Wenxing Hong

Learn More
—In this paper, we first provide a comprehensive investigation of four online job recommender systems (JRSs) from four different aspects: user profiling, recommendation strategies, recommendation output, and user feedback. In particular, we summarize the pros and cons of these online JRSs and highlight their differences. We then discuss the challenges in(More)
In this paper, we propose a dynamic user profile-based job recommender system. To address the challenge that the job applicants do not update the user profile in a timely manner, we update and extend the user profile dynamically based on the historical applied jobs and behaviors of job applicants. In particular, the statistical results of basic features in(More)
Keywords: Taxonomy Recommendation with life cycle Collaborative filtering Long-term profile Short-term profile a b s t r a c t In many E-commerce recommender systems, a special class of recommendation involves recommending items to users in a life cycle. For example, customers who have babies will shop on Diapers.com within a relatively long period, and(More)
In some E-commerce recommender systems, a special class of recommendation involves recommending items to users in a life cycle. For example, customers who have babies will shop on Amazon within a relatively long period, and purchase different products for babies within different growth stages. Traditional recommendation algorithms cannot effectively resolve(More)
News recommendation systems are widely used to address the information overloading problem. Many Web-based news reading services, like Google News and Yahoo! News, have become increasingly prevalent as they help users find interesting articles from news providers that match the users' preference. However, few research efforts have been reported on campus(More)
Recommending online news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Many online readers have their own reading preference on news articles; however, a group of users might be interested in similar fascinating topics. It would be helpful to take into(More)
Online recruiting systems have gained immense attention in the wake of more and more job seekers searching jobs and enterprises finding candidates on the Internet. A critical problem in a recruiting system is how to maximally satisfy the desires of both job seekers and enterprises with reasonable recommendations or search results. In this paper, we(More)
The sparsity of user-item rating matrix will reduce the performance of collaborative filtering algorithm in news recommendation system. In order to overcome the problem, we predict the values of user-item rating matrix combining two approaches: co-clustering and Radial Basis Function network (RBF). Co-clustering algorithm simultaneous cluster the rows and(More)
Finding experts in specified areas is an important task and has attracted much attention in the information retrieval community. Research on this topic has made significant progress in the past few decades and various techniques have been proposed. In this survey, we review the state-of-the-art methods in expert finding and summarize these methods into(More)
Singular Value Decomposition was widely used in recommendation system because of the Netflix Prize competition. The method decomposed the user item rating matrix into two matrix with low rank. In order to avoid overfitting the observed user item ratings. It used ℓ2 regularization method to regularize the learned parameters by penalizing their(More)