Wenxing Hong

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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)
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)
Automatic summarization technology based on word frequency statistics takes music comments characteristics into consideration, weights them to produce music label. We get essence of users' comments through summarizing comments of an album. The paper shows algorithm and flow-process diagrams, designs single document summarization (SDS) and compound document(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)
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 purchase different products for babies within different growth stages. Traditional recommendation algorithms produce(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)
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)
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)
In this paper, the expansion of feature points of the linear scale space is transformed into the classification of multi-scale data set within the same scale, which belongs to the classification of scale invariant non-equilibrium .The paper presents a sample approach based on kernel learning to solve classification on imbalance dataset by Support Vector(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)