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%0 Book Section
%A Gemmell, Jonathan; Schimoler, Thomas; Mobasher, Bamshad & Burke, Robin
%D 2010
%T Resource Recommendation in Collaborative Tagging Applications
%E Buccafurri, Francesco & Semeraro, Giovanni
%B E-Commerce and Web Technologies
%C Berlin/Heidelberg
%I Springer
%V 61
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%N 
%P 1--12
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%S Lecture Notes in Business Information Processing
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%@ 978-3-642-15208-5
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%3 incollection
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%F gemmell2010resource
%K collaborative, folksonomy, item, recommender, tagging
%X Collaborative tagging applications enable users to annotate online resources with user-generated keywords. The collection of these annotations and the way they connect users and resources produce a rich information space for users to explore. However the size, complexity and chaotic structure of these systems hamper users as they search for information. Recommenders can assist the user by suggesting resources, tags or even other users. Previous work has demonstrated that an integrative approach which exploits all three dimensions of the data (users, resources, tags) produce superior results in tag recommendation. We extend this integrative philosophy to resource recommendation. Specifically, we propose an approach for designing weighted linear hybrid resource recommenders. Through extensive experimentation on two large real world datasets, we show that the hybrid recommenders surpass the effectiveness of their constituent components while inheriting their simplicity, computational efficiency and explanatory capacity. We further introduce the notion of information channels which describe the interaction of the three dimensions. Information channels can be used to explain the effectiveness of individual recommenders or explain the relative contribution of components in the hybrid recommender.
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%U http://dx.doi.org/10.1007/978-3-642-15208-5_1
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%0 
%0 Conference Proceedings
%A Wang, Yonggang; Zhai, Ennan; Hu, Jianbin & Chen, Zhong
%D 2010
%T Claper: Recommend classical papers to beginners
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%B Proceedings of the seventh International Conference on Fuzzy Systems and Knowledge Discovery
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%I IEEE
%V 6
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%P 2777--2781
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%8 August
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%3 inproceedings
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%F wang2010claper
%K analysis, citation, item, paper, recommender, research
%X Classical papers are of great help for beginners to get familiar with a new research area. However, digging them out is a difficult problem. This paper proposes Claper, a novel academic recommendation system based on two proven principles: the Principle of Download Persistence and the Principle of Citation Approaching (we prove them based on real-world datasets). The principle of download persistence indicates that classical papers have few decreasing download frequencies since they were published. The principle of citation approaching indicates that a paper which cites a classical paper is likely to cite citations of that classical paper. Our experimental results based on large-scale real-world datasets illustrate Claper can effectively recommend classical papers of high quality to beginners and thus help them enter their research areas.
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%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5569227
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%0 Journal Article
%A Zhang, Zi-Ke; Zhou, Tao & Zhang, Yi-Cheng
%D 2010
%T Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs
%E 
%B Physica A: Statistical Mechanics and its Applications
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%I 
%V 389
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%N 1
%P 179 - 186
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%@ 0378-4371
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%2 ScienceDirect.com - Physica A: Statistical Mechanics and its Applications - Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs
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%F zhang2010personalized
%K diffusion, item, personalized, recommendation
%X Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this article, we propose a recommendation algorithm based on an integrated diffusion on user–item–tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.
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%U http://www.sciencedirect.com/science/article/pii/S0378437109006839
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%0 
%0 Conference Proceedings
%A Karypis, George
%D 2001
%T Evaluation of Item-Based Top-N Recommendation Algorithms
%E 
%B Proceedings of the Tenth International Conference on Information and Knowledge Management
%C New York, NY, USA
%I ACM
%V 
%6
%N 
%P 247--254
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%S CIKM '01
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%@ 1-58113-436-3
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%2 Evaluation of Item-Based Top-N Recommendation Algorithms
%3 inproceedings
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%F karypis2001evaluation
%K based, item, recommendation
%X The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based Collaborative filtering is the most successful technology for building recommender systems to date, and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers that in typical commercial applications can grow to be several millions. To address these scalability concerns item-based recommendation techniques have been developed that analyze the user-item matrix to identify relations between the different items, and use these relations to compute the list of recommendations.In this paper we present one such class of item-based recommendation algorithms that first determine the similarities between the various items and then used them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on five different datasets show that the proposed item-based algorithms are up to 28 times faster than the traditional user-neighborhood based recommender systems and provide recommendations whose quality is up to 27% better.
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%U http://doi.acm.org/10.1145/502585.502627
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%0 
%0 Conference Proceedings
%A Sarwar, Badrul; Karypis, George; Konstan, Joseph & Riedl, John
%D 2001
%T Item-based collaborative filtering recommendation algorithms
%E 
%B Proceedings of the 10th international conference on World Wide Web
%C New York, NY, USA
%I ACM
%V 
%6
%N 
%P 285--295
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%@ 1-58113-348-0
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%3 inproceedings
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%F sarwar2001itembased
%K cf, collaborative, filtering, item, recommender
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%U http://doi.acm.org/10.1145/371920.372071
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