Item recommendation in collaborative tagging systems via heuristic data fusion
Item recommendation in collaborative tagging systems via heuristic data fusion
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DOI：
10.1016/j.knosys.2014.11.026
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
Hao Wu;Yijian Pei;Bo Li;Zongzhan Kang;Xiaoxin Liu;Hao Li
中科院分区：
文献类型：
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作者：
Hao Wu;Yijian Pei;Bo Li;Zongzhan Kang;Xiaoxin Liu;Hao Li
协同标记系统在Web上已经很流行。然而，信息过载导致越来越多的用户需要推荐服务，因此项目推荐已成为此类系统中的关键问题之一。在本文中，我们研究数据融合是否有助于提高这些系统中的项目推荐的有效性。为此，我们首先总结了国家的最先进的推荐方法，根据其算法原理分为几类。然后，我们实验了大约40个推荐组件对数据集从三个社会标签系统-美味，Lastfm和CiteULike。在此基础上，采用基于等级和基于得分的启发式数据融合模型对选定的部件进行联合收割机组合。本文还提出了一种混合线性组合（HLC）的项目推荐融合模型.我们使用四种评价指标，分别考虑准确性，内部的多样性，多样性和新奇，系统地评估质量的各种组件或融合模型获得的建议。根据实验结果，如果单独的组件可以建议相似的相关项目集，但推荐不同的非相关项目集，则组合来自单独组件的证据可以导致推荐准确性的性能改进，而很少或没有损失推荐多样性和新奇。特别是，融合推荐集形成的不同组合的配置文件表示和相似性功能，在基于用户和基于项目的协同过滤，可以显着提高推荐精度。此外，本文还得到了一些有益的发现：（1）使用标签来表示用户或项目的特征可能不如使用项目来描述用户或使用用户来描述项目，但是在主题模型和随机游走中使用标签可以显著提高推荐的准确性、多样性和新奇;（ii）基于用户的协同过滤方法、基于项目的协同过滤方法和随机游走方法对于社会标签系统中的项目推荐任务具有较好的鲁棒性，可以作为数据融合过程的基本组成部分;与传统的数据融合模型相比，该方法具有更好的灵活性和鲁棒性。
Collaborative tagging systems have been popular on the Web. However, information overload results in the increasing need for recommender services from users, and thus item recommendation has been one of the key issues in such systems. In this paper, we examine if data fusion can be helpful for improving effectiveness of item recommendation in these systems. For this, we first summarize the state-of-the-art recommendation methods which are classified into several categories according to their algorithmic principles. Then, we experiment with about 40 recommending components against the datasets from three social tagging systems-Delicious, Lastfm and CiteULike. Based on these, several heuristic data fusion models including rank-based and score-based are used to combine selected components. We also put forward a hybrid linear combination (HLC) model for fusing item recommendation. We use four kinds of evaluation metrics, which respectively consider accuracy, inner-diversity, inter-diversity and novelty, to systematically assess quality of recommendations obtained by various components or fusion models. Depending on experimental results, combining evidence from separate components can lead to performance improvement in the accuracy of recommendations, with a little or without loss of recommendation diversity and novelty, if separate components can suggest similar sets of relevant items but recommend different sets of non-relevant items. Particularly, fusing recommendation sets formed from different combinations of profile representations and similarity functions in user-based and item-based collaborative filtering can significantly improve recommendation accuracy. In addition, some other useful findings are also drawn: (i) Using the tag to represent users profiles or items profiles maybe not as good as profiling users with the item or profiling items with the user, however, exploiting tags in the topic models and random walks can notably improve the accuracy, diversity and novelty of recommendations; (ii) Generally, user-based collaborative filtering, item-based collaborative filtering and random walks methods are robust for the task of item recommendation in social tagging systems, thus can be chosen as the basic components of data fusion process; and (iii) The proposed method (HLC) is more flexible and robust than traditional data fusion models.