With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems RS provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
%0 Journal Article
%1 noauthororeditor
%A Pareek, Dr. Jyoti
%A Jhaveri, Ms. Maitri
%A Kapasi, Mr. Abbas
%A Trivedi, Mr. Malhar
%D 2012
%J International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)
%K (CF) (RS) Collaborative Filtering Recommendation System User networking preferences social
%N 5
%P 45-54
%R 10.5121/ijcseit.2012.2505
%T Recommendation System Using Social Networking
%U http://airccse.org/journal/ijcseit/papers/2512ijcseit05.pdf
%V 2
%X With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems RS provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
@article{noauthororeditor,
abstract = {With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
},
added-at = {2018-05-07T13:44:11.000+0200},
author = {Pareek, Dr. Jyoti and Jhaveri, Ms. Maitri and Kapasi, Mr. Abbas and Trivedi, Mr. Malhar},
biburl = {https://www.bibsonomy.org/bibtex/2434fae2e5747edcd0c3b72fb7b7324ac/ijcseit},
doi = {10.5121/ijcseit.2012.2505},
interhash = {4a78f260d43d8d3a8324613719d20851},
intrahash = {434fae2e5747edcd0c3b72fb7b7324ac},
issn = {2231-3117 [Online] ; 2231-3605 [Print]},
journal = {International Journal of Computer Science, Engineering and Information Technology (IJCSEIT)},
keywords = {(CF) (RS) Collaborative Filtering Recommendation System User networking preferences social},
language = {English},
month = oct,
number = 5,
pages = {45-54},
timestamp = {2018-05-07T13:44:11.000+0200},
title = {Recommendation System Using Social Networking
},
url = {http://airccse.org/journal/ijcseit/papers/2512ijcseit05.pdf},
volume = 2,
year = 2012
}