Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model

The industry-scale ranking systems are typically applied to millions of items in a personalized way for billions of users. To We investigate the Plackett-Luce (PL) model meet the need of scalability and to exploit a huge based listwise learning-to-rank (LTR) on amount of user feedback data, learning-to-rank (LTR) data with partitioned preference, where a set has been the most popular paradigm for building the of items are sliced into ordered and disjoint ranking system. Existing LTR approaches can be categorized partitions, but the ranking of items within a into three groups: pointwise (Gey, 1994), pairwise partition is unknown. Given N items with (Burges et al., 2005), and listwise (Cao et al., M partitions, calculating the likelihood of 2007; Taylor et al., 2008) methods. The pointwise and data with partitioned preference under the pairwise LTR methods convert the ranking problem PL model has a time complexity of O(N S!), into regression or classification tasks on single or pairs where S is the maximum size of the top M 1 of items respectively.