• Journal of Information Technology Services
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• v.20 no.3
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• pp.57-73
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• 2021

Recently, online job websites have been activated as unemployment problems have emerged as social problems and demand for job openings has increased. However, while the online job platform market is growing, users have difficulty choosing their jobs. When users apply for a job on online job websites, they check various information such as job contents and recruitment conditions to understand the details of the job. When users choose a job, they focus on various details related to the job rather than simply viewing and supporting the job title. However, existing online job websites usually recommend jobs using only quantitative preference information such as ratings. However, if recommendation services are provided using only quantitative information, the recommendation performance is constantly deteriorating. Therefore, job recommendation services should provide personalized services using various information about the job. This study proposes a recommended methodology that improves recommendation performance by elaborating on qualitative preference information, such as details about the job. To this end, this study performs a topic modeling analysis on the job content of the user profile. Also, we apply LDA techniques to explore topics from job content and extract qualitative preferences. Experiments show that the proposed recommendation methodology has better recommendation performance compared to the traditional recommendation methodology.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.131-146
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• 2011

It is becoming more essential than ever for office professionals to become competent in information collection/gathering and problem solving in today's global business society. In particular, office professionals do not only assist simple chores but are also forced to make decisions as quickly and efficiently as possible in problematic situations that can end in either profit or loss to their company. Since office professionals rely heavily on their tacit knowledge to solve problems that arise in everyday business situations, it is truly helpful and efficient to refer to similar business cases from the past and share or reuse such previous business knowledge for better performance results. Case-based reasoning(CBR) is a problem-solving method which utilizes previous similar cases to solve problems. Through CBR, the closest case to the current business situation can be searched and retrieved from the case or knowledge base and can be referred to for a new solution. This reduces the time and resources needed and increase success probability. The main purpose of this study is to design a system called COKRS(Case-based reasoning Office Knowledge Recommender System) and develop a prototype for it. COKRS manages cases and their meta data, accepts key words from the user and searches the casebase for the most similar past case to the input keyword, and communicates with users to collect information about the quality of the case provided and continuously apply the information to update values on the similarity table. Core concepts like system architecture, definition of a case, meta database, similarity table have been introduced, and also an algorithm to retrieve all similar cases from past work history has also been proposed. In this research, a case is best defined as a work experience in office administration. However, defining a case in office administration was not an easy task in reality. We surveyed 10 office professionals in order to get an idea of how to define a case in office administration and found out that in most cases any type of office work is to be recorded digitally and/or non-digitally. Therefore, we have defined a record or document case as for COKRS. Similarity table was composed of items of the result of job analysis for office professionals conducted in a previous research. Values between items of the similarity table were initially set to those from researchers' experiences and literature review. The results of this study could also be utilized in other areas of business for knowledge sharing wherever it is necessary and beneficial to share and learn from past experiences. We expect this research to be a reference for researchers and developers who are in this area or interested in office knowledge recommendation system based on CBR. Focus group interview(FGI) was conducted with ten administrative assistants carefully selected from various areas of business. They were given a chance to try out COKRS in an actual work setting and make some suggestions for future improvement. FGI has identified the user-interface for saving and searching cases for keywords as the most positive aspect of COKRS, and has identified the most urgently needed improvement as transforming tacit knowledge and knowhow into recorded documents more efficiently. Also, the focus group has mentioned that it is essential to secure enough support, encouragement, and reward from the company and promote positive attitude and atmosphere for knowledge sharing for everybody's benefit in the company.

• Journal of Advanced Navigation Technology
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• v.18 no.5
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• pp.468-475
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• 2014

The recommend system is getting more difficult real time recommend by lager preference data set, computing power and recommend algorithm. For this reason, recommend system is proceeding actively one's studies toward distribute processing method of large preference data set. This paper studied distribute processing method of large preference data set using hadoop distribute processing platform and mahout machine learning library. The recommend algorithm is used Co-occurrence Matrix similar to item Collaborative Filtering. The Co-occurrence Matrix can do distribute processing by many node of hadoop cluster, and it needs many computation scale but can reduce computation scale by distribute processing. This paper has simplified distribute processing of co-occurrence matrix by changes over from four stage to three stage. As a result, this paper can reduce mapreduce job and can generate recommend file. And it has a fast processing speed, and reduce map output data.

• Information Systems Review
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• v.14 no.3
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• pp.115-129
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• 2012

A matchmaking system is a type of recommender systems that provides a set of dating partners suitable for the user by online. Many matchmaking systems, which are widely used these days, require users to specify their preferences with regards to ideal dating partners based on criteria such as age, job and salary. However, some users are not aware of their exact preferences, or are reluctant to reveal this information even if they do know. Also, users' selection standards are not fixed and can change according to circumstances. This paper suggests a new matchmaking system called Decision Tree based Matchmaking System (DTMS) that automatically adjusts the stated standards of a user by analyzing the characteristics of the people the user chose to contact. AMMS provides recommendations for new users on the basis of their explicit preferences. However, as a user's behavioral records are accumulated, it begins to analyze their hidden implicit preferences using a decision tree technique. Subsequently, DTMS reflects these implicit preferences in proportion to their predictive accuracy. The DTMS is regularly updated when a user's data size increases by a set amount. This paper suggests an architecture for the DTMS and presents the results of the implementation of a prototype.