• 제목/요약/키워드: Recommendation Systems

검색결과 839건 처리시간 0.024초

Empirical Comparison of the Effects of Online and Offline Recommendation Duration on Purchasing Decisions: Case of Korea Food E-commerce Company

  • Qinglong Li;Jaeho Jeong;Dongeon Kim;Xinzhe Li;Ilyoung Choi;Jaekyeong Kim
    • Asia pacific journal of information systems
    • /
    • 제34권1호
    • /
    • pp.226-247
    • /
    • 2024
  • Most studies on recommender systems to evaluate recommendation performances focus on offline evaluation methods utilizing past customer transaction records. However, evaluating recommendation performance through real-world stimulation becomes challenging. Moreover, such methods cannot evaluate the duration of the recommendation effect. This study measures the personalized recommendation (stimulus) effect when the product recommendation to customers leads to actual purchases and evaluates the duration of the stimulus personalized recommendation effect leading to purchases. The results revealed a 4.58% improvement in recommendation performance in the online environment compared with that in the offline environment. Furthermore, there is little difference in recommendation performance in offline experiments by period, whereas the recommendation performance declines with time in online experiments.

Performance Analysis of Group Recommendation Systems in TV Domains

  • Kim, Noo-Ri;Lee, Jee-Hyong
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • 제15권1호
    • /
    • pp.45-52
    • /
    • 2015
  • Although researchers have proposed various recommendation systems, most recommendation approaches are for single users and there are only a small number of recommendation approaches for groups. However, TV programs or movies are most often viewed by groups rather than by single users. Most recommendation approaches for groups assume that single users' profiles are known and that group profiles consist of the single users' profiles. However, because it is difficult to obtain group profiles, researchers have only used synthetic or limited datasets. In this paper, we report on various group recommendation approaches to a real large-scale dataset in a TV domain, and evaluate the various group recommendation approaches. In addition, we provide some guidelines for group recommendation systems, focusing on home group users in a TV domain.

Design of a Recommendation System for Improving Deep Neural Network Performance

  • Juhyoung Sung;Kiwon Kwon;Byoungchul Song
    • 인터넷정보학회논문지
    • /
    • 제25권1호
    • /
    • pp.49-56
    • /
    • 2024
  • There have been emerging many use-cases applying recommendation systems especially in online platform. Although the performance of recommendation systems is affected by a variety of factors, selecting appropriate features is difficult since most of recommendation systems have sparse data. Conventional matrix factorization (MF) method is a basic way to handle with problems in the recommendation systems. However, the MF based scheme cannot reflect non-linearity characteristics well. As deep learning technology has been attracted widely, a deep neural network (DNN) framework based collaborative filtering (CF) was introduced to complement the non-linearity issue. However, there is still a problem related to feature embedding for use as input to the DNN. In this paper, we propose an effective method using singular value decomposition (SVD) based feature embedding for improving the DNN performance of recommendation algorithms. We evaluate the performance of recommendation systems using MovieLens dataset and show the proposed scheme outperforms the existing methods. Moreover, we analyze the performance according to the number of latent features in the proposed algorithm. We expect that the proposed scheme can be applied to the generalized recommendation systems.

추천시스템 연구의 개발추세 동향 (Development Trend Analysis of the Research on Recommendation System)

  • 이연님;권오병
    • 지능정보연구
    • /
    • 제14권2호
    • /
    • pp.63-82
    • /
    • 2008
  • 추천시스템은 정보 과부하의 문제를 해결하기 위해 폭넓게 사용되어지고 있다. 지난 수십년 동안 다양한 추천시스템이 정보량이 그것을 처리할 수 있는 능력보다 더 빠르게 증가하게 됨에 따라 개발되어져 왔다. 이 같은 상황에서 본 연구의 목적은 기 개발된 추천시스템을 분석하여 시스템적 관점을 제공하고 이를 구현하는데 따르는 기본적인 이슈들을 밝히는 것이다. 이를 통하여 추천시스템의 개선을 위한 유용한 정보를 제안하며, 시스템 개발자들에게는 그러한 시스템을 개선하기 위한 아이디어를 제공하고자 한다. 특히 본 연구는 추천시스템의 이론적 관점에 집중하는데, 이를 위해 과거 추천시스템의 도메인과 목표, 주요 방법 및 평가 방법에 대해서 다루고자 하며, 이 결과는 통계치나 도표 등의 형태로 보이려고 한다.

  • PDF

자유 위협과 개인화에 대한 사용자의 지각이 상품 추천 서비스 수용에 미치는 영향 (Effects of the User's Perceived Threat to Freedom and Personalization on Intention to Use Recommendation Services)

  • 이규동;김종욱;이원준
    • Asia pacific journal of information systems
    • /
    • 제17권1호
    • /
    • pp.123-145
    • /
    • 2007
  • There are flourishing studies in the acceptance or usage of information systems literature. Most of them have taken the pro - acceptance view. Undesirably, information technologies often provoke users' reactance or resistance. This paper explores one of the negative reactions -psychological reactance. The present paper studies the effects of the users' perception of threatened freedom and personalization degree on intention to use recommendation services. High personalization can be a major motivation for users to accept recommendation systems. However recommendation services are a two-edged sword, which not only provides users the efficiency of decision making but also poses threats to free choice. When people consider that their freedom is reduced or threatened by others, they experience the motivational state to restore the freedom. This motivational state must be considered in understanding usage of information systems, especially personalized services which are designed for persuasion or compliance. This paper empirically investigates the effect of personalization and the psychological reactance on the intention to use information systems in the personalized recommendation context. Users' perception of personalization increases the usefulness of recommendation service while their perception of threat to freedom reduces the intention to use personalized recommendation service. Findings and implications are discussed.

