dataset is rich with its 927 entries of user?s profile data, which makes it a choice for experiments on the proposed work. The cosine similarity and euclidean distance measurements have been used to compute the personalized profile similarities between users profile preferences according to their age, gender and occupation. These similarities are helpful to predict the recommendations to the zero-rated and low-rated users without using any extra information such as ratings, likes or dislikes. The evaluation of experiments has been performed using mean precision with result of 83.44% and mean recall with result of 85.22%. The results demonstrate that percentage of user?s profile similarity measurements is probably effective for web-based system?s recommendations. 2014-05 Thesis http://eprints.utm.my/id/eprint/47969/ http://eprints.utm.my/id/eprint/47969/25/MuhammadWaseemChughtgaiMFC2014.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Chughtgai, Muhammad Waseem Goal-based filtering approach for recommender system
description	Recommender system is a sub part of information retrieval. It decreases the content searching time, increases the user?s interest, and provides recommendations relevant to user?s goals or interests. The major drawback of recommender system is user-based cold-start problem, which has two causes: new-user zero-rated profile recommendations and average-user low-rated profile recommendations. This research proposes goal-based filtering approach consisting of two hybrid parts; first is content-based filtering with collaborative features to overcome the first cause of user-based cold-start problem. The second is collaborative filtering with k-nearest neighbor scheme features to improve the second cause of user-based cold-start problem. The famous „MovieLens? dataset is rich with its 927 entries of user?s profile data, which makes it a choice for experiments on the proposed work. The cosine similarity and euclidean distance measurements have been used to compute the personalized profile similarities between users profile preferences according to their age, gender and occupation. These similarities are helpful to predict the recommendations to the zero-rated and low-rated users without using any extra information such as ratings, likes or dislikes. The evaluation of experiments has been performed using mean precision with result of 83.44% and mean recall with result of 85.22%. The results demonstrate that percentage of user?s profile similarity measurements is probably effective for web-based system?s recommendations.
format	Thesis
qualification_level	Master's degree
author	Chughtgai, Muhammad Waseem
author_facet	Chughtgai, Muhammad Waseem
author_sort	Chughtgai, Muhammad Waseem
title	Goal-based filtering approach for recommender system
title_short	Goal-based filtering approach for recommender system
title_full	Goal-based filtering approach for recommender system
title_fullStr	Goal-based filtering approach for recommender system
title_full_unstemmed	Goal-based filtering approach for recommender system
title_sort	goal-based filtering approach for recommender system
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/47969/25/MuhammadWaseemChughtgaiMFC2014.pdf
_version_	1747817275786788864

Goal-based filtering approach for recommender system

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