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Incorporating heterogeneous information for personalized tag recommendation in social tagging systems

Published: 12 August 2012 Publication History

Abstract

A social tagging system provides users an effective way to collaboratively annotate and organize items with their own tags. A social tagging system contains heterogeneous information like users' tagging behaviors, social networks, tag semantics and item profiles. All the heterogeneous information helps alleviate the cold start problem due to data sparsity. In this paper, we model a social tagging system as a multi-type graph. To learn the weights of different types of nodes and edges, we propose an optimization framework, called OptRank. OptRank can be characterized as follows:(1) Edges and nodes are represented by features. Different types of edges and nodes have different set of features. (2) OptRank learns the best feature weights by maximizing the average AUC (Area Under the ROC Curve) of the tag recommender. We conducted experiments on two publicly available datasets, i.e., Delicious and Last.fm. Experimental results show that: (1) OptRank outperforms the existing graph based methods when only (user, tag, item) relation is available. (2) OptRank successfully improves the results by incorporating social network, tag semantics and item profiles.

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        cover image ACM Conferences
        KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
        August 2012
        1616 pages
        ISBN:9781450314626
        DOI:10.1145/2339530
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 12 August 2012

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        1. recommender system
        2. social tagging system

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        • (2024)A Method for Constructing a Cross-Media Heterogeneous Information Network2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN)10.1109/CICN63059.2024.10847528(1310-1316)Online publication date: 22-Dec-2024
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