• Title/Summary/Keyword: Collaborative Recommendation

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Distributed Recommendation System Using Clustering-based Collaborative Filtering Algorithm (클러스터링 기반 협업 필터링 알고리즘을 사용한 분산 추천 시스템)

  • Jo, Hyun-Je;Rhee, Phill-Kyu
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.14 no.1
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    • pp.101-107
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    • 2014
  • This paper presents an efficient distributed recommendation system using clustering collaborative filtering algorithm in distributed computing environments. The system was built based on Hadoop distributed computing platform, where distributed Min-hash clustering algorithm is combined with user based collaborative filtering algorithm to optimize recommendation performance. Experiments using Movie Lens benchmark data show that the proposed system can reduce the execution time for recommendation compare to sequential system.

A Social Travel Recommendation System using Item-based collaborative filtering

  • Kim, Dae-ho;Song, Je-in;Yoo, So-yeop;Jeong, Ok-ran
    • Journal of Internet Computing and Services
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    • v.19 no.3
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    • pp.7-14
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    • 2018
  • As SNS(Social Network Service) becomes a part of our life, new information can be derived through various information provided by SNS. Through the public timeline analysis of SNS, we can extract the latest tour trends for the public and the intimacy through the social relationship analysis in the SNS. The extracted intimacy can also be used to make the personalized recommendation by adding the weights to friends with high intimacy. We apply SNS elements such as analyzed latest trends and intimacy to item-based collaborative filtering techniques to achieve better accuracy and satisfaction than existing travel recommendation services in a new way. In this paper, we propose a social travel recommendation system using item - based collaborative filtering.

A Contents Recommendation Scheme Based on Collaborative Filtering Using Consumer's Affection and Consumption Type (소비자의 감성과 소비유형을 이용한 협업여과기반 콘텐츠 추천 기법)

  • Choi, In-Bok;Park, Tae-Keun;Lee, Jae-Dong
    • The KIPS Transactions:PartD
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    • v.15D no.3
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    • pp.421-428
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    • 2008
  • Collaborative filtering is a popular technique used for the recommendation system, but its performance, especially the accuracy of recommendation, depends on how to define the reference group. This paper proposes a new contents recommendation scheme based on collaborative filtering technique whose reference groups are created by consumer's affection and consumption type in order to improve the accuracy of recommendation. In this paper, joy, sadness, anger, happiness, and relax are considered as the consumer's affection. And, low-utility / low-pleasure, low-utility / high-pleasure, high-utility / low-pleasure, and high-utility / high-pleasure are considered as the consumer's shopping types. Experimental results show that the proposed scheme improves the accuracy of recommendation compared to the recommendation scheme considering neither consumer's affection nor consumption type.

APMDI-CF: An Effective and Efficient Recommendation Algorithm for Online Users

  • Ya-Jun Leng;Zhi Wang;Dan Peng;Huan Zhang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.11
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    • pp.3050-3063
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    • 2023
  • Recommendation systems provide personalized products or services to online users by mining their past preferences. Collaborative filtering is a popular recommendation technique because it is easy to implement. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. To address these problems, a novel collaborative filtering recommendation algorithm is proposed. The proposed algorithm partitions the users using affinity propagation clustering, and searches for k nearest neighbors in the partition where active user belongs, which can reduce the range of searching and improve real-time performance. When predicting the ratings of active user's unrated items, mean deviation method is used to impute values for neighbors' missing ratings, thus the sparsity can be decreased and the recommendation quality can be ensured. Experiments based on two different datasets show that the proposed algorithm is excellent both in terms of real-time performance and recommendation quality.

Development of a Personalized Recommendation Procedure Based on Data Mining Techniques for Internet Shopping Malls (인터넷 쇼핑몰을 위한 데이터마이닝 기반 개인별 상품추천방법론의 개발)

  • Kim, Jae-Kyeong;Ahn, Do-Hyun;Cho, Yoon-Ho
    • Journal of Intelligence and Information Systems
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    • v.9 no.3
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    • pp.177-191
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    • 2003
  • Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is the most successful recommendation technology. Web usage mining and clustering analysis are widely used in the recommendation field. In this paper, we propose several hybrid collaborative filtering-based recommender procedures to address the effect of web usage mining and cluster analysis. Through the experiment with real e-commerce data, it is found that collaborative filtering using web log data can perform recommendation tasks effectively, but using cluster analysis can perform efficiently.

