• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.45-52
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• 2015

Although researchers have proposed various recommendation systems, most recommendation approaches are for single users and there are only a small number of recommendation approaches for groups. However, TV programs or movies are most often viewed by groups rather than by single users. Most recommendation approaches for groups assume that single users' profiles are known and that group profiles consist of the single users' profiles. However, because it is difficult to obtain group profiles, researchers have only used synthetic or limited datasets. In this paper, we report on various group recommendation approaches to a real large-scale dataset in a TV domain, and evaluate the various group recommendation approaches. In addition, we provide some guidelines for group recommendation systems, focusing on home group users in a TV domain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2809-2821
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• 2023

Effective recommendation of similar business groups is a critical factor in obtaining market information for companies. In this study, we propose a novel method for enhancing similar business group recommendation by incorporating derivative criteria and web crawling. We use employment announcements, employment incentives, and corporate vocational training information to derive additional criteria for similar business group selection. Web crawling is employed to collect data related to the derived criteria from 'credit jobs' and 'worknet' sites. We compare the efficiency of different datasets and machine learning methods, including XGBoost, LGBM, Adaboost, Linear Regression, K-NN, and SVM. The proposed model extracts derivatives that reflect the financial and scale characteristics of the company, which are then incorporated into a new set of recommendation criteria. Similar business groups are selected using a Euclidean distance-based model. Our experimental results show that the proposed method improves the accuracy of similar business group recommendation. Overall, this study demonstrates the potential of incorporating derivative criteria and web crawling to enhance similar business group recommendation and obtain market information more efficiently.

• Journal of Information Technology Services
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• v.6 no.2
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• pp.113-123
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• 2007

In ubiquitous computing environment such as ubiquitous marketplace (u-market), there is a need of providing context-based personalization service while considering the nomadic user preference and corresponding requirements. To do so, the recommendation systems should deal with the tremendous amount of context data. Hence, the purpose of this paper is to propose a novel recommendation method which provides the products-group list of the customers in u-market based on the shopping intention and preferences. We have developed FREPIRS(FREquent Purchased Item-sets Recommendation Service), which makes recommendation listof product-group, not individual product. Collaborative filtering and apriori algorithm are adopted in FREPIRS to build product-group.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.192-197
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• 2001

A recommendation system tracks past actions of a group of users to make a recommendation to individual members of the group. The computer-mediated marketing and commerce have grown rapidly nowadays so the concerns about various recommendation procedures are increasing. We introduce a recommendation methodology by which e-commerce sites suggest new products of services to their customers. The suggested methodology is based on web log analysis, product taxonomy, and association rule mining. A product recommendation system is developed based on our suggested methodology and applied to a Korean internet shopping mall. The validity of our recommendation system is discussed with the analysis of a real internet shopping mall case.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.344-353
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• 2006

Nowadays many people participate in online communities for information sharing. But most recommender systems are designed for personalization of individual user, so it is necessary to develop a recommendation procedure for group users, such as participants in online communities. This paper proposes a group recommender system to recommend books for group users in online communities. For such a purpose, we suggest a group recommendation procedure consisting of two phases. The first phase is to generate recommendation list for 'big user' using collaborative filtering, and the second phase is to remove irrelevant books among previous list reflecting the preference of each individual user. The procedure is explained step by step with an illustrative example. And this procedure can potentially be applied to other domains, such as music, movies and etc.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.11-20
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• 2013

Recently, as SNS services have been increased, studies on recommendation schemes have been actively done. Recommendation scheme provides various favorable or needed services with users on real time. Group recommendation provides users with suitable groups based on their preference. In this paper, we propose a new group recommendation scheme considering user profiles and collaborative filtering in social networks. The proposed scheme can solve the problems of the static profile based group recommendation scheme because it collects the recent group activities and updates user profiles. It also recommends the more various groups by reflecting the similar tendencies of other users within a group through collaborative filtering. Our experimental results show that the proposed scheme recommends various groups that significantly considers the user's changing preferences compared to the existing scheme.

• Journal of KIISE
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• v.43 no.10
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• pp.1144-1153
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• 2016

Group recommendation analyzes the characteristics and tendency of a group rather than an individual and provides relevant information for the members of the group. Existing group recommendation methods merely consider the average and frequency of a preference. However, if the users' preferences have large deviations, it is difficult to provide satisfactory results for all users in the group, although the average and frequency values are high. To solve these problems, we propose a method that considers not only the average of a preference but also the deviation. The proposed method provides recommendations with high average values and low deviations for the preference, so it reflects the tendency of all group members better than existing group recommendation methods. Through a comparative experiment, we prove that the proposed method has better performance than existing methods, and verify that it has high performance in groups with a large number of members as well as in small groups.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.312-319
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• 2002

A recommendation system tracks past action of a group of users to make a recommendation to individual members of the group. The computer-mediated marking and commerce have grown rapidly nowadays so the concerns about various recommendation procedure are increasing. We introduce a recommendation methodology by which Korean department store suggests products and services to their customers. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is to select target customers, who have high purchase possibility of recommended products. Product taxonomy and association rule mining are used to select proper products. The validity of our recommendation methodology is discussed with the analysis of a real Korean department store.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.803-811
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• 2010

This study analyzes the characteristics of preference ratings by dividing estimated values into four groups according to rank correlation coefficient after obtaining preference estimated value to user's ratings by using collaborative filtering algorithm. It is known that the value of standard error of skewness and standard error of kurtosis lower in the group of higher rank correlation coefficient This explains that the preference of higher rank correlation coefficient has lower extreme values and the differences of preference rating values. In addition, top n recommendation lists are made after obtaining rank fitting by using the result ranks of prediction value and the ranks of real rated values, and this top n is applied to the four groups. The value of top n recommendation is calculated higher in the group of higher rank correlation coefficient, and the recommendation accuracy in the group of higher rank correlation coefficient is higher than that in the group of lower rank correlation coefficient Thus, when using standard error of skewness and standard error of kurtosis in recommender system, rank correlation coefficient can be higher, and so the accuracy of recommendation prediction can be increased.

• Journal of Animal Science and Technology
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• v.53 no.6
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• pp.517-523
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• 2011

The experiment was conducted with 144 broiler chicks from day-old to 5 weeks of age to investigate the efficacy of a water additive in broiler production. The chicks were randomly distributed into four different treatments namely T1 (control), T2 (water additive as per recommendation level), T3 (25% less than recommendation) and T4 (25% more than recommendation). Body weight of control group was higher in 2nd week of age, but at the end of the experiment additive groups showed higher values compare to control (p<0.05). Body weight gain was increased and feed conversion ratio was improved in the additives groups during the finishing and total period, although feed intake was different among the additive groups (p<0.05). When the hematological parameters were evaluated, packed cell volume and total erythrocytes counts were increased in the additive group that received 25% more than recommendation, and hemoglobin in 25% less than recommendation group. Mean cell volume and mean cell hemoglobin of the additive groups showed lower (p<0.05) values compare to the control, but other parameters were not affected. Sales price and profit were significantly higher in the additive groups compare to the control, although total production cost was increased in the additive groups (p<0.05). All levels of water additive increased profit in comparison with the control but 25% less than recommendation level appeared to be most profitable and cost effective. It also suggests that any additive considered for poultry, must undergo trial for determining efficacy as well as its cost effectiveness for application.