• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.148-157
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• 2021

The effectiveness of recommendation systems depends on the performance of the algorithms with which these systems are designed. The quality of the algorithms themselves depends on the quality of the strategies with which they were designed. These strategies differ from author to author. Thus, designing a good recommendation system means implementing the good strategies. It's in this context that several research works have been proposed on various strategies applied to algorithms to meet the needs of recommendations. Researchers are trying indefinitely to address this objective of seeking the qualities of recommendation algorithms. In this paper, we propose a new algorithm for recommending learning items. Learner performance predictions and collaborative recommendation methods are used as strategies for this algorithm. The proposed performance prediction model is based on convolutional neural networks (CNN). The results of the performance predictions are used by the proposed recommendation algorithm. The results of the predictions obtained show the efficiency of Deep Learning compared to the k-nearest neighbor (k-NN) algorithm. The proposed recommendation algorithm improves the recommendations of the learners' learning items. This algorithm also has the particularity of dissuading learning items in the learner's profile that are deemed inadequate for his or her training.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2003년도 춘계학술대회
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• pp.400-409
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• 2003

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권11호
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• pp.5344-5356
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• 2018

Nowadays with the help of Location-Based Social Networks (LBSNs), users of Point-of-Interest (POI) recommendation service in LBSNs are able to publish their geo-tagged information and physical locations in the form of sign-ups and share their experiences with friends on POI, which can help users to explore new areas and discover new points-of-interest, and promote advertisers to push mobile ads to target users. POI recommendation service in LBSNs is attracting more and more attention from all over the world. Due to the sparsity of users' activity history data set and the aggregation characteristics of sign-in area, conventional recommendation algorithms usually suffer from low accuracy. To address this problem, this paper proposes a new recommendation algorithm based on a novel Preference-Content-Region Model (PCRM). In this new algorithm, three kinds of information, that is, user's preferences, content of the Point-of-Interest and region of the user's activity are considered, helping users obtain ideal recommendation service everywhere. We demonstrate that our algorithm is more effective than existing algorithms through extensive experiments based on an open Eventbrite data set.

• 컴퓨터교육학회논문지
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• 제11권1호
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• pp.11-21
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• 2008

지식정보화시대가 시작되면서 멘토링은 인재 발굴 및 관리를 위한 효율적인 방법으로 인식되고 있다. 멘토링 효과를 높이기 위한 요소는 여러 가지이다. 그 중 멘토와 멘티를 매칭하는 요소는 멘토링 시스템에서 핵심이라 할 수 있다. 기존 e-멘토링 시스템의 매칭은 대부분 개인 정보를 충분히 활용하지 못하고 관리자에 의해 일괄적으로 처리되는데, 이는 멘토링 효과에 부정적인 영향을 미칠 수 있다. 본 논문에서는 중 고등학생을 대상으로 개인선호도를 입력받아 매칭 항목으로 결정하고 이를 기반으로 가장 적절한 멘토/멘티를 매칭시키는 개인선호도기반의 멘토/멘티 추천 알고리즘을 제안한다. 또한, 본 논문에서는 기존의 알고리즘과 함께 제안한 알고리즘을 정교성, 일치성, 다양성 측면에서 분석하여 제안한 알고리즘의 효율성을 증명한다.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.221-232
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• 2023

In this paper, we propose a recommendation system based on the latent factor model using matrix factorization, which is one of the most commonly used collaborative filtering algorithms for recommendation systems. In particular, by introducing the concept of creating a list of recommended content and a list of non-preferred recommended content, and removing the non-preferred recommended content from the list of recommended content, we propose a method to ultimately increase the satisfaction. The experiment confirmed that using a separate list of non-preferred content to find non-preferred content increased precision by 135%, accuracy by 149%, and F1 score by 72% compared to using the existing recommendation list. In addition, assuming that users do not view non-preferred content through the proposed algorithm, the average evaluation score of a specific user used in the experiment increased by about 35%, from 2.55 to 3.44, thereby increasing user satisfaction. It has been confirmed that this algorithm is more effective than the algorithms used in existing recommendation systems.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.109-134
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• 2018

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.

• 경영정보학연구
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• 제24권3호
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• pp.69-91
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• 2022

짧은 동영상의 대표서비스인 틱톡의 사용자를 대상으로 프라이버시 계산 모델을 이용하여 틱톡의 추천서비스 유형(추천서비스 정도에 따라서 3단계로 구분함)에 대한 사용자의 반응(인지된 위험, 인지된 혜택, 지속적 사용의도)과 인지된 위험과 인지된 혜택이 지속적 사용의도에 미치는 영향을 검증하였다. 뿐만 아니라 지속적 사용의도에 영향을 조절하는 호기심의 역할이 있는지를 검증하였다. 연구 결과, 인지된 혜택(인지된 정보성)요인과 지속적 사용의도는 추천서비스 유형중에서도 고, 저, 중 추천 서비스정도 순으로 높게 나타났고, 인지된 혜택(인지된 오락성)과 인지된 위험(프라이버시 심각성, 프라이버시 침해 가능성)은 고, 중, 저의 추천 서비스정도 순으로 높게 나타났다. 인지된 혜택(인지된 정보성, 인지된 오락성)은 지속적 사용의도에 긍정적인 영향을 주었으나, 인지된 위험(프라이버시 심각성, 프라이버시 침해 가능성)은 지속적 사용의도에 부정적인 영향을 주는 것으로 확인되었다. 마지막으로 프라이버시 계산모델에서 호기심은 조절효과가 있다는 것을 확인하였다. 사용자들은 추천서비스의 프라이버시에 대한 우려와 서비스에 대한 혜택 모두를 인지하고 있으며, 추천서비스에 대해서 위험과 혜택이 모두 있지만 지속적으로 서비스를 이용할 것으로 나타났다. 더 많은 연구를 통해서 추천시스템의 긍정적인 효과와 부정적인 반응을 비교하여 사용자들의 프라이버시 허용정도에 대해서 좀 더 알게 되었을 때 추천서비스를 염려없이 사용할 수 있을 것으로 사료된다.