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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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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
http://eprints.utm.my/id/eprint/47969/
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spelling my.utm.479692017-08-11T05:42:35Z http://eprints.utm.my/id/eprint/47969/ Goal-based filtering approach for recommender system 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/47969/25/MuhammadWaseemChughtgaiMFC2014.pdf Chughtgai, Muhammad Waseem (2014) Goal-based filtering approach for recommender system. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computing.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2014
url http://eprints.utm.my/id/eprint/47969/25/MuhammadWaseemChughtgaiMFC2014.pdf
http://eprints.utm.my/id/eprint/47969/
_version_ 1643652420216029184
score 13.211869

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