Hyea Kyeong Kim

A literature review and classification of recommender systems research

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. Although academic research on recommender systems has increased significantly over the past 10years, there are deficiencies in the comprehensive literature review and classification of that research. For that reason, we reviewed 210 articles on recommender systems from 46 journals published between 2001 and 2010, and then classified those by the year of publication, the journals in which they appeared, their application fields, and their data mining techniques. The 210 articles are categorized into eight application fields (books, documents, images, movie, music, shopping, TV programs, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). Our research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this paper helps anyone who is interested in recommender systems research with insight for future research direction.

A product network analysis for extending the market basket analysis

In this study, we propose a product network analysis, a network-based analysis to analyze a network-leveled relation among all products. Compared to market basket analysis, which focuses on the transaction-leveled relation between products, the suggested product network analysis focuses on extended network-leveled point of view of the relation between all products. For such a purpose, we suggest two kinds of product networks, market basket networks and co-purchased product networks. Two networks are comparatively evaluated to analyze the topological characteristics and the structure of those networks. The extended use of market basket analysis, network-leveled analysis are expected to be more effectively and efficiently used in personalized services, such as cross selling, up selling, and personalized product display utilizing the deep relation between products.

A collaborative filtering recommendation methodology for peer-to-peer systems

To deal with the image recommending problems in P2P systems, this paper proposes a PeerCF-CB (Peer oriented Collaborative Filtering recommendation methodology using Contents-Based filtering). PeerCF-CB uses recent ratings of peers to adopt a change in peer preferences, and searches for nearest peers with similar preference through peer-based local information only. The performance of PeerCF-CB is evaluated with real transaction data in S content provider. Our experimental result shows that PeerCF-CB offers not only remarkably higher quality of recommendations but also dramatically faster performance than the centralized collaborative filtering recommendation systems.

A peer-to-peer CF-Recommendation for ubiquitous environment

In ubiquitous environment where all entities can freely connect and collaborate with each other from anywhere, the amount of accessible information is overwhelming and desired information often remains unfound. So there is a growing need to provide the personalized recommendation services for the customers in ubiquitous space. This paper suggests a UREC_P2P(U-RECom-mendation by peer-to-peer), a recommendation procedure in ubiquitous environment adopting P2P technologies combined with collaborative filtering algorithm. UREC_P2P is implemented and comparatively evaluated with a CF-based recommender system in client-server environment. The evaluation result shows that UREC_P2P has a good potential to be a preeminent and realistic solution to the recommendation problems encountered in ubiquitous environment.

A hybrid recommendation procedure for new items using preference boundary

As new items are frequently released nowadays, item providers and customers need the recommender system which is specialized in recommending new items. Because most of previous approaches for recommender system have to rely on the usage history of customers, collaborative filtering is not directly applicable to solve the new item problem. Therefore they have suggested content-based recommender system using feature values of new items. However it is not sufficient to recommend new items. This research aims to suggest hybrid recommendation procedures based on preference boundary of target customer. We suggest TC, BC, and NC algorithms to determine the preference boundary. TC is an algorithm developed from contents-based filtering, whereas BC and NC are algorithms based on collaborative filtering, which incorporates neighbors, similar customers to a target customer. We evaluate the performances of suggested algorithms with real mobile image transaction data set. Experimental test results that the performances of BC and NC is better than that of TC, which means that the suggested hybrid procedures are more effective than the content-based approach.