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arXiv · 1703.08855

Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned

Stefan Langer·Joeran Beel
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Abstract

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.

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Stefan Langer, Joeran Beel. 2017-03-26. Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned. https://arxiv.org/abs/1703.08855

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