Earticle

본 연구는 초등학생들의 인공지능에 대한 이미지를 긍정적으로 향상시키고자 하는 인지 모델링기반 인공지능 알고리즘 교육 프로그램의 개발에 관한 것이다. 먼저 인공지능 알고리즘 중 협력필터링의 개념을 분석하고 이를 인지모델링 방법을 활용하여 교육 프로그램을 개발하였다. 이후 전문가 타당도 검사를 통해 인지 모델링기반의 콘텐츠 개발 방법과 개발된 프로그램에 대한 적절성이 CVR .80 이상으로 타당함을 확인하였다. 개발 프로그램 은 초등학교 6학년 학생들에게 수업으로 적용하였고 형용사 단어 23쌍을 이용한 의미분별법을 이용하여 사전- 사후에 인공지능에 대한 학생들의 이미지 인식의 변화를 살펴보았다. 학생들의 인공지능에 대한 이미지는 총 23 개 단어 쌍 중 12개에서 유의미한 긍정적 변화를 확인할 수 있었다.

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.

Collaborative filtering algorithms often encounter data sparsity issues. To overcome this issue, auxiliary information of relevant items is analyzed and an item attribute matrix is derived. In this study, we combine the user-item attribute preference with the traditional similarity calculation method to develop an improved similarity calculation approach and use weights to control the importance of these two elements. A collaborative filtering algorithm based on user-item attribute preference is proposed. The experimental results show that the performance of the recommender system is the most optimal when the weight of traditional similarity is equal to that of user-item attribute preference similarity. Although the rating-matrix is sparse, better recommendation results can be obtained by adding a suitable proportion of user-item attribute preference similarity. Moreover, the mean absolute error of the proposed approach is less than that of two traditional collaborative filtering algorithms.

본 연구의 목적은 좀 더 정확한 고객 선호도 예측을 위한 협업 필터링 알고리즘의 예측 성능을 평가하기 위한 것이다. 고객 선호도 예측의 정확도를 비교하기 위하여 이웃 기반의 협업 필터링 알고리즘과 대응평균 알고리즘에 의한 고객 선호도 예측의 MAE를 비교하였다. 예측 알고리즘의 정확성을 분석하기 위하여 MovieLens 1 Million dataset을 이용하여 실험을 하였다. 각 예측 알고리즘에 사용된 유사도 가중치는 일반적으로 이용되는 피어슨 상관계수와 벡터 유사도를 이용하였으며 분석결과 대응평균 알고리즘의 예측 정확도가 이웃 기반의 협업 필터링 알고리즘의 예측 정확도 보다 우수한 것으로 나타났다. 두 알고리즘에 사용된 유사도 가중치인 피어슨 상관계수와 벡터 유사도는 두 고객이 특정 상품에 대하여 공통으로 평가한 선호도 평가치를 이용하여 계산된다. 이때 공통으로 평가한 선호도 평가치의 개수가 적으면 계산된 유사도 가중치가 과대 평가된다. 과대 평가된 유사도 가중치를 보정하여 고객 선호도 예측의 정확도를 높이기 위하여 기존의 연구에서 고려한 공통 평가 영화의 개수 보다 확대된 범위를 적용하였으며 각 예측 방법에 따라 서로 다른 개선 경향을 파악할 수 있었다.

The purpose of this study is to evaluate the performance of collaborative filtering recommender algorithms for better prediction accuracy of the customer's preference. The accuracy of customer's preference prediction is compared through the MAE of neighborhood based collaborative filtering algorithm and correspondence mean algorithm. It is analyzed by using MovieLens 1 Million dataset in order to experiment with the prediction accuracy of the algorithms. For similarity, weight used in both algorithms, commonly, Pearson's correlation coefficient and vector similarity which are used generally were utilized, and as a result of analysis, we show that the accuracy of the customer's preference prediction of correspondence mean algorithm is superior. Pearson's correlation coefficient and vector similarity used in two algorithms are calculated using the preference rating of two customers' co-rated movies, and it shows that similarity weight is overestimated, where the number of co-rated movies is small. Therefore, it is intended to increase the accuracy of customer's preference prediction through expanding the number of the existing co-rated movies.

