Reverse Regret Query

Reverse operators have lately gained much attention within the realm of multi-criteria decision-making. While forward operators, such as skyline, seek to identify products that may interest a customer, reverse operators identify prospective customers who are likely to be attracted to a particular product. Specifically, for each customer, they assign scores to all products w.r.t. the customer's preference and then rank the products based on these scores. If the particular product ranks high, the customer is considered a prospective customer for that product. However, relying purely on rankings might cause misleading results, as rankings emphasize the products' relative positions without accounting for their score differences. In a competitive market, a comparatively low-ranked product may have a score that is nearly indistinguishable from that of the top-tier product(s), and thus, may still be interesting to the customer. In this paper, we directly utilize scores to evaluate products, enabling more accurate identification of prospective customers. We refer to our problem as the reverse regret query (RRQ) and make several contributions. First, for the special case in which each product is described by two attributes, we propose an algorithm Sweeping that only takes linear time. Second, for the general case in which each product can be described by multiple attributes, we present two algorithms: an exact algorithm E-PT and a faster approximate algorithm A-PC. We conducted experiments on synthetic and real datasets. The results confirm that evaluating products via scores provides a sound and insightful way of identifying prospective customers. Under typical settings, our proposed algorithms execute faster than existing ones by 1–3 orders of magnitude.