Recommendation framework: Improving automated collaborative filtering by trusted category

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2015.

Detalhes bibliográficos
Autor principal: Noor, Md Sadaf
Outros Autores: Ahmed, Tarem
Formato: Tese
Idioma:en_US
Publicado em: Brac University 2021
Assuntos:
Acesso em linha:http://hdl.handle.net/10361/14813
id 10361-14813
record_format dspace
spelling 10361-148132021-07-15T21:01:27Z Recommendation framework: Improving automated collaborative filtering by trusted category Noor, Md Sadaf Ahmed, Tarem Department of Electrical and Electronic Engineering, Brac University Recommended System Recommender Trusted Category This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2015. Cataloged from PDF version of thesis. Includes bibliographical references (pages 40-46). The key idea behind user user automated collaborative filtering is that it predicts item's rating based on similar users who share the same taste by rating item similarly. Automated collaborative filtering (acf) is proposed on a hypothesis that users with similar rating will also have similar rating in everything. People often agree on an idea, or on a group of ideas but it is very rare that agree on everything. So in this thesis we suggest that this is one of the main causes behind huge noises while working with a large number of neighbors in acf. In this thesis we are proposing a method where at the time of calculating acf we will only consider a subset of products based on their category that we trust based on users previous ratings. Usually when a developer has to build a recommendation system he has to start writing code from scratch like in the early days of web development, which consumes a lot of time and development energy so we tried to build a framework for our updated acf that provides the building blocks for a recommendation system as APIs. By describing models of their recommender in simple JSON format, within a few commands and using provided API support, programmers can easily get a customizable generic service running. All the input and output communications are done using RESTful APIs so that the system can communicate with any part of the whole system. Md Sadaf Noor B. Electrical and Electronic Engineering 2021-07-15T12:38:08Z 2021-07-15T12:38:08Z 2015 2015-12 Thesis ID: 11121023 http://hdl.handle.net/10361/14813 en_US 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. 46 Pages application/pdf Brac University
institution Brac University
collection Institutional Repository
language en_US
topic Recommended System
Recommender
Trusted Category
spellingShingle Recommended System
Recommender
Trusted Category
Noor, Md Sadaf
Recommendation framework: Improving automated collaborative filtering by trusted category
description This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2015.
author2 Ahmed, Tarem
author_facet Ahmed, Tarem
Noor, Md Sadaf
format Thesis
author Noor, Md Sadaf
author_sort Noor, Md Sadaf
title Recommendation framework: Improving automated collaborative filtering by trusted category
title_short Recommendation framework: Improving automated collaborative filtering by trusted category
title_full Recommendation framework: Improving automated collaborative filtering by trusted category
title_fullStr Recommendation framework: Improving automated collaborative filtering by trusted category
title_full_unstemmed Recommendation framework: Improving automated collaborative filtering by trusted category
title_sort recommendation framework: improving automated collaborative filtering by trusted category
publisher Brac University
publishDate 2021
url http://hdl.handle.net/10361/14813
work_keys_str_mv AT noormdsadaf recommendationframeworkimprovingautomatedcollaborativefilteringbytrustedcategory
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