1. Distributed Group Recommendation Model for Crowdfunding
Domains
Vineeth Rakesh*, Niranjan Jadhav+ and Chandan K. Reddy+
* Electrical and Computer Engineering , + Computer Science, Wayne State University, Detroit, MI, USA.
.
•CrowdRec performs better than all other
base-lines and the state-of the art group
recommendation model.
•Performance over experienced backers is
better than occasional backers due to the
richness of prior information.
•Incorporating time-dependent prior
information such as increase in backer,
rewards and funds provides a significant boost
to the performance of the model.
1)Kickstarter: a highly heterogeneous domain: Users’
decision to back a project is determined by a diverse set of
features namely:
•Social group, personal preference, geo-location and the
real-time status of projects.
1)Influence from Social Group: Due to Pervasive growth of
social media, users’ decision to back a project depends not
only on their personal interests, but also on their
relationship to a social group of peer investors they
communicate with.
•Despite the huge success of crowdfunding platforms, not
every project is successful in reaching its funding goal.
•Recommendation systems that suggest suitable projects to
Crowdfunding investors can address this problem.
•We propose a probabilistic recommendation called
CrowdRec that suggests suitable projects to a Group (or
community) of crowdfunding investors.
•Propose a group-recommendation model for crowdfunding
domains, which incorporates the dynamic-status of the on-going
projects.
•Incorporate a diverse set of prior information such as: topical
preference (2) geo-location preference (3) social-network links of
backers and (4) various temporal information about the projects.
•Topic Preference: conditional probability of a user
b to back a project in topic t, given t is present in
the backing history of this user.
•Creator Preference: Preference of user towards a
creator of project (mutual trust).
•Geo-Location Preference: probability of a user b
to back a project v, given b and v are from the
same geo-location.
Dynamic priors include (1) the popularity of the
project, and (2) the availability of popular
rewards at specific time .
•Kickstarter Dataset: Our dataset spans from 12/15/13 to 12/15/14. The project
dataset was obtained from Kickspy.com and the backers and their profile was obtained
using our web-scraping program.
•We removed canceled or suspended projects and projects with less than one backer
and $100 as a pledged amount. 70,143 projects with over 1 million backers.
•A group consists of backers who have backed the same project.
•We calculate the inner group similarity between the group members using Pearson
correlation co-efficient (PCC) and filter out groups which have PCC less than 0.2.
A VISUAL REPRESENTATION OF THE GENERATIVE PROCESS
DISTRIBUTED ALGORITHM OF CROWDREC MODEL
•Divide the training corpus into P parts, each part is saved on one computer.
•Each computer executes one iteration of the Gibbs sampling algorithm to update
its local model using its local data.
•P local models are summed up to form the global model, which is replicated to the
P computers to support the next iteration.
MOTIVATION
RESEARCH CHALLENGES
RESEARCH CONTRIBUTIONS
DATASET DESCRIPTION
CREATING GROUPS
STATIC PRIORS
DYNAMIC PRIORS
CONCLUSION
Contact & Reference
V. Rakesh, J. Choo, and C. K. Reddy. Project
recommendation using heterogeneous traits in
crowdfunding. ICWSM, 2015.
Email: vineethrakesh@wayne.edu,
niranjan.jadhav@wayne.edu
We thank Dr. Harpreet Singh for his valuable
guidance in this poster presentation.
10 days to go
Day 1
Availability of Rewards
Personal Preference
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40
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%Funds
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68
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Day 5 Day 10 Day 15 Day 20 Day 30
Film TheaterArts
Geo-LocationTopical Preference
Should
I Back
EndofProject
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