• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.19-26
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• 2011

Users want to receive customized service using users' personal device. To fulfill this requirement, the mobile device has to support a lot of functions. However, the mobile device has limitations such as tiny display screens. To solve this limitation problem and provide customized service to users, this paper proposes the environment to provide services by sharing resources and the method to recommend user-suitable resources among sharable resources. For the resource recommendation, This paper analyzes user's behavior pattern from usage history and proposes the method for recommending customized resources. This paper also shows that the approach is reasonable one for resource recommendation through the satisfaction evaluation.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.224-226
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• 2012

최근 스마트폰의 대중화와 더불어 스마트폰 애플리케이션의 공급과 수요 또한 활성화 되고 있다. 이에 스마트폰의 애플리케이션 시장 또한 활성화 되었다. 하지만 기하급수적으로 증가한 애플리케이션에 사용자가 자신에게 적합한 애플리케이션을 선택하기가 용이하지 않다. 이에 본 논문에서는 사용자 개인 정보와 감정을 이용한 애플리케이션 추천 시스템을 제안한다. 사용자 정보와 감정을 k-means 알고리즘을 이용하여 군집화를 시켜주었으며 사용자가 평가한 애플리케이션에 대한 만족도를 이용하여 유사도를 검출 및 추천하기 위하여 피어슨 상관계수와 교차추천을 이용하였다. 또한 협업 필터링의 신규 사용자에 대한 초기 평가치 부재에 의한 콜드 스타트(cold-start) 문제를 해결하기 위해 신규 사용자의 개인정보와 감성정보를 활용하여 기존 사용자와의 유사도를 비교한다. 이웃사용자를 추출하고 이웃사용자로부터 추천을 받는다. 즉, 추천시스템 데이터베이스 내의 정보가 충분한 사용자에게는 협업필터링을 그렇지 않은 신규 사용자에게는 협업필터링 대신 제시한 방법을 적용하는 하이브리드 추천 방법을 제안하였다.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.157-172
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• 2013

There are many recommendation systems available to provide users with personalized services. Among them, the most frequently used in electronic commerce is 'collaborative filtering', which is a technique that provides a process of filtering customer information for the preparation of profiles and making recommendations of products that are expected to be preferred by other users, based on such information profiles. Collaborative filtering systems, however, have in their nature both technical issues such as sparsity, scalability, and transparency, as well as security issues in the collection of the information that becomes the basis for preparation of the profiles. In this paper, we suggest a movie recommendation system, based on the selection of optimal personal propensity variables and the utilization of a secure collaborating filtering system, in order to provide a solution to such sparsity and scalability issues. At the same time, we adopt 'push attack' principles to deal with the security vulnerability of collaborative filtering systems. Furthermore, we assess the system's applicability by using the open database MovieLens, and present a personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the selection of optimal personalization factors and the embodiment of a safe collaborative filtering system.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.827-829
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• 2000

오늘날 인터넷 사용자들은 정보의 홍수 속에 놓여있다. 웹사이트에 들어가면 대부분은 자신과 관련 없는 정보들이 쏟아진다. 따라서 인터넷 사용자들의 관심에 맞는 내용을 제 공해주어 시간의 절약과 동시에 사용자에게 가치 있는 정보를 제공할 수 있게 하는 서비스가 필요하다. 이러한 개인화 된 서비스를 제공해주기 위해 사용자에 대한 정확한 분석을 바탕으로 사용자에게 효율적인 서비스를 제공하여야 할 것이다. 따라서 본 논문에서는 사용자 프로파일 및 웹 로그 등을 토대로 각 고객의 성향과 패턴을 정확하게 분석하여, 사용자 각 개인에게 적합하며 효율적인 서비스를 제공해 줄 수 있는 Web Usage Mining 을 통한 사용자 패턴 추출 알고리즘을 개발하고자 한다. 본 논문에서 연구한 Web Usage Mining 알고리즘은 사용자의 웹 사용 습관을 토대로 데이터 마이닝의 과정을 거쳐 사용자의 성향과 관심을 결정하고, 이를 바탕으로 사용자에게 알맞은 내용을 제공할 수 있도록 할 것이다. 이때, 사용자의 정보는 웹 내에서의 행동 중에서 중요하게 사용되는 특정한 페이지를 보는 시간, 웹 서핑 패턴, 전자 상거래 사이트의 경우에는 구매한 상품과 쇼핑 카트에 넣은 상품 등의 관찰된 정보를 기반으로 하며, 개인의 사생활을 침해하지 않는 범위 내에서 이루어지도록 했다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11c
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• pp.2133-2136
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• 2002

인터넷 특성상 방대한 양의 정보와 상품 등으로 사용자들이 원하는 정보를 찾기 위해서 많은 시간을 낭비하고 있는 실정이다. 이러한 사용자의 시간 소모를 중이기 위해서 추천 시스템이 개발되었다. 현재 인터넷 상의 추천 기술 중에서 가장 많이 사용하는 기법으로는 협력적 여과(Collaborative filtering) 방법이다. 그러나, 협력적 추천 방법으로 추천 받기 위해서는 특정수 이상의 아이템에 대한 평가가 필요하며, 또한 비슷한 성향을 가지는 일부 사용자 정보에 근거하여 추천함으로써 나머지 사용자 정보를 무시하는 경향이 있다. 이러한 문제점이 발생되므로 최근에는 데이터 마이닝(Data Mining) 기법 중 연관 규칙(Association Rule)을 이용한 추천 시스템이 개발되고 있다[1,10]. 그러나, 연관 규칙 기법은 개인별 사용자의 성향을 반영하지 못하는 단점이 있다[4]. 연관 규칙은 단지 대용량 데이터 베이스에서 아이템간의 지지도(Support)와 신뢰도(Confidence)에 근거하여 규칙을 발견하는 특징을 가지고 있기 때문이다. 즉 개인성향을 무시하고 아이템간의 연관성만을 근거로 하여 아이템을 추천하기 때문이다. 본 논문에서는 효율적인 연관 규칙을 이용한 개인화 추천 시스템을 구현하기 위해서 연관 규칙과 여과 방법을 통합한 시스템을 제안한다. 본 시스템에 대하여 성능 비교 실험을 수행함으로써 제안한 방법의 타당성을 제시한다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.83-90
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• 2012

In e-commerce systems that deal with a large number of items, the function of personalized recommendation is essential. Collaborative filtering that is a successful recommendation algorithm, suffers from the sparsity, cold-start, and scalability restrictions. Additionally, this work raises a new flaw of the algorithm, inconsistent performance of recommendation. This is also not measurable by the current MAE-based evaluation that does not consider the deviation of prediction error, and furthermore is performed independently of precision and recall measurement. To evaluate the collaborative filtering comprehensively, this work proposes an extended evaluation model that includes the current criteria such as MAE, Precision, Recall, deviation, and applies it to cluster-based combined collaborative filtering.

• Journal of the Korean Society for information Management
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• v.26 no.1
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• pp.81-105
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• 2009

In this paper, we propose user contents using behavior and location information on contents on various channels, such as web, IPTV, for contents distribution. With methods to build user and contents profiles, contents using behavior as an implicit user feedback was applied into machine learning procedure for updating user profiles and contents preference. In machine learning procedure, contents-based and collaborative filtering methods were used to analyze user's contents preference. This study proposes contents location information on web sites for final recommendation contents as well. Finally, we refer to a generalized recommender system for personalization. With those methods, more effective and accurate recommendation service can be possible.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.