popular user
Quarantine Deceiving Yelp's Users by Detecting Unreliable Rating Reviews
Trinh, Viet, More, Vikrant, Zare, Samira, Homayon, Sheideh
Online reviews have become a valuable and significant resource, for not only consumers but companies, in decision making. In the absence of a trusted system, highly popular and trustworthy internet users will be assumed as members of the trusted circle. In this paper, we describe our focus on quarantining deceiving Yelp's users that employ both review spike detection (RSD) algorithm and spam detection technique in bridging review networks (BRN), on extracted key features. We found that more than 80% of Yelp's accounts are unreliable, and more than 80% of highly-rated businesses are subject to spamming.
A Deployed People-to-People Recommender System in Online Dating
Wobcke, Wayne (University of New South Wales) | Krzywicki, Alfred (University of New South Wales) | Kim, Yang Sok (Keimyung University) | Cai, Xiongcai (University of New South Wales) | Bain, Michael (University of New South Wales) | Compton, Paul (University of New South Wales) | Mahidadia, Ashesh (smartAcademic)
Online dating is a prime application area for recommender systems, as users face an abundance of choice, must act on limited information, and are participating in a competitive matching market. This article reports on the successful deployment of a people-to-people recommender system on a large commercial online dating site. The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms. Results taken a few months after deployment show that the recommender system delivered its projected benefits.
Using a Critic to Promote Less Popular Candidates in a People-to-People Recommender System
Krzywicki, Alfred (University of New South Wales) | Wobcke, Wayne (University of New South Wales) | Cai, Xiongcai (University of New South Wales) | Bain, Michael (University of New South Wales) | Mahidadia, Ashesh (University of New South Wales) | Compton, Paul (University of New South Wales) | Kim, Yang Sok (University of New South Wales)
This paper shows how to improve the recommendations of an interaction-based collaborative filtering (IBCF) recommender used in online dating. Previous work has shown that IBCF works well in this domain, although it tends to rank popular candidates highly, which leads to these users receiving a large number of contacts. We address this problem by using a Decision Tree model as a "critic" to re-rank the candidates generated by IBCF, effectively promoting less popular candidates. This method was first evaluated on historical data from a large online dating site and then trialled live on the same site by providing recommendations to a large number of users throughout a 9 week period. The live trial confirmed the consistency of the analysis on historical data and the ability of the method to generate suitable candidates over an extended period. Our recommendations gave higher success rates than those for a control group made with a baseline recommender.