• Information Systems Review
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• 제8권1호
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• pp.181-201
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• 2006

As the Internet spreads, many people have interests in e-CRM and product recommender systems, one of e-CRM applications. Among various approaches for recommendation, collaborative filtering and content-based approaches have been investigated and applied widely. Despite their popularity, traditional recommendation approaches have some limitations. They require at least one purchase transaction per user. In addition, they don't utilize much information such as demographic and specific personal profile information. This study suggests new hybrid recommendation model using two data mining techniques, association rule and classification, as well as intelligent agent to overcome these limitations. To validate the usefulness of the model, it was applied to the real case and the prototype web site was developed. We assessed the usefulness of the suggested recommendation model through online survey. The result of the survey showed that the information of the recommendation was generally useful to the survey participants.

• The Journal of Society for e-Business Studies
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• 제10권2호
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• pp.109-132
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• 2005

With the flood of multimedia contents over the digital TV channels, the internet, and etc., users sometimes have a difficulty in finding their preferred contents, spend heavy surfing time to find them, and are even very likely to miss them while searching. In this paper we suggests two-step clustering technique using time schema on how the system can recommend the user's preferred contents based on the collaborative filtering that has been proved to be successful when new users appeared. This method maps and recommends users' profile according to the gender and age at the first step, and then recommends a probabilistic item clustering customers who choose the same item at the same time based on time schema at the second stage. In addition, this has improved the accuracy of predictions in recommendation and the efficiency in time calculation by reflecting feedbacks of the result of the recommender engine and dynamically update customers' preference.

• Journal of Korea Society of Industrial Information Systems
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• 제23권6호
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• pp.9-19
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• 2018

As the recent developments in the game industry and people's interest in game streaming become more popular, non-professional gamers are also interested in games and buying them. However, it is difficult to judge which game is the most enjoyable among the games released in dozens every day. Although the game sales platform is equipped with the game recommendation function, it is not accurate because it is used as a means of increasing their sales and recommending users with a focus on their discount products or new products. For this reason, in this paper, we propose a game recommendation system based on the users ratings, which raises the recommendation satisfaction level of users and appropriately reflect their experience. In the system, we implement the rate prediction function using collaborative filtering and the game recommendation function using Naive Bayesian classifier to provide users with quick and accurate recommendations. As the result, the rate prediction algorithm achieved a throughput of 2.4 seconds and an average of 72.1 percent accuracy. For the game recommendation algorithm, we obtained 75.187 percent accuracy and were able to provide users with fast and accurate recommendations.

• Journal of The Korean Association of Information Education
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• 제24권6호
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• pp.573-584
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• 2020

This study is about the development of AI algorithm education program using cognition modeling to positively improve students' image on AI. First, we analyzed the concept of user-based collaborative filtering and developed the education program using the cognition modeling method. We checked the adequacy of program through the expert validity test. Both CVR values for the content development method of cognitive modeling and the developed program showed validity above .80. We applied the developed program to elementary school students in class. The test was conducted using a semantic discrimination to examine changes in students' perception of artificial intelligence before and after. We were able to confirm that the students' AI images were significant positive change in 12 of the 23 words in the adjective pair.

• The Journal of the Convergence on Culture Technology
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• 제8권5호
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• pp.663-668
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• 2022

As the number of people who experience sleep disorders is increasily growing, users' desire to improve their sleep quality has also increased. Acoordingly, the 'Sleeptech' market is showing a steady growth. This study designs and proposes a system after consideration of existing related research that can help modern people overcome sleep disorders, which is based on the necessity for customized sleep hygien service. This system analyzes user's sleep data collected through smartphone built-in sensors to calculate sleep patterns, provides customized sleep hygiene-based solutions to users through collaborative filtering, and provides an environment suitable for sleep through the automatic control of IoT devices. This method of using sleep data is expected to contribute to the improvement of the quality of life of modern people suffering from sleep disorders, which results from expansion to Sleeptech market as well as improvement of users' sleep habits.

• Journal of Broadcast Engineering
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• 제17권1호
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• pp.129-139
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• 2012

This paper introduces a recommender system that is to recommend broadcast content. Our recommender system uses user's viewing history for personalized recommendations. Broadcast contents has unique characteristics as compared with books, musics and movies. There are two types of broadcast content, a series program and an episode program. The series program is comprised of several programs that deal with the same topic or story. Meanwhile, the episode program covers a variety of topics. Each program of those has different topic in general. Therefore, our recommender system recommends TV programs to users according to the type of broadcast content. The recommendations in this system are based on user's viewing history that is used to calculate content similarity between contents. Content similarity is calculated by exploiting collaborative filtering algorithm. Our recommender system uses java sparse array structure and performs memory-based processing. And then the results of processing are stored as an index structure. Our recommender system provides recommendation items through OPEN APIs that utilize the HTTP Protocol. Finally, this paper introduces the implementation of our recommender system and our web demo.

• Information Systems Review
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• 제19권2호
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• pp.71-94
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• 2017

Recommender systems reduce information overload and enhance choice quality. This technology is used in many services and industry. Previous studies did not consider recommendation quantity and the repetitive recommendations of an item. This study is the first to examine recommender systems by considering recommendation quantity and repetitive recommendations. Only a limited number of items are displayed in offline stores because of their physical limitations. Determining the type and number of items that will be displayed is an important consideration. In this study, I suggest the use of a user-based recommender system that can recommend the most appropriate items for each store. This model is evaluated by MAE, Precision, Recall, and F1 measure, and shows higher performance than the baseline model. I also suggest a new performance evaluation measure that includes Quantity Precision, Quantity Recall, and Quantity F1 measure. This measure considers the penalty for short or excess recommendation quantity. Novelty is defined as the proportion of items in a recommendation list that consumers may not experience. I evaluate the new revenue creation effect of the suggested model using this novelty measure. Previous research focused on recommendations for customer online, but I expand the recommender system to cover stores offline.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.273-276
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• 2019

There is a lot of information in our world, quick access to the most accurate information or finding the information we need is more difficult and complicated. The recommendation system has become important for users to quickly find the product according to user's preference. A social recommendation system using community detection based on label propagation is proposed. In this paper, we applied community detection based on label propagation and collaborative filtering in the movie recommendation system. We implement with MovieLens dataset, the users will be clustering to the community by using label propagation algorithm, Our proposed algorithm will be recommended movie with finding the most similar community to the new user according to the personal propensity of users. Mean Absolute Error (MAE) is used to shown efficient of our proposed method.

• Journal of the Korea Computer Industry Society
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• 제2권11호
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• pp.1365-1372
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• 2001

We have suggested three learning rules and implemented an experimental system that automatically restructures hypertext networks according to their user browsing behavior. The system applies link weights to the hypertext networks and updates these weights according to the learning rules. The learning rules are based on how often a particular hyperlink is being traversed and operate on strictly local information of link traversals. Changes in network structure are fed back to users by dynamic link ordering according to descending link weight. This approach is used collaborative filtering and navigation support techniques. The system has been investigation to be able to structure random hypertext networks into valid representations of their user browsing preferences in experiments.

• Journal of Intelligence and Information Systems
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• 제20권4호
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.