• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Processing & Management >Personalized and object-centered tag recommendation methods for Web 2.0 applications
【24h】

Personalized and object-centered tag recommendation methods for Web 2.0 applications

机译：Web 2.0应用程序的个性化和以对象为中心的标签推荐方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Several Web 2.0 applications allow users to assign keywords (or tags) to provide better organization and description of the shared content. Tag recommendation methods may assist users in this task, improving the quality of the available information and, thus, the effectiveness of various tag-based information retrieval services, such as searching, content recommendation and classification. This work addresses the tag recommendation problem from two perspectives. The first perspective, centered at the object, aims at suggesting relevant tags to a target object, jointly exploiting the following three dimensions: (ⅰ) tag cooccurrences, (ⅱ) terms extracted from multiple textual features (e.g., title, description), and (ⅲ) various metrics to estimate tag relevance. The second perspective, centered at both object and user, aims at performing personalized tag recommendation to a target object-user pair, exploiting, in addition to the three aforementioned dimensions, a metric that captures user interests. In particular, we propose new heuristic methods that extend state-of-the-art strategies by including new metrics that estimate how accurately a candidate tag describes the target object. We also exploit three learning-to-rank (L2R) based techniques, namely, RankSVM, Genetic Programming (GP) and Random Forest (RF), for generating ranking functions that exploit multiple metrics as attributes to estimate the relevance of a tag to a given object or object-user pair. We evaluate the proposed methods using data from four popular Web 2.0 applications, namely, Bibsonomy, LastFM, YouTube and YahooVideo. Our new heuristics for object-centered tag recommendation provide improvements in precision over the best state-of-the-art alternative of 12% on average (up to 20% in any single dataset), while our new heuristics for personalized tag recommendation produce average gains in precision of 121% over the baseline. Similar performance gains are also achieved in terms of other metrics, notably recall, Normalized Discounted Cumulative Gain (NDCG) and Mean-Reciprocal Rank (MRR). Further improvements, for both object-centered (up to 23% in precision) and personalized tag recommendation (up to 13% in precision), can also be achieved with our new L2R-based strategies, which are flexible and can be easily extended to exploit other aspects of the tag recommendation problem. Finally, we also quantify the benefits of personalized tag recommendation to provide better descriptions of the target object when compared to object-centered recommendation by focusing only on the relevance of the suggested tags to the object. We find that our best personalized method outperforms the best object-centered strategy, with average gains in precision of 10%.
机译：几个Web 2.0应用程序允许用户分配关键字（或标签）以提供更好的组织和共享内容的描述。标签推荐方法可以帮助用户完成此任务，从而提高可用信息的质量，从而提高各种基于标签的信息检索服务的有效性，例如搜索，内容推荐和分类。这项工作从两个角度解决了标签推荐问题。第一个观点以对象为中心，旨在为目标对象建议相关标签，共同利用以下三个维度：（ⅰ）标签共现，（ⅱ）从多个文本特征（例如标题，描述）中提取的术语，以及（ⅲ）各种评估标签相关性的指标。以对象和用户为中心的第二个观点旨在执行对目标对象-用户对的个性化标签推荐，除了上述三个方面之外，还利用了捕获用户兴趣的度量。特别是，我们提出了新的启发式方法，通过包括估计候选标签描述目标对象的准确度的新指标来扩展最新策略。我们还利用了基于排名学习（L2R）的三种技术，即RankSVM，遗传编程（GP）和随机森林（RF），来生成利用多个度量作为属性来估计标签与标签相关性的排名函数。给定的对象或对象-用户对。我们使用来自四个流行的Web 2.0应用程序，即Bibsonomy，LastFM，YouTube和YahooVideo的数据评估提出的方法。我们的以对象为中心的标签推荐的新启发式方法提供了优于平均水平最佳最佳替代方法（平均12％）（在任何单个数据集中最多达到20％）的精度，而我们的新的个性化标签推荐的启发式方法产生了平均与基准相比，精度提高了121％。在其他指标方面，也可以实现类似的性能提升，特别是召回率，归一化贴现累积增益（NDCG）和均值倒数排名（MRR）。我们新的基于L2R的策略也可以实现以对象为中心（精度高达23％）和个性化标签推荐（精度高达13％）的进一步改进，该策略灵活且可以轻松扩展到利用标签推荐问题的其他方面。最后，我们还通过仅关注建议标签与对象的相关性，量化了个性化标签推荐的好处，以便与以对象为中心的推荐相比提供更好的目标对象描述。我们发现，我们最好的个性化方法优于最佳的以对象为中心的策略，平均精度提高了10％。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号