Abstract
In this chapter, we introduce the pointwise approach to learning to rank. Specifically, we will cover the regression-based algorithms, classification-based algorithms, and ordinal regression-based algorithms, and then make discussions on their advantages and disadvantages.
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Notes
- 1.
One should note that the ranking function used in this work is highly non-linear. This difference may partially account for the performance improvement.
- 2.
Note that there are some algorithms, such as [11, 13], which were also referred to as ordinal regression-based algorithms in the literature. According to our categorization, however, they belong to the pairwise approach since they do not really care about the accurate assignment of a document to one of the ordered categories. Instead, they focus more on the relative order between two documents.
- 3.
- 4.
For the re-ranking scenario, the number of documents to rank for each query may be very similar, e.g., the top 1000 documents per query. However, if we consider all the documents containing the query word, the difference between the number of documents for popular queries and that for tail queries may be very large.
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Liu, TY. (2011). The Pointwise Approach. In: Learning to Rank for Information Retrieval. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14267-3_2
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