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Social Commerce Hybrid Product Recommender

by Rahul Hooda, Kulvinder Singh, Sanjeev Dhawan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 12
Year of Publication: 2014
Authors: Rahul Hooda, Kulvinder Singh, Sanjeev Dhawan
10.5120/17581-8419

Rahul Hooda, Kulvinder Singh, Sanjeev Dhawan . Social Commerce Hybrid Product Recommender. International Journal of Computer Applications. 100, 12 ( August 2014), 43-49. DOI=10.5120/17581-8419

@article{ 10.5120/17581-8419,
author = { Rahul Hooda, Kulvinder Singh, Sanjeev Dhawan },
title = { Social Commerce Hybrid Product Recommender },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 12 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 43-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number12/17581-8419/ },
doi = { 10.5120/17581-8419 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:29:49.439578+05:30
%A Rahul Hooda
%A Kulvinder Singh
%A Sanjeev Dhawan
%T Social Commerce Hybrid Product Recommender
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 12
%P 43-49
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The vision for Web 3. 0 (also known as Semantic Web) is the ability to create meaning out of huge quantity of qualitative data. Existing data can be interconnected for further uses. Web 2. 0 focused on the users interaction with others whereas Web 3. 0 focus more on the users themselves. The advantages of Semantic Web and E-commerce give rise to social commerce (also referred as f-commerce). The future of business lies on the "social" factor and it is this factor which gives rise to a new kind of connected consumers who are becoming influential in their own right. This paper explores a very specific instance of Semantic Web – Social Recommender System. This paper discusses the likelihood of converting social data into quantitative information and using this information to power social recommendations. This paper first outlines the benefits of social commerce over e-commerce platform. Then the related literature work regarding hybrid recommenders is discussed. Next it is discussed how to predict ratings from a user-item rating network and friend's network and then how to unify similarity matrices obtained from different networks. And lastly this paper covers the social hybrid product recommender algorithm and its experimental evaluations to predict its efficiency.

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Index Terms

Computer Science
Information Sciences

Keywords

Social commerce hybrid product recommender f-commerce homophily Cosine similarity Smith Waterman string similarity measure unipartite graph bipartite graph

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