코로나19로 인하여 배달 빈도수가 높은 힘든 1인가구나 맞벌이가족에게는 식자재의 낭비 및 유통기한 관리의 문제점을 가지고 있다. 이런 문제점을 해결하기 위해 본 논문에서는 사용자가 식재료의 유통기한 을 관리하고 식재료 기반 또는 사용자 행동 양식 기반으로 레시피를 추천 받을 수 있게 해주는 애플리케 이션을 설계한다. 본 애플리케이션은 객체 인식 알고리즘인 YOLO를 이용하여 카메라로 식재료를 인식 하고, 협업 필터링을 이용한 레시피 추천 시스템으로 사용자가 원하는 레시피 정보를 제공한다. 사용자 는 본 애플리케이션을 사용하여 카메라로 식재료를 인식하여 레시피를 검색할 수 있고 사용자의 과거이 력이나 취향에 연관된 레시피를 추천 받을 수 있다.

The user-based collaborative filtering algorithm has been widely used in various kinds of personalized recommendation systems. But it has a serious shortcoming: with the increasing number of the users and commodities, its calculation work grows rapidly. To address the problem of vast time consumption by big dataset, we utilize MapReduce programming idea to do parallelized transformation of the algorithm; finally deploy it to be run in Hadoop cloud computing platform. Experiments have revealed that if computing data is reasonably distributed and the data volume is big, then the algorithm performance of the algorithm can realize favorable linearly speeding effect.

With the development of information technology, technology mining technology can help users to find the needed information accurately and efficiently. In this paper, the author makes factors analysis of professional growth of innovative talents based on data mining technology. Knowledge innovation is the starting point of scientific and technological innovation, and the development of innovative talents is the most important and the scarcest resource for enterprises. By analyzing the professional growth of innovative talents, we construct the evaluation index system of innovative talents. The conclusion proves that the balance between supply and demand of enterprise and personal professional growth is the important factor to promote organizational technology progress and the career development of employees.

In order to solve this problem of cloud model, this paper presents another new collaborative filtering recommendation algorithm by combining the item classification and cloud model. Firstly the algorithm utilizes the item classification information and cloud model to compute items inner-similarity, and then gets the scores from neighbor items which have the highest similarity and uses their scores to forecast the unrated inner-class items. Secondly, the neighbors of user are obtained by computing the inner-class user similarities in the cloud model, providing the final forecast grade and carrying out the recommendation.

With analyzing usual collaborative filtering algorithm , a modified collaborative filtering algorithm which fuses with fashion attributes is researched .Firstly , using fuzzy mathematics processes fashion attributes, which include style , color , material , quality , brand , and seasonality , to produce fashion attributes function . Secondly, using fashion attributes function adjusts users appraising matrix parameters of collaborative filtering algorithm to improve fashion recommendation system performance. Lastly, to use experiment proves that performance of modified collaborative filtering algorithm is better than performance of usual filtering algorithm performance in personalized fashion recommendation system.

Collaborative filtering algorithm is the most used items recommendation algorithm. We find the k neighbors with the highest similarity by calculating user similarity and recommend items for users by the score of the neighbors of the items. In the paper, we propose a hybrid recommendation algorithm based on user similarity and attribute weights to solve user ratings sparsity. We obtained the weights of users like properties through learning user ratings records and combined with the user similarity for users to recommend item. Finally, we transplant the algorithm to HADOOP platform. Through the experiment, the improved collaborative filtering algorithm is better than the original algorithm in precision and parallel attribute.

Memory-based CF algorithms have the weakness of low real-time ability and scalability. For these issues, a SVD-based K-means clustering CF algorithm is proposed. Traditional clustering-based CF algorithms have low recommendation precision because of data sparsity. So we first fill the missing ratings by SVD prediction, and then implement k-means clustering in the filled matix. This algorithm overcomse the data sparsity issue via SVD and keep the advantage of clustering, such as good real-time ability and scalability. Experiments results show that this algorithm outperforms Pearson CF, svd CF and k-means CF.

