TY  - JOUR
T1  - Implementing Multimedia Information Retrieval using Memory-Based Collaborative Filtering
AU - Umoren, Imeh AU - Gilean, Onukwugha AU - Odii, Juliet 
JO  - Asian Journal of Information Technology
VL  - 20
IS  - 2
SP  - 60
EP  - 76
PY  - 2021
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2021.60.76
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2021.60.76
KW  - Intelligent agents
KW  -Collaborative Filtering (CF)
KW  -memory-based CF and Jaccard similarity algorithm
AB  - As the amount of information available to
users on the internet increases geometrically, several
approaches are required to assist the user in finding and
retrieving relevant information. Intelligent agents with the
capacity to learn user’s profile towards efficient sentiment
analysis are one solution to this problem. Collaborative
Filtering (CF) is one of the most successful recommended
approaches used in academia and industry for making
automatic predictions (filtering) about the interests of a
user by collecting preferences or taste information
from many users (collaboration). This work applies
Memory-Based CF using Jaccard similarity algorithm in
electronic commerce to develop a recommendation
system for analyzing user data and extracting user
information for accurate predictions of user preferences
based on user’s behavior in a Business-to-Consumer
(B2C) E-commerce store. The results outcome indicates
that CF as a classical method of information retrieval can
be used in helping people deal with information overload
as the technique reduces the time spent searching for
relevant information and also increases the accuracy of
retrieval. Furthermore, the results from predictions of
user’s interests through recommendation lists are useful
for enhance customer’s loyalty and higher marketing
rates.
ER  -