Discovering Attractive Products based on Influence Sets

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 branchand-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.