• The Journal of Society for e-Business Studies
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• v.13 no.4
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• pp.173-191
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• 2008

As volumes of electronic commerce increase rapidly, customers are faced with information overload, and it becomes difficult for them to find necessary information and select what they need. In this situation, recommender systems can help the customers search and select the products and services they need more conveniently. These days, the recommender systems play important roles in customer relationship management. In this research, we develop a recommender system that recommends the books to the customers of Internet bookstore. In previous researches on recommender systems, collaborative filtering technique has been often employed. For the collaborative filtering technique to be used, the rating scores on books given by previous purchasers have to be collected. However, the collection of rating scores is not an easy task in reality. Therefore, in this research, we employed case-based reasoning technique that can work only with the book purchase history of customers. The accuracy of recommendation of the resulting book recommender system was about 40% on the level 3 classification code.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.3
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• pp.193-202
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• 2010

The recommender system is a typical software solution for personalized services which are now popular in e-commerce sites. Most of the existing recommender systems are based on customers' explicit rating data on items (e.g., ratings on movies), and it is only recently that recommender systems based on implicit ratings have been proposed as a better alternative. Implicit ratings of a customer on those items that are clicked but not purchased can be inferred from the customer's navigational and behavioral patterns. In this article, a dimensionality reduction (DR) technique is newly applied to the implicit rating-based recommender system, and its effectiveness is assessed using an experimental e-commerce site. The experimental results indicate that the performance of the proposed approach is superior or at least similar to the conventional collaborative filtering (CF)-based approach unless the number of recommended products is 'large.' In addition, the proposed approach requires less memory space and is computationally more efficient.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.153-163
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• 2004

Many recommender systems are based on Content-based Filtering and Social Filtering Both methods have their own advantages and disadvantages, and they complement each other rather than compete. So incorporating of both methods can make the better system and combination technique controls the quality of the entire recommender system. In this paper, we presented each user has his own tendency to decide which is the better recommendation for himself among the various recommendation results, and suggested the Personalized combination technique. To represent user tendency, we defined and used loyalty, diversity and pioneerity and showed by experiments that our combination technique is useful. This combination technique improved the average coverage 23% and for the ceiling 40%.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.75-87
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• 2016

With the exploration of digital academic information, it is certainly required to develop more effective academic contents recommender system in order to accommodate increasing needs for accessing more personalized academic contents. Considering historical usage data, the academic content recommender system recommends personalized academic contents which corresponds with each user's preference. So, the academic content recommender system effectively increases not only the accessibility but also usability of digital academic contents. In this paper, we propose the new journal recommendation technique based on information of journal subscription and web usage logs in order to properly recommend more personalized academic contents. Our proposed recommendation method predicts user's preference with the institution similarity, the journal similarity and journal importance based on citation relationship data of references and finally compose institute-oriented recommendations. Also, we develop a recommender system prototype. Our developed recommender system efficiently collects usage logs from distributed web sites and processes collected data which are proper to be used in proposed recommender technique. We conduct compare performance analysis between existing recommender techniques. Through the performance analysis, we know that our proposed technique is superior to existing recommender methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.61-68
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• 2012

Recommender Systems have gained much popularity among researchers and is applied in a number of applications. The exponential growth of users and products poses some key challenges for recommender systems. Recommender Systems mostly suffer from scalability and accuracy. The accuracy of Recommender system is somehow inversely proportional to its scalability. In this paper we proposed a Context Aware Hybrid Recommender System using matrix reduction for Hybrid model and clustering technique for predication of item features. In our approach we used user item-feature rating, User Demographic information and context information i.e. specific time and day to improve scalability and accuracy. Our Algorithm produce better results because we reduce the dimension of items features matrix by using different reduction techniques and use user demographic information, construct context aware hybrid user model, cluster the similar user offline, find the nearest neighbors, predict the item features and recommend the Top N- items.

• Journal of Information Technology Applications and Management
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• v.16 no.1
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• pp.97-113
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• 2009

Collaborative filtering(CF) has been a very successful approach for building recommender system, but its widespread use has exposed to some well-known problems including sparsity and scalability problems. In order to mitigate these problems, we propose two novel models for improving the typical CF algorithm, whose names are ISCF(Item-Selected CF) and USCF(User-Selected CF). The modified models of the conventional CF method that condense the original dataset by reducing a dimension of items or users in the user-item matrix may improve the prediction accuracy as well as the efficiency of the conventional CF algorithm. As a tool to optimize the reduction of a user-item matrix, our study proposes genetic algorithms. We believe that our approach may relieve the sparsity and scalability problems. To validate the applicability of ISCF and USCF, we applied them to the MovieLens dataset. Experimental results showed that both the efficiency and the accuracy were enhanced in our proposed models.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.819-830
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• 2008

Web recommender system was suggested in order to solve the problem which is cause by overflow of information. Collaborative filtering is the technique which predicts and recommends the suitable goods to the user with collection of preference information based on the history which user was interested in. However, there is a difficulty of recommendation by lack of information of goods which have less popularity. In this paper, it has been researched the way to select the sparsity of goods and the preference in order to solve the problem of recommender system's sparsity which is occurred by lack of information, as well as it has been described the solution which develops the quality of recommender system by selection of customers who were interested in.

• Archives of design research
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• v.16 no.2
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• pp.23-34
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• 2003

The need for personalized web site design has been increased these days. Current approach for personalized web site design is easily applied to web site with their cost-effective feature, but is hard to provide a more refined personalized service due to its lack of accumulation of user data. In this study, the design recommender system is investigated as a more advanced method for web site design personalization. We provide an overview of current recommender systems, and then outlined a newly developed design recommender system, which employs collaborative filtering technique to provide tailored recommendation for users.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.42-48
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• 2020

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.83-90
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• 2019

People constantly strive for better choices. For this reason, recommender system has been developed since the early 1990s. In particular, collaborative filtering technique has shown excellent performance in the field of recommender systems, and research of recommender system using machine learning has been actively conducted. This study constructs recommender system using collaborative filtering and machine learning based on stacking model which is one of ensemble methods. The results of this study confirm that the recommender system with the stacking model is useful in aspects of recommender performance. In the future, the model proposed in this study is expected to help individuals or firms to make better choices.