In computer aided diagnosis (CAD) process, one of the most challenging problems is data sparsity, which leads to the diagnosis results are not reliable. This paper proposes a clustering collaborative filtering based algorithm to solve the problem of data sparsity. In this paper, we use k-means clustering algorithm to cluster the same type of patients, and then adopt collaborative filtering method to fill the missing data values for each cluster, in this way to reduce the complexity of similarity calculation of collaborative filtering. The proposed method makes full use of the information-sharing mechanism of "similar patient population" to predict and fill the missing values. A hepatitis dataset is used for evaluating the performance of the algorithm. Results indicate that the proposed algorithm has better performance for medical record data sparsity problem.

Most existing calculations of similarities suffer from data sparsity and poor prediction quality problems. For this issue, we proposed a similarity measurement algorithm based on entropy. The entropy is computed by the difference of two users’ ratings, and we also consider the size of their common rated items, the size is bigger, the weight of their similarity is higher. Experiments show that the algorithm effectively solves the problem of the inaccuracy of similarities in data sparsity or small size neighborhood environments, and outperforms other state-of-the-art CF algorithms and it is more robust against data sparsity.

Traditional collaborative filtering methods just use user-item rating matrix to generate recommendations, and lead to difficult to computer the similarity because of the data sparsity. We propose a hybrid collaborative filtering algorithm combining the rating matrix and item attributes. First, we design a user similarity measurement method by computing the user’s preference to different item attributes, this approach is consistent with the true relationship between users, and also can effectively alleviate the issue of rating matrix sparse. Then, when computing the similarity of two users, we combine the Pearson correlation and the items attribute preference similarity, with a weighting coefficient “w” to balance the importance of two parts. Experiments show that this algorithm effectively solves the problem of data sparsity, and outperforms better when the sparsity is more serious, compared to the traditional CF algorithms.

Aiming at the problems that the existing model-based collaborative filtering algorithm has low recommendation accuracy and small recommendation coverage, we propose a collaborative filtering recommendation algorithm based on the trust propagation by introducing the trust information of social network to extend the matrix factorization-based recommendation model. We first design a set of trust propagation rules based on the direct trust relationships of the social network, so as to propagate the trust relationship in the social networks, and get to quantize the new trust relationship. Then we load the quantitative trust relations after the trust propagation as the trust weight into the matrix factorization-based model according to the characteristics that the matrix factorization technique can reduce the dimension of large-scale datasets.

To overcome the problem of too much sparse by scoring matrix, the paper proposes from the part of user interests the collaborative filtering recommendation algorithm based on such interest. In the e-commerce websites, recommendation items have various appearance, function and category attributes. Items which hold similar characteristics generally gain approximate scoring values. The aforesaid method extracts useful information for improvement from user interests. Users’ scorings about different items imply their modes of interest. Interest association exists amongst items that were evaluated by the same user. Since individual interest shifts, the intensity of such correlation will gradually change along with days. By building interest intensity model with time decay, and discovering interest correlation among different items through that model, the proposed algorithm can predict scoring matrix and fill it, which is helpful to alleviate problems with sparse caused by user-item scoring matrix.

Traditional methods UBCF have limitations of poor recommendation quality and problems of data sparsity. To alleviate these problems, a novel collaborative filtering algorithm is designed, which firstly get the users’ ratings and time intervals for each attribute from the users’ ratings for items, then produce two methods to calculate the similarity between users, introduce a weighting parameters to control the weight between the two similarity methods in order to get a fusion similarity between two users. The results show that this method is able to improve the accuracy of predicted values, resulting in improving recommendation quality of the collaborative filtering recommendation algorithm.

The paper Proposed Item parallel collaborative filtering recommendation algorithm (IP-CF). Through designing efficient parallel algorithm, compute-extensive procedures are distributed to different processing nodes in Hadoop platform. Taking advantage of parallel computing, we accelerate the response of recommendation. The experimental results show that our proposed algorithm IP-CF is more efficient and scalable than current parallel algorithms.

An improved CF algorithm based on the classification of items is introduced to overcome the problems caused by the data sparseness and inaccuracy of the user neighbors. The new algorithm first rates the unrated items by applying the item classification, and then calculates the user similarity within classes for nearest-neighbors, after which it could recommend the items based on the final prediction.