• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.49-50
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• 2006

Digital satellite systems are usually integrated with terrestrial systems to provide various services, and in these cases they should satisfy the performance objectives defined by the terrestrial systems. Recommendation ITU-R S.1062 specifies the performance of digital satellite systems. The performance objectives were given in terms of bit error probability divided by the average number of errors per burst versus percentage of time. This paper presents theoretical method to estimate performance measure of digital satellite systems defined in Recommendation ITU-R S.1062. We show performance estimation results of duo-binary Turbo codes, and verify them by comparing to the simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1272-1290
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• 2013

Privacy-preserving collaborative filtering schemes are becoming increasingly popular because they handle the information overload problem without jeopardizing privacy. However, they may be susceptible to shilling or profile injection attacks, similar to traditional recommender systems without privacy measures. Although researchers have proposed various privacy-preserving recommendation frameworks, it has not been shown that such schemes are resistant to profile injection attacks. In this study, we investigate two memory-based privacy-preserving collaborative filtering algorithms and analyze their robustness against several shilling attack strategies. We first design and apply formerly proposed shilling attack techniques to privately collected databases. We analyze their effectiveness in manipulating predicted recommendations by experimenting on real data-based benchmark data sets. We show that it is still possible to manipulate the predictions significantly on databases consisting of masked preferences even though a few of the attack strategies are not effective in a privacy-preserving environment.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.510-514
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• 2004

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is known to be the most successful recommendation technology, but its widespread use has exposed some problems such as sparsity and scalability in the e-business environment. In this paper, we propose a recommendation methodology based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of original CF-based recommender systems. Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, so leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. Several experiments on real e-commerce data show that the proposed methodology provides higher quality recommendations and better performance than original collaborative filtering methodology.

• Asia pacific journal of information systems
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• v.12 no.4
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• pp.121-137
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• 2002

Researches on P2P, the information sharing model from person to person, up to date have focused on the technical side and there have been lacking of the business side researches such as customer loyalty. Considering the problem, this study tries to examine empirically in what way the factors of service, market, and customer affect the service loyalty of P2P sites. Results of the study show that the three factors have statistically significant effects on the service loyalty in general. In particular, the results uncover that those factors have different impacts on the reuse intention and the recommendation intention, which are the two measures of service loyalty. For example, the cost of service use affects the reuse intention significantly whereas the same element does not affect the recommendation intention. Interestingly, some of the results are not coincide with the results of previous studies and do not meet general expectation. For example, users' tendency of pursuing variety has positive impacts on the service loyalty, especially, the intention of recommendation.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.71-85
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• 2006

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.658-663
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• 1994

The classification and coding of part for group technology applications continus to be labour intensive and time-consuming process, and therefore much effort is dedicated to the structure and creation of automatic coding systems. IN this paper, Neural networks is used to generate processes-related digit as well as part geometry-related digit of the TS code where part name is provided as input.since part name, which is appropriately designated, provides much information about part geometry and manufacturing processes. THe developed GT recommendation system is integrated with interactive TS coding system and database in order to handle the changes of production environment, such as the change of production part of plant. It is found to recommend codes accurately and promises to be a useful tool for consistent, reliable and convenient coding processes.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.352-368
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• 2021

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.

• Database Research
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• v.34 no.3
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• pp.99-123
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• 2018

Due to the ever expanding nature of the Web, separating more valuable information from the noisy data is getting more important. Although recommendation systems are widely used for addressing the information overloading issue, their performance does not seem meaningfully improved in currently suggested approaches. Hence, to investigate the issues, this study discusses different characteristics of popular, existing recommendation approaches, and proposes a new profiling technique that uses collaborative tags and test whether it successfully compensates the limitations of the existing approaches. In addition, the study also empirically evaluates rating/tagging patterns of users in various recommendation approaches, which include the proposed approach, to learn whether those patterns can be used as effective cues for improving the recommendations accuracy. Through the sensitivity analyses, this study also suggests the potential associated with a single recommendation system that applies multiple approaches for different users or items depending upon the types and contexts of recommendations.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.13-21
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• 2019

The tendency of buyers of web information is changing from the cost-effectiveness which emphasizes the performance over the price to the cost-satisfaction which emphasizes the psychological satisfaction of the buyer. In music recommendation, one of the methods to increase psychological satisfaction is to use the music mood. In this paper, a music recommendation method considering the mood tag and the synonyms tag is proposed and, as an intermediate result of the proposed method, mood tags and music pieces are expressed in Thayer's AV space and then their distribution are analyzed. The analysis result shows the distributions of mood tags and the ones of music pieces are similar, which implies that the proposed recommendation method can provide significant results. In the future, the music recommendation performance will be analyzed.