• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.101-106
• /
• 2023

In this busy world actually stress is continuously grow up in research and monitoring social websites. The social interaction is a process by which people act and react in relation with each other like play, fight, dance we can find social interactions. In this we find social structure means maintain the relationships among peoples and group of peoples. Its a limit and depends on its behavior. Because relationships established on expectations of every one involve depending on social network. There is lot of difference between emotional pain and physical pain. When you feel stress on physical body we all feel with tensions, stress on physical consequences, physical effects on our health. When we work on social network websites, developments or any research related information retrieving etc. our brain is going into stress. Actually by social network interactions like watching movies, online shopping, online marketing, online business here we observe sentiment analysis of movie reviews and feedback of customers either positive/negative. In movies there we can observe peoples reaction with each other it depends on actions in film like fights, dances, dialogues, content. Here we can analysis of stress on brain different actions of movie reviews. All these movie review analysis and stress on brain can calculated by machine learning techniques. Actually in target oriented business, the persons who are working in marketing always their brain in stress condition their emotional conditions are different at different times. In this paper how does brain deal with stress management. In software industries when developers are work at home, connected with clients in online work they gone under stress. And their emotional levels and stress levels always changes regarding work communication. In this paper we represent emotional intelligence with stress based analysis using machine learning techniques in social networks. It is ability of the person to be aware on your own emotions or feeling as well as feelings or emotions of the others use this awareness to manage self and your relationships. social interactions is not only about you its about every one can interacting and their expectations too. It about maintaining performance. Performance is sociological understanding how people can interact and a key to know analysis of social interactions. It is always to maintain successful interactions and inline expectations. That is to satisfy the audience. So people careful to control all of these and maintain impression management.

• Journal of Information Technology and Architecture
• /
• v.9 no.3
• /
• pp.333-344
• /
• 2012

This paper proposes a system which recommends movies using information from social network services containing personal interest and taste. Method for establishing data is as follows. The system gathers movies' information from web sites and user's information from social network services such as Facebook and twitter. The data from social network services is categorized into six steps of emotion level for more accurate processing following users' emotional states. Gathered data will be established into vector space model which is ideal for analyzing and deducing the information with the system which is suggested in this paper. The existing similarity measurement method for movie recommendation is presentation of vector information about emotion level and similarity measuring method on the coordinates using Cosine measure. The deducing method suggested in this paper is two-phase arithmetic operation as follows. First, using general cosine measurement, the system establishes movies list. Second, using similarity measurement, system decides recommendable movie list by vector operation from the coordinates. After Comparative Experimental Study on the previous recommendation systems and new one, it turned out the new system from this study is more helpful than existing systems.

• Journal of Information Processing Systems
• /
• v.15 no.5
• /
• pp.1141-1155
• /
• 2019

Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities' detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.

• Annual Conference of KIPS
• /
• 2022.11a
• /
• pp.34-36
• /
• 2022

Over the last decades, information has increased exponentially due to SNS(Social Network Service), IoT devices, World Wide Web, and many others. Therefore, it was monumentally hard to offer a good service or set of recommendations to consumers. To surmount this obstacle numerous research has been conducted in the Data Mining field. Different and new recommendation models have emerged. In this paper, we proposed a Content-based movie recommendation system using demographic information of users and the average rating for genres. We used MovieLens Dataset to proceed with our experiment.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.4
• /
• pp.593-601
• /
• 2017

In this paper, we constructed the model of production/investment, distribution, and screening by using variables that can be considered at each stage according to the value chain stage of the movie industry. To increase the predictive power of the model, a regression analysis was used to derive meaningful variables. Based on the given variables, we compared the difference in predictive power between the artificial neural network, which is a machine learning analysis method, and the decision tree analysis method. As a result, the accuracy of artificial neural network was higher than that of decision trees when all variables were added in production/ investment model and distribution model. However, decision trees were more accurate when selected variables were applied according to regression analysis results. In the screening model, the accuracy of the artificial neural network was higher than the accuracy of the decision tree regardless of whether the regression analysis result was reflected or not. This paper has an implication which we tried to improve the performance of movie prediction model by using machine learning analysis. In addition, we tried to overcome a limitation of linear approach by reflecting the results of regression analysis to ANN and decision tree model.

• Annual Conference of KIPS
• /
• 2023.11a
• /
• pp.522-523
• /
• 2023

CTR(Click Through Rate) 예측은 사용자가 광고나 아이템을 클릭할 확률을 예측하는 데 사용되는 용어로, 광고 분야에서 중요한 연구 분야로 자리 잡았다. 인터넷 데이터의 양이 증가함에 따라, 전통적인 피쳐 엔지니어링의 인건비는 계속해서 상승하고 있다. 특징 상호 작용에 대한 의존도를 줄이기 위해, 본 논문은 TMH(Two-Tower Multi-Headed Attention Neural Network) 접근법이라고 하는 명시적인 특징 상호 작용과 암시적인 특징 상호 작용을 결합한 융합 모델을 제안한다. CTR 예측에서 TMH 의 효과를 평가하기 위해 3 개의 실제 데이터 세트를 사용하여 많은 수의 실험을 수행하였다. 성능은 3 개의 데이터 세트에서 0.12%, 0.41% 및 0.68%으로 향상되었다.

• Annual Conference of KIPS
• /
• 2023.05a
• /
• pp.389-391
• /
• 2023

In the era of Big Data, humanity is facing a huge overflow of information. To overcome such an obstacle, many new cutting-edge technologies are being introduced. The movie recommendation system is also one such technology. To date, many theoretical and practical kinds of research have been conducted. Our research also focuses on the movie recommendation system by implementing methods from Social Network Analysis(SNA) and Parallel Programming. We applied the Girvan-Newman algorithm to detect communities of users, and a future package to perform the parallelization. This approach not only tries to improve the accuracy of the system but also accelerates the execution time. To do our experiment, we used the MovieLense Dataset.

• The Journal of the Korea Contents Association
• /
• v.13 no.12
• /
• pp.81-91
• /
• 2013

Social Media has been vitalized according to development of technology, it make the crowd-funding which have a form of new donation culture. The crowd-funding has been known as form that is supported for getting investments of ongoing or new project by much public in area of cultural art. Nowadays it receive attention from the movie content production. There are so many successful case such as , in abroad while it is hard to find distinct case in Korea' the movie content production market. Since the movie <26 years> informed public of 'the crowd-funding', recently was successfully complete first and second fund-raising and third fund-raising is in progress. It is upraised as a representative successful case.

• Communications for Statistical Applications and Methods
• /
• v.18 no.6
• /
• pp.859-869
• /
• 2011

Interest has increased in academic research regarding key factors that drive box-office success as well as the ability to predict the box-office success of a movie from a commercial perspective. This study analyzed the relationship between key success factors of a movie and box office records based on movies released in 2010 in Korea. At the pre-production investment decision-making stage, the movie genre, motion picture rating, director power, and actor power were statistically significant. At the stage of distribution decision-making process after movie production, among other factors, the influence of star actors, number of screens, power of distributors, and social media turned out to be statistically significant. We verified movie success factors through the application of a Multinomial Logit Model that used the concept of choice probabilities. The Multinomial Logit Model resulted in a higher level of accuracy in predicting box-office success compared to the Artificial Neural Network and Discriminant Analysis.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.525-531
• /
• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.