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 ca...

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Main Author: Chughtgai, Muhammad Waseem
Format: Thesis
Published: 2014
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spelling my-utm-ep.484602017-08-07T03:22:42Z Goal-based filtering approach for recommender system 2014 Chughtgai, Muhammad Waseem QA Mathematics 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 Thesis http://eprints.utm.my/id/eprint/48460/ masters Universiti Teknologi Malaysia, Advanced Informatics School Advanced Informatics School
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
topic QA Mathematics
spellingShingle QA Mathematics
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, Advanced Informatics School
granting_department Advanced Informatics School
publishDate 2014
_version_ 1747817395719766016