Apache Lucene作为基于内容过滤的推荐系统：3个经验教训
Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned
摘要：
在过去的几年里，我们使用Apache Lucene作为推荐框架，在我们的参考文献管理软件Docear的文学推荐系统。在本文中，我们分享了从Lucene工作中学到的三个经验教训。首先，相关性分数低于0.025的推荐往往比相关性分数高于0.025的推荐具有显著更低的点击率。其次，从Lucene的前50个搜索结果中随机挑选10个推荐，与推荐前10个结果相比，点击率下降了15%。第三，返回的搜索结果的数量往往会预测点击率有多高：当Lucene返回少于1,000个搜索结果时，点击率往往是返回1,000多个结果的一半左右。
英文摘要：
For the past few years, we used Apache Lucene as recommendation frame-work in our scholarly-literature recommender system of the reference-management software Docear. In this paper, we share three lessons learned from our work with Lucene. First, recommendations with relevance scores below 0.025 tend to have significantly lower click-through rates than recommendations with relevance scores above 0.025. Second, by picking ten recommendations randomly from Lucene's top50 search results, click-through rate decreased by 15%, compared to recommending the top10 results. Third, the number of returned search results tend to predict how high click-through rates will be: when Lucene returns less than 1,000 search results, click-through rates tend to be around half as high as if 1,000+ results are returned.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
