• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.04a
• /
• pp.727-728
• /
• 2014

온라인 서비스와 스마트 기기의 발달로 언제 어디서나 인터넷에 접속할 수 있는 시대가 도래되었다. 수많은 콘텐츠와 서비스가 쏟아져 사용자 입장에서 자신이 선호하는 콘텐츠를 자신이 원할 때 전달받는 것이 필요해졌다. 즉, 사용자의 선호도에 따라 콘텐츠를 추천하는 시스템이 현재 실생활에서도 활용되고 있다는 뜻이다. 이를 근거로 대용량의 데이터를 다루는 마이닝 기법 기반의 추천 시스템인 협업 필터링 추천기법과 내용기반 추천기법의 개념과 문제점들을 분석해 보았다.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.3
• /
• pp.99-105
• /
• 2021

In addition to the OTT video production service represented by Nexflix and YouTube, a personalized recommendation system for content with artificial intelligence has become common. YouTube's personalized recommendation service system consists of two neural networks, one neural network consisting of a recommendation candidate generation model and the other consisting of a ranking network. Netflix's video recommendation system consists of two data classification systems, divided into content-based filtering and collaborative filtering. As the online platform-led content production is activated by the Corona Pandemic, the field of virtual influencers using artificial intelligence is emerging. Virtual influencers are produced with GAN (Generative Adversarial Networks) artificial intelligence, and are unsupervised learning algorithms in which two opposing systems compete with each other. This study also researched the possibility of developing AI platform based on individual recommendation and virtual influencer (metabus) as a core content of OTT in the future.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.151-166
• /
• 2020

A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer's search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.05a
• /
• pp.372-374
• /
• 2020

최근 화장품이나 뷰티산업의 성장이 가속화되고 있다. 이에 따라 시장에 다양한 뷰티제품들이 출시되고 있지만 그로 인해 오히려 본인에게 적합한 제품이 무엇인지 알지 못하는 경우가 많다. 온라인을 통해 구매하는 경우 구매후기 및 광고에 의지해야 하며 전문가의 조언을 구하기 위해서는 오프라인 상점을 방문할 수밖에 없다. 그러나 오프라인 상점을 방문한 경우에도 자신에게 적합한 화장품을 추천받는 것 또한 다분하지 않다. 본 논문에서는 이러한 문제점을 해결하고자 온라인 환경에서 소비자에게 맞는 상품의 광고 및 정보를 받을 수 있는 화장품 추천 서비스를 제안한다. 또한 제안서비스는 AI기능을 적용하여 기존의 방식보다 소비자 친화적인 서바스를 제공하는 것을 목표로 한다.

• Proceedings of the Korea Contents Association Conference
• /
• 2016.05a
• /
• pp.37-38
• /
• 2016

온라인 소셜 네트워크(OSN)의 활성화로 인해 다양한 정보가 생성됨에 따라 사용자에 적합한 정보를 선택적으로 제공하기 위한 개인 추천 서비스에 대한 연구가 진행되고 있다. 본 논문에서는 온라인 소셜 네트워크에서 사용자 신뢰성을 고려한 콘텐츠 추천 기법을 제안한다. 제안하는 기법은 추천의 정확성을 향상시키기 위해 신뢰성 있는 사용자를 선별한다. 사용자 신뢰성을 기반으로 유사 사용자를 선별하고 이를 기반으로 협업 필터링을 수행한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.437-438
• /
• 2019

스마트폰을 중심으로 한 모바일 기기의 보급과 온라인 소셜 네트워크 서비스의 이용자들이 증가하면서 사용자들은 많은 콘텐츠를 소비하고 공유한다. 이는 콘텐츠 사용자들의 개별적 기호에 맞지 않거나 만족도가 떨어지는 콘텐츠를 소비하게 한다. 이와 같은 문제를 해결하기 위해 소셜 네트워크 사용자에게 적합한 콘텐츠를 추천하기 위한 기법에 대한 연구가 활발하게 진행되고 있다. 본 논문에서는 온라인 상에 존재하는 다양한 정보 중에서 공연과 관련한 콘텐츠들을 중심으로 사용자 성향별로 추천을 해줄 수 있는 협업필터링 방법에 대하여 제안한다.

• The Journal of the Korea Contents Association
• /
• v.19 no.3
• /
• pp.171-182
• /
• 2019

As online shopping malls continue to grow in popularity, various chances of consumption are provided to customers. Customers decide the purchase by exploiting information provided by shopping malls such as the reviews of actual purchasing users, the detailed information of items, and so on. It is required to provide objective and reliable information because customers have to decide on their own whether the massive information is credible. In this paper, we propose a personalized recommendation method considering an item confidence to recommend reliable items. The proposed method determines user preferences based on various behaviors for personalized recommendation. We also propose an user preference measurement that considers time weights to apply the latest propensity to consume. Finally, we predict the preference score of items that have not been used or purchased before, and we recommend items that have highest scores in terms of both the predicted preference score and the item confidence score.