속성추출을 이용한 협동적 추천시스템의 성능 향상 (Performance Improvement of a Collaborative Recommendation System using Feature Selection)

  • 유상종;권영식
    • 산업공학
    • /
    • 제19권1호
    • /
    • pp.70-77
    • /
    • 2006
  • One of the problems in developing a collaborative recommendation system is the scalability. To alleviate the scalability problem efficiently, enhancing the performance of the recommendation system, we propose a new recommendation system using feature selection. In our experiments, the proposed system using about a third of all features shows the comparable performances when compared with using all features in light of precision, recall and number of computations, as the number of users and products increases.

Multi-Purpose Hybrid Recommendation System on Artificial Intelligence to Improve Telemarketing Performance

  • Hyung Su Kim;Sangwon Lee
    • Asia pacific journal of information systems
    • /
    • 제29권4호
    • /
    • pp.752-770
    • /
    • 2019
  • The purpose of this study is to incorporate telemarketing processes to improve telemarketing performance. For this application, we have attempted to mix the model of machine learning to extract potential customers with personalisation techniques to derive recommended products from actual contact. Most of traditional recommendation systems were mainly in ways such as collaborative filtering, which predicts items with a high likelihood of future purchase, based on existing purchase transactions or preferences for products. But, under these systems, new users or items added to the system do not have sufficient information, and generally cause problems such as a cold start that can not obtain satisfactory recommendation items. Also, indiscriminate telemarketing attempts can backfire as they increase the dissatisfaction and fatigue of customers who do not want to be contacted. To this purpose, this study presented a multi-purpose hybrid recommendation algorithm to achieve two goals: to select customers with high possibility of contact, and to recommend products to selected customers. In addition, we used subscription data from telemarketing agency that handles insurance products to derive realistic applicability of the proposed recommendation system. Our proposed recommendation system would certainly solve the cold start and scarcity problem of existing recommendation algorithm by using contents information such as customer master information and telemarketing history. Also. the model could show excellent performance not only in terms of overall performance but also in terms of the recommendation success rate of the unpopular product.

개인별 상품추천시스템, WebCF-PT: 웹마이닝과 상품계층도를 이용한 협업필터링 (A Personalized Recommender System, WebCF-PT: A Collaborative Filtering using Web Mining and Product Taxonomy)

  • 김재경;안도현;조윤호
    • Asia pacific journal of information systems
    • /
    • 제15권1호
    • /
    • pp.63-79
    • /
    • 2005
  • 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 system, WebCF-PT based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of traditional 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. A prototype recommendation system, WebCF-PT is developed and Internet shopping mall, EBIB(e-Business & Intelligence Business) is constructed to test the WebCF-PT system.

사용자 상황을 이용한 추천 서비스 시스템의 필터링 기법에 관한 연구 (A Study on a Filtering Method of Recommendation Service System Using User's Context)

  • 한동조;박대영;최기호
    • 한국ITS학회 논문지
    • /
    • 제8권1호
    • /
    • pp.119-126
    • /
    • 2009
  • 최근 개개인의 취향이나 특성을 고려하여 자동으로 사용자에게 정보를 찾아주거나 추천해주는 추천 서비스 시스템이 많이 개발되고 있다. 하지만 사용자의 상황에 따른 선호도를 고려하지 않을 경우 정확한 추천이 힘든 단점이 있다. 따라서 본 논문에서는 사용자의 상황에 따른 선호도를 고려하여 정확한 추천을 할 수 있는 필터링 방법을 제안하였다. 이를 위해 상황에 따른 사용자 선호도를 구하고 피어슨 상관계수를 이용하여 사용자의 상황별 오브젝트 선호도를 구하였다. 실험 결과, 기존의 서비스 시스템들과 비교하여 precision은 11%, 2%, recall은 8%, 4% 향상되었으며, 전체적으로 precision은 77%, recall은 53%로 나타났다.

  • PDF

A Web Recommendation System using Grid based Support Vector Machines

  • Jun, Sung-Hae
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • 제7권2호
    • /
    • pp.91-95
    • /
    • 2007
  • Main goal of web recommendation system is to study how user behavior on a website can be predicted by analyzing web log data which contain the visited web pages. Many researches of the web recommendation system have been studied. To construct web recommendation system, web mining is needed. Especially, web usage analysis of web mining is a tool for recommendation model. In this paper, we propose web recommendation system using grid based support vector machines for improvement of web recommendation system. To verify the performance of our system, we make experiments using the data set from our web server.