Incremental preference adjustment: a graph-theoretical approach
Liangjun Song, Junhao Gan, Zhifeng Bao, Boyu Ruan, H Jagadish, Timos Sellis
The VLDB Journal | Springer | Published : 2020
Learning users’ preferences is critical to personalized search and recommendation. Most such systems depend on lists of items rank-ordered according to the user’s preference. Ideally, we want the system to adjust its estimate of users’ preferences after every interaction, thereby becoming progressively better at giving the user what she wants. We also want these adjustments to be gradual and explainable, so that the user is not surprised by wild swings in system rank ordering. In this paper, we support a rank-reversal operation on two items x and y for users: adjust the user’s preference such that the personalized rank of x and y is reversed. We emphasize that this problem is orthogo..View full abstract
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Awarded by ARC
This work is supported in part by ARC under Grants DP200102611, DP180102050, DE190101118, and a Google Faculty Award.