• Information Systems Review
• /
• v.18 no.2
• /
• pp.59-77
• /
• 2016

Although various recommendation techniques have been applied to the e-commerce market, few studies compare the intent to use these techniques from the customer's perspective. In this paper, we conduct a comparative analysis of customers' intention to use five recommendation techniques widely adapted by online shopping malls and focus on the differences in purchasing electronic goods and apparel products. The recommendation techniques are as follows: best-seller recommendation, merchandiser recommendation, content-based recommendation, collaborative filtering recommendation, and social recommendation. Additionally, we examine which factors influence customer intent to use the recommendation services. Data were collected through a survey administered to 220 e-commerce users with prior experience with recommendation services. Collected data were examined using analysis of variance and regression analysis. Results indicate statistically significant differences in customers' intention to use recommendation services according to the recommendation technique. In particular, the best-seller recommendation technique is preferred when purchasing electronic goods, whereas the content-based recommendation technique is preferred for apparel purchases. Factors such as personal characteristics and personality, purchasing tendency, as well as perception of the product or recommendation service affect a customer's intention to use a recommendation service. However, the influence of these factors varies depending on the recommendation technique. This study provides guidelines for companies to adopt appropriate recommendation techniques according to product categories and personal characteristics of customers.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.1129-1131
• /
• 2013

온라인 상에서 불특정 타인과 관계를 맺을 수 있는 서비스로 소셜 네트워크 서비스(Social Network Service : SNS)가 새롭게 떠오르고 있다. 1990년대에 등장한 SNS는 최근에는 스마트폰을 이용한 모바일 서비스로 인해 이용자의 수가 급격히 늘어나고 있다. SNS에서는 '친구 찾기' 라는 서비스를 제공하는데, 이는 이용자의 개인정보를 분석하여 이용하여 친구를 찾아주는 서비스이다. 기존의 '친구 찾기' 서비스는 이용자가 제공하는 정보만을 다른 이용자의 정보와 비교하여 친구를 찾았다. 그러나 이용자가 제공하는 정보는 한정적이기 때문에 비교할 수 있는 정보의 양도 한정되어 찾을 수 있는 친구의 수에도 한계가 생긴다. 그래서 본 논문에서는 단순한 개인정보 비교를 통한 친구를 찾는 방법이 아닌 이용자가 제공하는 시공간 데이터를 활용하여 추론을 통해 친구를 추천해주는 시스템을 설계하고 구현한다.

• Information Systems Review
• /
• v.16 no.3
• /
• pp.49-68
• /
• 2014

These days, consumers have increasingly preferred to digital real-time streamlining and downloading to listen to music because this is convenient and affordable for the consumers. Accordingly, sales of music in compact disk formats have steadily declined. In this regards, online digital music has become a new communication channel to listen musics, where digital files can be delivered over various online networks to people's computing devices. The majority of online digital music distributors has Music Recommender Systems for sales of digital music on their websites. Music Recommender Systems are parts of information filtering systems that provide the ratings or preferences that users give to music. Korean online digital music distributors have Music Recommender Systems. But those online music distributors didn't provide any rules or clear procedures that recommend music. Therefore, we raise important questions as follows: "Is Music Recommender Systems Fair?", "What is the impact of Music Recommender Systems on online music rankings and sales?" While previous studies have focused on usefulness of Music Recommender Systems, this study investigates not only fairness of Current Music Recommender Systems but also Relationship between Music Recommender Systems and online Music Charts. This study examines these issues based on Bandwagon effect, ranking effect, Slot effect theories. For our empirical analysis, we selected the most famous five online digital music distributors in terms of market shares. We found that all recommended music is exposed to the top of 'daily music charts' in online digital music distributors' websites. We collected music ranking data and recommended music data from 'daily music chart' during a one month. The result shows that online music recommender systems are not fair, since they mainly recommend particular music that supported by a specific music production company. In addition, the recommended music are always exposed to the top of music ranking charts. We also find that recommended music usually appear at the top 20 ranking charts within one or two days. Also, the most music in the top 50 or 100 ranks are the recommended music. Moreover, recommended music usually remain the ranking charts more than one month while non-recommended music often disappear at the ranking charts within two week. Our study provides an important implication to online music industry. Because music recommender systems and music ranking charts are closely related, music distributors may improperly use their recommender systems to boost the sales of music that related to their own companies. Therefore, online digital music distributor must clearly announce the rules and procedures about music recommender systems for the better music industry.