• Title, Summary, Keyword: Recommender Systems

Search Result 261, Processing Time 0.05 seconds

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.

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

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.

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.

Toward Socially Agreeable Aggregate Functions for Group Recommender Systems (Group Recommender System을 위한 구성원 합의 도출 함수에 관한 연구)

  • Ok, Chang-Soo;Lee, Seok-Cheon;Jeong, Byung-Ho
    • Journal of the Korean Operations Research and Management Science Society
    • /
    • v.32 no.4
    • /
    • pp.61-75
    • /
    • 2007
  • In ubiquitous computing, shared environments are required to adapt to people intelligently. Based on information about user preferences, the shared environments should be adjusted so that all users in a group are satisfied as possible. Although many group recommender systems have been proposed to obtain this purpose, they only consider average and misery. However, a broad range of philosophical approaches suggest that high inequality reduces social agreeability, and consequently causes users' dissatisfactions. In this paper, we propose social welfare functions, which consider inequalities in users' preferences, as alternative aggregation functions to achieve a social agreeability. Using an example in a previous work[7], we demonstrate the effectiveness of proposed welfare functions as socially agreeable aggregate functions in group recommender systems.

Design and Implementation of e-Commerce Applications using Improved Recommender Systems (개선된 추천시스템을 이용한 전자상거래시스템 설계 및 구현)

  • Kim, Yeong-Seol;Kim, Byeong-Cheon;Yun, Byeong-Ju
    • The KIPS Transactions:PartD
    • /
    • v.9D no.2
    • /
    • pp.329-336
    • /
    • 2002
  • With the fast development of Internet environment, e-Commerce is rapidly increasing. n the expanding e-Commerce environment, the need for a new e-Commerce systems what will deliver products to the customer rapidly and increase sales is growing bigger. Recently, these requirements brought many researches on recommender systems. However, until now, those recommender systems have a limit because it takes too much time for recommender systems to give customers the recommendations in real time, if the number of purchase data of customers is large. So this paper concerns on the recommender systems using collaborative filtering as one of the solutions to increase the competitive power. We proposed and experimented the more improved recommender systems which could decrease the data size to shorten the recommending time by using the representative category of the product which customers want to buy. Also, we design and implement a recommender system using Enterprise JavaBeans.

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.

The Influence of Social Presence on Evaluating Personalized Recommender Systems

  • Choi, Jae-Won;Lee, Hong-Joo;Kim, Yong-Chul
    • Proceedings of the Korean Operations and Management Science Society Conference
    • /
    • /
    • pp.410-414
    • /
    • 2008
  • Providing recommendations is acknowledged as one of important features of a business-to-consumer online storefront. Though there have been many studies on algorithms and operational procedures of personalized recommender systems, there is still a lack of empirical evidence demonstrating relationships between social presence and two important outcome variables of recommender systems: reuse intention and trust. To test the existence of a causal link between social presence and reuse intention, and mediating role of trust between these two variables, this study performed experiments varying level of social presence while providing personalized recommendations to users based on their explicit preferences. This study also compared these effects in two different product contexts: hedonic and utilitarian product. The results show that the provision of higher social presence increases both the reuse intention and trust of the recommender systems. In addition, the influence of social presence on reuse intention in the setting of recommending utilitarian products is less than that in the setting of recommending hedonic products.

  • PDF

An Integrated Perspective of User Evaluating Personalized Recommender Systems : Performance-Driven or User-Centric (개인화 추천시스템의 사용자 평가에 대한 통합적 접근 : 시스템 성과와 사용자 태도를 기반으로)

  • Choi, Jae-Won;Lee, Hong-Joo
    • The Journal of Society for e-Business Studies
    • /
    • v.17 no.3
    • /
    • pp.85-103
    • /
    • 2012
  • This study focused on user evaluation for personalized recommender systems with the integrated view of performance of the system and user attitude of recommender systems. Since users' evaluations of recommender systems can be affected by recommendation outcomes and presentation methods, both system performances based on outcomes and user attitudes formed by the presentation methods should be considered when explaining users' evaluations. However, an integrated view of system performance and user attitudes has not been applied to explain users' evaluation of recommender systems. Thus, the goal of this study is to explain users' evaluations of recommender systems under the integrated view of predictive features and explanation features at the same time. Our findings suggest that social presence, both accuracy and noveltyhave impacts onuser satisfaction for recommender systems. Especially, predictive features including accuracy and novelty affected user satisfaction. Novelty as well as accuracy is one of the significant factors for user satisfaction while recommender systems provided usual items users have experienced when systems provide serendipitous items. Likewise, explanation features with social presence and self-reference were important for user evaluation of personalized recommender systems. For explanation features, while social presence appears as one of important factors to user satisfaction of evaluating personalized recommendations, self-reference has no significant effect on user's satisfaction for recommender systems when compared to the result of social presence. Self-referencing messages did not affect user satisfaction but the levels of self-referencing are different between low and high groups in the experiment.

Using Experts Among Users for Novel Movie Recommendations

  • Lee, Kibeom;Lee, Kyogu
    • Journal of Computing Science and Engineering
    • /
    • v.7 no.1
    • /
    • pp.21-29
    • /
    • 2013
  • The introduction of recommender systems to existing online services is now practically inevitable, with the increasing number of items and users on online services. Popular recommender systems have successfully implemented satisfactory systems, which are usually based on collaborative filtering. However, collaborative filtering-based recommenders suffer from well-known problems, such as popularity bias, and the cold-start problem. In this paper, we propose an innovative collaborative-filtering based recommender system, which uses the concepts of Experts and Novices to create fine-grained recommendations that focus on being novel, while being kept relevant. Experts and Novices are defined using pre-made clusters of similar items, and the distribution of users' ratings among these clusters. Thus, in order to generate recommendations, the experts are found dynamically depending on the seed items of the novice. The proposed recommender system was built using the MovieLens 1 M dataset, and evaluated with novelty metrics. Results show that the proposed system outperforms matrix factorization methods according to discovery-based novelty metrics, and can be a solution to popularity bias and the cold-start problem, while still retaining collaborative filtering.