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Product-group Recommendation based on Association Rule Mining and Collaborative Filtering in Ubiquitous Computing Environment (유비쿼터스 환경에서 연관규칙과 협업필터링을 이용한 상품그룹추천)

  • Kim, Jae-Kyeong;Oh, Hee-Young;Kwon, Oh-Byung
    • Journal of Information Technology Services
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    • v.6 no.2
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    • pp.113-123
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    • 2007
  • In ubiquitous computing environment such as ubiquitous marketplace (u-market), there is a need of providing context-based personalization service while considering the nomadic user preference and corresponding requirements. To do so, the recommendation systems should deal with the tremendous amount of context data. Hence, the purpose of this paper is to propose a novel recommendation method which provides the products-group list of the customers in u-market based on the shopping intention and preferences. We have developed FREPIRS(FREquent Purchased Item-sets Recommendation Service), which makes recommendation listof product-group, not individual product. Collaborative filtering and apriori algorithm are adopted in FREPIRS to build product-group.

The cluster-indexing collaborative filtering recommendation

  • Park, Tae-Hyup;Ingoo Han
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2003.05a
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    • pp.400-409
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    • 2003
  • Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

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Design and Implementation of a User-based Collaborative Filtering Application using Apache Mahout - based on MongoDB -

  • Lee, Junho;Joo, Kyungsoo
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.4
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    • pp.89-95
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    • 2018
  • It is not easy for the user to find the information that is appropriate for the user among the suddenly increasing information in recent years. One of the ways to help individuals make decisions in such a lot of information is the recommendation system. Although there are many recommendation methods for such recommendation systems, a representative method is collaborative filtering. In this paper, we design and implement the movie recommendation system on user-based collaborative filtering of apache mahout based on mongoDB. In addition, Pearson correlation coefficient is used as a method of measuring the similarity between users. We evaluate Precision and Recall using the MovieLens 100k dataset for performance evaluation.

Personalized Movie Recommendation System Using Context-Aware Collaborative Filtering Technique (상황기반과 협업 필터링 기법을 이용한 개인화 영화 추천 시스템)

  • Kim, Min Jeong;Park, Doo-Soon;Hong, Min;Lee, HwaMin
    • KIPS Transactions on Computer and Communication Systems
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    • v.4 no.9
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    • pp.289-296
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    • 2015
  • The explosive growth of information has been difficult for users to get an appropriate information in time. The various ways of new services to solve problems has been provided. As customized service is being magnified, the personalized recommendation system has been important issue. Collaborative filtering system in the recommendation system is widely used, and it is the most successful process in the recommendation system. As the recommendation is based on customers' profile, there can be sparsity and cold-start problems. In this paper, we propose personalized movie recommendation system using collaborative filtering techniques and context-based techniques. The context-based technique is the recommendation method that considers user's environment in term of time, emotion and location, and it can reflect user's preferences depending on the various environments. In order to utilize the context-based technique, this paper uses the human emotion, and uses movie reviews which are effective way to identify subjective individual information. In this paper, this proposed method shows outperforming existing collaborative filtering methods.

Recommendation system for supporting self-directed learning on e-learning marketplace (이러닝 마켓플레이스에서 자기주도학습지원을 위한 추천시스템)

  • Kwon, Byung-Il;Moon, Nam-Mee
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.2
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    • pp.135-146
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    • 2010
  • In this paper, we propose an Recommendation System for supporting self-directed learning on e-learning marketplace. The key idea of this system is recommendation system using revised collaborative filtering to support marketplace. Exisiting collaborative filtering method consists of 3 stages as preparing low data, building familiar customer group by selecting nearest neighbor, creating recommendation list. This study designs recommendation system to support self-directed learning by using collaborative filtering added nearest neighbor learning course that considered industry and learning level. This service helps to select right learning course to learner in industry. Recommendation System can be built by many method and to recommend the service content including explicit properties using revised collaborative filtering method can solve limitations in existing content recommendation.