• Title, Summary, Keyword: recommender system

Search Result 361, Processing Time 0.047 seconds

Reconfiguration of Recommender System Success with DeLone and McLean's Model of IS Success (DeLone과 McLean의 정보시스템 성공 모형을 통한 추천시스템 성공 요인 재구성)

  • Kwon, Ohbyung
    • Knowledge Management Research
    • /
    • v.11 no.4
    • /
    • pp.21-39
    • /
    • 2010
  • Recommender system is a core component of e-commerce. Correspondingly, metrics to evaluate the system performance have been developed and applied. However, even though we have lots of applications that have tried to adopt recommender systems, the dearth of successfully installed recommender systems for more than a decade leads us to a skeptical thinking that current metrics do not sufficiently indicate the recommender system success in business viability point of view. Hence, the purpose of this paper is to reconfigure measures for recommender system success. Adopting DeLone and McLean's amended model of information system success as the underlying framework, content analysis with intellectual properties on recommender systems was conducted to modify the currently used metrics. Then a model of recommender system success is proposed based on the newly identified metrics are compared with traditional metrics.

  • PDF

Application of diversity of recommender system accordingtouserpreferencechange (사용자 선호도 변화에 따른 추천시스템의 다양성 적용)

  • Na, Hyeyeon;Nam, Kihwan
    • Journal of Intelligence and Information Systems
    • /
    • v.26 no.4
    • /
    • pp.67-86
    • /
    • 2020
  • Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.

Scalable Hybrid Recommender System with Temporal Information (시간 정보를 이용한 확장성 있는 하이브리드 Recommender 시스템)

  • Ullah, Farman;Sarwar, Ghulam;Kim, Jae-Woo;Moon, Kyeong-Deok;Kim, Jin-Tae;Lee, Sung-Chang
    • The Journal of the Institute of Internet, Broadcasting and Communication
    • /
    • v.12 no.2
    • /
    • pp.61-68
    • /
    • 2012
  • Recommender Systems have gained much popularity among researchers and is applied in a number of applications. The exponential growth of users and products poses some key challenges for recommender systems. Recommender Systems mostly suffer from scalability and accuracy. The accuracy of Recommender system is somehow inversely proportional to its scalability. In this paper we proposed a Context Aware Hybrid Recommender System using matrix reduction for Hybrid model and clustering technique for predication of item features. In our approach we used user item-feature rating, User Demographic information and context information i.e. specific time and day to improve scalability and accuracy. Our Algorithm produce better results because we reduce the dimension of items features matrix by using different reduction techniques and use user demographic information, construct context aware hybrid user model, cluster the similar user offline, find the nearest neighbors, predict the item features and recommend the Top N- items.

Tourism Destination Recommender System for the Cold Start Problem

  • Zheng, Xiaoyao;Luo, Yonglong;Xu, Zhiyun;Yu, Qingying;Lu, Lin
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.10 no.7
    • /
    • pp.3192-3212
    • /
    • 2016
  • With the advent and popularity of e-commerce, an increasing number of consumers prefer to order tourism products online. A recommender system can help these users contend with information overload; however, such a system is affected by the cold start problem. Online tourism destination searching is a more difficult task than others on account of its more restrictive factors. In this paper, we therefore propose a tourism destination recommender system that employs opinion-mining technology to refine user preferences and item opinion reputations. These elements are then fused into a hybrid collaborative filtering method by combining user- and item-based collaborative filtering approaches. Meanwhile, we embed an artificial interactive module in our recommender system to alleviate the cold start problem. Compared with several well-known cold start recommendation approaches, our method provides improved recommendation accuracy and quality. A series of experimental evaluations using a publicly available dataset demonstrate that the proposed recommender system outperforms existing recommender systems in addressing the cold start problem.

Application of Domain Knowledge in Transaction-based Recommender Systems through Word Embedding (트랜잭션 기반 추천 시스템에서 워드 임베딩을 통한 도메인 지식 반영)

  • Choi, Yeoungje;Moon, Hyun Sil;Cho, Yoonho
    • Knowledge Management Research
    • /
    • v.21 no.1
    • /
    • pp.117-136
    • /
    • 2020
  • In the studies for the recommender systems which solve the information overload problem of users, the use of transactional data has been continuously tried. Especially, because the firms can easily obtain transactional data along with the development of IoT technologies, transaction-based recommender systems are recently used in various areas. However, the use of transactional data has limitations that it is hard to reflect domain knowledge and they do not directly show user preferences for individual items. Therefore, in this study, we propose a method applying the word embedding in the transaction-based recommender system to reflect preference differences among users and domain knowledge. Our approach is based on SAR, which shows high performance in the recommender systems, and we improved its components by using FastText, one of the word embedding techniques. Experimental results show that the reflection of domain knowledge and preference difference has a significant effect on the performance of recommender systems. Therefore, we expect our study to contribute to the improvement of the transaction-based recommender systems and to suggest the expansion of data used in the recommender system.

