dc.contributor.advisor | Mostakim, Moin | |
dc.contributor.author | Sajid, Md.Ashiq Ul Islam | |
dc.contributor.author | Romeo, Raihan | |
dc.contributor.author | Farid, Sheikh | |
dc.contributor.author | Tasin, Mohammed Shahrier | |
dc.date.accessioned | 2024-06-23T11:06:51Z | |
dc.date.available | 2024-06-23T11:06:51Z | |
dc.date.copyright | ©2023 | |
dc.date.issued | 2023-09 | |
dc.identifier.other | ID 20201225 | |
dc.identifier.other | ID 19301055 | |
dc.identifier.other | ID 20201221 | |
dc.identifier.other | ID 19101255 | |
dc.identifier.uri | http://hdl.handle.net/10361/23518 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 28-29). | |
dc.description.abstract | Recommender systems play a role, in helping consumers by suggesting products.
These systems rely on algorithms, many of which are based on machine learning
from the intelligence field. However, choosing the algorithm for a recommender
system can be pretty challenging due to the range of options available. Additionally
developing systems often comes with obstacles. Raises questions.One major
challenge in creating a recommendation system for an e-commerce business is the
”cold start” problem. This occurs when there isn’t data in the product dataset.
Consequently accurately recommending products to customers becomes extremely
difficult. The problem arises because there is no user purchase history or item ratings,
for users making it impossible for the system to provide recommendations based
on their preferences.In our research, we focus on addressing this cold start problem
in recommender systems. Our goal is to find solutions using multiple approaches,
including similarity methods and clustering algorithms like Agglomerative Hierarchical
and K-means clustering, and also create a merged recommendation system
from all the approaches. By doing we aim to overcome the cold start issue and assist
businesses in generating accurate recommendations.To conduct our research we use
an unsupervised learning dataset since the cold start problem primarily occurs when
there is no user data available. Our investigation aims to provide insights and suggestions
to address the challenges related to the cold start problem, in recommender
systems. This will benefit businesses. Improve the user experience. | en_US |
dc.description.statementofresponsibility | Md.Ashiq Ul Islam Sajid | |
dc.description.statementofresponsibility | Raihan Romeo | |
dc.description.statementofresponsibility | Sheikh Farid | |
dc.description.statementofresponsibility | Mohammed Shahrier Tasin | |
dc.format.extent | 38 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | 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. | |
dc.subject | Machine learning | en_US |
dc.subject | Product recommendation | en_US |
dc.subject | Clustering | en_US |
dc.subject | Agglomerative clustering | en_US |
dc.subject.lcsh | Data mining | |
dc.subject.lcsh | Machine learning | |
dc.title | Product recommendation system | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B.Sc in Computer Science | |