Novelty and Diversity in Top-N Recommendation - Analysis and Evaluation
Novelty and Diversity in Top-N Recommendation - Analysis and Evaluation
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DOI：
10.1145/1944339.1944341
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发表时间：
2011-03-01
影响因子：
5.3
通讯作者：
Zhang, Mi
中科院分区：
文献类型：
--
作者：
Hurley, Neil;Zhang, Mi
对于主要基于项目和用户查询之间的相似性度量来进行产品排名的推荐系统，经常会发生推荐列表上的产品彼此高度相似并且缺乏多样性的情况。在这篇文章中，我们认为，多样性研究的动机是增加检索不寻常的或新颖的项目，这是相关的用户的概率，并介绍了一种方法来评估其性能方面的新颖项目检索。此外，注意到一组匹配用户查询的项目的检索是一个常见的问题，在许多应用程序的信息检索，我们制定的多样性和匹配质量之间的权衡作为一个二进制优化问题，与输入控制参数允许明确调整这种权衡。我们研究的最优化问题的解决方案策略，并证明在获得所需的系统性能的控制参数的重要性。该方法进行了评估的协作推荐使用两个数据集和基于案例的建议，使用一个合成的数据集构建从公共领域的旅游数据集。
For recommender systems that base their product rankings primarily on a measure of similarity between items and the user query, it can often happen that products on the recommendation list are highly similar to each other and lack diversity. In this article we argue that the motivation of diversity research is to increase the probability of retrieving unusual or novel items which are relevant to the user and introduce a methodology to evaluate their performance in terms of novel item retrieval. Moreover, noting that the retrieval of a set of items matching a user query is a common problem across many applications of information retrieval, we formulate the trade-off between diversity and matching quality as a binary optimization problem, with an input control parameter allowing explicit tuning of this trade-off. We study solution strategies to the optimization problem and demonstrate the importance of the control parameter in obtaining desired system performance. The methods are evaluated for collaborative recommendation using two datasets and case-based recommendation using a synthetic dataset constructed from the public-domain Travel dataset.