The research of new algorithm to improve prediction accuracy of recommender system in electronic commercey

  • Kim, Sun-Ok
    • Journal of the Korean Data and Information Science Society
    • /
    • v.21 no.1
    • /
    • pp.185-194
    • /
    • 2010
  • In recommender systems which are used widely at e-commerce, collaborative filtering needs the information of user-ratings and neighbor user-ratings. These are an important value for recommendation in recommender systems. We investigate the in-formation of rating in NBCFA (neighbor Based Collaborative Filtering Algorithm), we suggest new algorithm that improve prediction accuracy of recommender system. After we analyze relations between two variable and Error Value (EV), we suggest new algorithm and apply it to fitted line. This fitted line uses Least Squares Method (LSM) in Exploratory Data Analysis (EDA). To compute the prediction value of new algorithm, the fitted line is applied to experimental data with fitted function. In order to confirm prediction accuracy of new algorithm, we applied new algorithm to increased sparsity data and total data. As a result of study, the prediction accuracy of recommender system in the new algorithm was more improved than current algorithm.

A Literature Review and Classification of Recommender Systems on Academic Journals (추천시스템관련 학술논문 분석 및 분류)

  • Park, Deuk-Hee;Kim, Hyea-Kyeong;Choi, Il-Young;Kim, Jae-Kyeong
    • Journal of Intelligence and Information Systems
    • /
    • v.17 no.1
    • /
    • pp.139-152
    • /
    • 2011
  • Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

L-PRS: A Location-based Personalized Recommender System

  • Kim, Taek-hun;Song, Jin-woo;Yang, Sung-bong
    • Proceedings of the KAIS Fall Conference
    • /
    • /
    • pp.113-117
    • /
    • 2003
  • As the wireless communication technology advances rapidly, a personalization technology can be incorporated with the mobile Internet environment, which is based on location-based services to support more accurate personalized services. A location-based personalized recommender system is one of the essential technologies of the location-based application services, and is also a crucial technology for the ubiquitous environment. In this paper we propose a framework of a location-based personalized recommender system for the mobile Internet environment. The proposed system consists of three modules the interface module, the neighbor selection module and the prediction and recommendation module. The proposed system incorporates the concept of the recommendation system in the Electronic Commerce along with that of the mobile devices for possible expansion of services on the mobile devices. Finally a service scenario for entertainment recommendation based on the proposed recommender system is described.

  • PDF

Pre-Evaluation for Detecting Abnormal Users in Recommender System

  • Lee, Seok-Jun;Kim, Sun-Ok;Lee, Hee-Choon
    • Journal of the Korean Data and Information Science Society
    • /
    • v.18 no.3
    • /
    • pp.619-628
    • /
    • 2007
  • This study is devoted to suggesting the norm of detection abnormal users who are inferior to the other users in the recommender system compared with estimation accuracy. To select the abnormal users, we propose the pre-filtering method by using the preference ratings to the item rated by users. In this study, the experimental result shows the possibility of detecting the abnormal users before the process of preference estimation through the prediction algorithm. And It will be possible to improve the performance of the recommender system by using this detecting norm.

  • PDF

A Recommendation System Based-on Interactive Evolutionary Computation with Data Grouping (데이터 그룹화를 이용한 상호진화연산 기반의 추천 시스템)

  • Kim, Hyun-Tae;Ahn, Chang-Wook;An, Jin-Ung
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.17 no.8
    • /
    • pp.739-746
    • /
    • 2011
  • Recently, recommender systems have been widely applied in E-commerce websites to help their customers find the items what they want. A recommender system should be able to provide users with useful information regarding their interests. The ability to immediately respond to the changes in user's preference is a valuable asset of recommender systems. This paper proposes a novel recommender system which aims to effectively adapt and respond to the immediate changes in user's preference. The proposed system combines IEC (Interactive Evolutionary Computation) with a content-based filtering method and also employs data grouping in order to improve time efficiency. Experiments show that the proposed system makes acceptable recommendations while ensuring quality and speed. From a comparative experimental study with an existing recommender system which uses the content-based filtering, it is revealed that the proposed system produces more reliable recommendations and adaptively responds to the changes in any given condition. It denotes that the proposed approach can be an alternative to resolve limitations (e.g., over-specialization and sparse problems) of the existing methods.