Movie recommendation using link prediction
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021.
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| Taal: | English |
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Brac University
2023
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| Online toegang: | http://hdl.handle.net/10361/18930 |
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10361-18930 |
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10361-189302023-07-19T21:03:13Z Movie recommendation using link prediction Dhrubo, Md Alif-uz-zaman Supty, Tasfia Chowdhury Hossin, Md Rakib Rahman, SM Ashfaqur Das, Ananya Parvez, Dr. Mohammad Zavid Patwary, Md Anwarul Kaium Department of Computer Science and Engineering, Brac University Link prediction Recommendation system Graph algorithm Jaccard coefficient Network analyzing Sparse network Potential connection The Naive Bayes Data mining. Computer communication systems. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 23-25). Link prediction is an important task for analyzing movie recommendation which also has applications in other domain like, information retrieval and bioinformatics. Proximity measure quantify the closeness or similarity between nodes in movie rec ommendation and form the basis of a range of applications in social sciences different quality based movie, information about user’s choice, networking and connecting . Recommendation can be effective of link prediction sub-process, with unique nodes (users and items) and connections (similar user/item relationships and user/item interections). Through specific methods and techniques, the recommending systems try to identify the most appropriate items, such as types of information and good and propose the closest to the user’s tastes. One of the easiest and most under standable and authorisation for locating people with the same preferences in the recommendation systems is mutual filtering that provides active performance data based on the ranking of a segment of people. In this model, the process is subject to scalability, with a growing number of users and movies. Across the other hand, when there is little information available on the ratings, it is essential to promote the system’s performance. This study proposes an efficient dynamic graph prediction using link algorithm to predict the user’s choice and recommended the movie based on that link prediction. Temporal information offers link occurrence behavior in the dynamic network, while community clustering shows how strong the connection between two individual nodes is, based on whether they share the same community. These model and methods have achieved higher prediction of recommending. We got better prediction by implementing Jaccard coefficient into methods. Furthermore, in the future, we will use more algorithms to improve the recommending based on the rating of the movies by sorting them for the users. Md Alif-uz-zaman Dhrubo Tasfia Chowdhury Supty Md Rakib Hossin SM Ashfaqur Rahman Ananya Das B. Computer Science 2023-07-19T08:42:16Z 2023-07-19T08:42:16Z 2021 2021-09 Thesis ID: 16101173 ID: 21101001 ID: 17101543 ID: 16101052 ID: 17101382 http://hdl.handle.net/10361/18930 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 25 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Link prediction Recommendation system Graph algorithm Jaccard coefficient Network analyzing Sparse network Potential connection The Naive Bayes Data mining. Computer communication systems. |
| spellingShingle |
Link prediction Recommendation system Graph algorithm Jaccard coefficient Network analyzing Sparse network Potential connection The Naive Bayes Data mining. Computer communication systems. Dhrubo, Md Alif-uz-zaman Supty, Tasfia Chowdhury Hossin, Md Rakib Rahman, SM Ashfaqur Das, Ananya Movie recommendation using link prediction |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2021. |
| author2 |
Parvez, Dr. Mohammad Zavid |
| author_facet |
Parvez, Dr. Mohammad Zavid Dhrubo, Md Alif-uz-zaman Supty, Tasfia Chowdhury Hossin, Md Rakib Rahman, SM Ashfaqur Das, Ananya |
| format |
Thesis |
| author |
Dhrubo, Md Alif-uz-zaman Supty, Tasfia Chowdhury Hossin, Md Rakib Rahman, SM Ashfaqur Das, Ananya |
| author_sort |
Dhrubo, Md Alif-uz-zaman |
| title |
Movie recommendation using link prediction |
| title_short |
Movie recommendation using link prediction |
| title_full |
Movie recommendation using link prediction |
| title_fullStr |
Movie recommendation using link prediction |
| title_full_unstemmed |
Movie recommendation using link prediction |
| title_sort |
movie recommendation using link prediction |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/18930 |
| work_keys_str_mv |
AT dhrubomdalifuzzaman movierecommendationusinglinkprediction AT suptytasfiachowdhury movierecommendationusinglinkprediction AT hossinmdrakib movierecommendationusinglinkprediction AT rahmansmashfaqur movierecommendationusinglinkprediction AT dasananya movierecommendationusinglinkprediction |
| _version_ |
1814306882410512384 |