Risk Aware Ranking for Top-$k$ Recommendations

TL;DR

This paper introduces a risk-aware ranking algorithm for top-$k$ recommendations that considers payoff uncertainties, improving recommendation quality by using a risk-seeking utility function after preference scores are learned.

Contribution

It proposes a novel, efficient ranking method that incorporates payoff uncertainty and demonstrates the effectiveness of risk-seeking utility functions in ranking performance.

Findings

Risk-aware ranking improves recommendation quality.

Using risk-seeking utility functions yields better top-$k$ rankings.

The proposed algorithm is computationally efficient.

Abstract

Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items. In practical settings, a ranked list of top-$k$ items from the estimated preference order is recommended to the end user in the decreasing order of preference for final consumption. We analyze this model and observe that such a ranking model results in suboptimal performance when the payoff associated with the recommended items is different. We propose a novel and very efficient algorithm for the preference ranking considering the uncertainty regarding the payoffs of the items. Once the preference scores for the users are obtained using any preference learning algorithm, we show that ranking the items using a risk seeking utility function results in the best ranking performance.