Resume information extraction with cascaded hybrid model

Yu, Ke; Guo, Gang; Zhou, Ming

doi:10.3115/1219840.1219902

Cited by 93 publications

(49 citation statements)

References 11 publications

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“…Recently, some studies has tried to create a user profiling model by means of information extraction [7]- [9]. However, information such as user interest can't be extracted from the website using those methods.…”

Section: B User Profiling Enginementioning

confidence: 99%

Profiling-based mobile advertisement as a marketing strategy for GPS- based online traffic map

Pradipta

Endarnoto

Purnama

et al. 2011

Proceedings of the 2011 International Conference on Electrical Engineering and Informatics

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Abstract-The emergence of mobile computing is inevitably followed by mobile advertising: advertising that target mobile device, such as feature phones, smartphones and tablets. However, the majority of mobile advertising is still relying on the traditional approach: to send an advertisement to as many people as possible, in hope that some of them will be interested in the advertisement and in turn buying the promoted product or service. The problem with this method is that each people have their own preferences, so that kind of strategy won't get an optimum result. Furthermore, with traditional strategy there is very low chance of people getting the right advertisement in the right place.This research proposed a new advertisement system that combined a location based service with user profiling system. In this study, the advertisement system is integrated to an online traffic map mobile application, which also has an RSS feed reader feature. The system will learn user interest by using web mining to analyse browsing history that is taken from the RSS reader. When the user tries to generate a route in the map, the system will automatically fetch advertisements that are located along the route, which suited user interest.With this proposed system, we have successfully created a mobile advertisement that is highly efficient and effective. Moreover, it has gained positive feedback from user by being accurate, beneficial, and non-obtrusive. Better reception of advertisement from user will lead to the increasing rate of advertisement success.

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Section: B User Profiling Enginementioning

confidence: 99%

Profiling-based mobile advertisement as a marketing strategy for GPS- based online traffic map

Pradipta

Endarnoto

Purnama

et al. 2011

Proceedings of the 2011 International Conference on Electrical Engineering and Informatics

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“…A typical way for representing a researcher's interests is to create a list of relevant keywords. Most of the existing methods use predefined rules or specific machine learning models to extract the different types of profile information (Alani et al 2003;Yu et al 2005). Arnetminer relies on rich researcher description created by Web user profiling, i.e.…”

Section: Related Workmentioning

confidence: 99%

Intelligent information processing for building university knowledge base

et al. 2016

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There are many ready-to-use software solutions for building institutional scientific information platforms, most of which have functionality well suited to repository needs. However, there have already been discussions about various problems with institutional digital libraries. As a remedy, an approach that is researcher-centric (rather than documentcentric) has been proposed recently in some systems. This paper is devoted to research aimed at tools for building knowledge bases for university research. We focus on the AI methods that have been elaborated and applied practically within our platform for building such knowledge bases. In particular we present a novel approach to data acquisition and the semantic enrichment of the acquired data. In addition, we present the algorithms applied in the real life system for experts profiling and retrieval.

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“…Collaborative filtering recommendation algorithm can be classified into memory-based and model-based. In the memory-based collaborative filtering recommendation, a user-item rating matrix is usually used as the input [2,3]. Applied in the job recruiting domain, some user behaviors or actions can generate the user-item rating matrix according to the predefined definitions and transition rules.…”

Section: Related Workmentioning

confidence: 99%

Open Source Resume

Bankar¹,

Vishwakarma²,

Sarolkar³

2016

International Journal of Advanced Research in Computer and Comm

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Abstract:The Internet-based recruiting platforms become a primary recruitment channel in most companies. While such platforms decrease the recruitment time and advertisement cost, they suffer from an inappropriateness of traditional information retrieval techniques like the Boolean search methods. Consequently, a vast amount of candidates missed the opportunity of recruiting. The recommender system technology aims to help users in finding items that match their personnel interests; it has a successful usage in e-commerce applications to deal with problems related to information overload efficiently. In order to improve the e-recruiting functionality, many recommender system approaches have been proposed. This article will present a survey of e-recruiting process and existing recommendation approaches for building personalized recommender systems for candidates/job matching.

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Resume information extraction with cascaded hybrid model

Cited by 93 publications

References 11 publications

Profiling-based mobile advertisement as a marketing strategy for GPS- based online traffic map

Profiling-based mobile advertisement as a marketing strategy for GPS- based online traffic map

Intelligent information processing for building university knowledge base

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