Number of co-authors:20
Number of publications with 3 favourite co-authors:Li Ding:3Timothy W. Finin:3Anupam Joshi:2
Rong Pan's 3 most productive colleagues in number of publications:Qiang Yang:34Timothy W. Finin:24Anupam Joshi:20
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Publications by Rong Pan (bibliography)
Huang, Yu-Jia, Xiang, Evan Wei and Pan, Rong (2012): Constrained collective matrix factorization. In: Proceedings of the 2012 ACM Conference on Recommender Systems 2012. pp. 237-240.
Transfer learning for collaborative filtering (TLCF) aims to solve the sparsity problem by transferring rating knowledge across multiple domains. Taking domain difference into account, one of the issues in cross-domain collaborative filtering is to selectively transfer knowledge from source/auxiliary domains. In particular, this paper addresses the problem of inconstant users (users with changeable preferences across different domains) when transferring knowledge about users from another auxiliary domain. We first formulate the problem of inconstant users caused by domain difference and then propose a new model that performs constrained collective matrix factorization (CCMF). Our experiments on simulated and real data show that CCMF has superior performance than other methods.
© All rights reserved Huang et al. and/or ACM Press
Yu, Le, Pan, Rong and Li, Zhangfeng (2011): Adaptive social similarities for recommender systems. In: Proceedings of the 2011 ACM Conference on Recommender Systems 2011. pp. 257-260.
Collaborative filtering (CF) is an effective recommendation technique, which selects items for an individual user based on similar users' preferences. However, CF may not fully reflect the procedure how people choose an item in real life, for users are more likely to ask friends for opinions instead of asking similar strangers. Recently, some recommendation methods based on social network have been raised. These approaches incorporate social network into the CF algorithms and users' preferences can be influenced by the favors of their friends. These social approaches require the knowledge of similarities among friends. There are two popular similarity functions: Vector Space Similarity (VSS) and Pearson Correlation Coefficient (PCC). However, both friends similarity functions are based on the item-sets they rated in common. In most cases, these functions are impractical, i.e. if two friends do not share the same items in common, the similarity between them will be zeros. To solve this problem, we propose an Adaptive Social Similarity (ASS) function based on the matrix factorization technique. We conduct our experiment on a large dataset: Epinions, which is a widely-used dataset with social information. The experiment results illustrate that our approach outperforms the baseline models and achieves a better performance than social-based method in .
© All rights reserved Yu et al. and/or ACM Press
Shen, Dou, Pan, Rong, Sun, Jian-Tao, Pan, Jeffrey Junfeng, Wu, Kangheng, Yin, Jie and Yang, Qiang (2006): Query enrichment for web-query classification. In ACM Transactions on Information Systems, 24 (3) pp. 320-352.
Web-search queries are typically short and ambiguous. To classify these queries into certain target categories is a difficult but important problem. In this article, we present a new technique called query enrichment, which takes a short query and maps it to intermediate objects. Based on the collected intermediate objects, the query is then mapped to target categories. To build the necessary mapping functions, we use an ensemble of search engines to produce an enrichment of the queries. Our technique was applied to the ACM Knowledge Discovery and Data Mining competition (ACM KDDCUP) in 2005, where we won the championship on all three evaluation metrics (precision, F1 measure, which combines precision and recall, and creativity, which is judged by the organizers) among a total of 33 teams worldwide. In this article, we show that, despite the difficulty of an abundance of ambiguous queries and lack of training data, our query-enrichment technique can solve the problem satisfactorily through a two-phase classification framework. We present a detailed description of our algorithm and experimental evaluation. Our best
© All rights reserved Shen et al. and/or ACM Press
Ding, Li, Finin, Timothy W., Joshi, Anupam, Peng, Yun, Pan, Rong and Reddivari, Pavan (2005): Search on the Semantic Web. In IEEE Computer, 38 (10) pp. 62-69.
Ding, Li, Finin, Timothy W., Joshi, Anupam, Pan, Rong, Cost, R. Scott, Peng, Yun, Reddivari, Pavan, Doshi, Vishal and Sachs, Joel (2004): Swoogle: a search and metadata engine for the semantic web. In: Grossman, David A., Gravano, Luis, Zhai, Chengxiang, Herzog, Otthein and Evans, David A. (eds.) Proceedings of the 2004 ACM CIKM International Conference on Information and Knowledge Management November 8-13, 2004, Washington, DC, USA. pp. 652-659.
Zou, Youyong, Finin, Timothy W., Ding, Li, Chen, Harry and Pan, Rong (2003): Using semantic web technology in multi-agent systems: a case study in the TAGA trading agent environment. In: Sadeh, Norman M., Dively, Mary Jo, Kauffman, Robert J., Labrou, Yannis, Shehory, Onn, Telang, Rahul and Cranor, Lorrie Faith (eds.) Proceedings of the 5th International Conference on Electronic Commerce - ICEC 2003 September 30 - October 03, 2003, Pittsburgh, Pennsylvania, USA. pp. 95-101.
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