Discovering Attractive Products based on Influence Sets

TL;DR

This paper introduces novel algorithms for reverse skyline and k-Most Attractive Candidates queries, enabling efficient identification of products with maximum expected buyers based on customer preferences, with demonstrated superior performance over existing methods.

Contribution

The paper presents new algorithms for reverse skyline and k-MAC queries, improving efficiency in identifying attractive products in preference-based datasets.

Findings

Proposed RSA algorithm effectively answers reverse skyline queries.

Batched algorithm outperforms branch-and-bound in k-MAC queries.

Experimental results show superior performance on synthetic and real datasets.

Abstract

Skyline queries have been widely used as a practical tool for multi-criteria decision analysis and for applications involving preference queries. For example, in a typical online retail application, skyline queries can help customers select the most interesting, among a pool of available, products. Recently, reverse skyline queries have been proposed, highlighting the manufacturer's perspective, i.e. how to determine the expected buyers of a given product. In this work we develop novel algorithms for two important classes of queries involving customer preferences. We first propose a novel algorithm, termed as RSA, for answering reverse skyline queries. We then introduce a new type of queries, namely the k-Most Attractive Candidates k-MAC query. In this type of queries, given a set of existing product specifications P, a set of customer preferences C and a set of new candidate products Q, the k-MAC query returns the set of k candidate products from Q that jointly maximizes the total number of expected buyers, measured as the cardinality of the union of individual reverse skyline sets (i.e., influence sets). Applying existing approaches to solve this problem would require calculating the reverse skyline set for each candidate, which is prohibitively expensive for large data sets. We, thus, propose a batched algorithm for this problem and compare its performance against a branch-and-bound variant that we devise. Both of these algorithms use in their core variants of our RSA algorithm. Our experimental study using both synthetic and real data sets demonstrates that our proposed algorithms outperform existing, or naive solutions to our studied classes of queries.