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Conference Papers Year : 2019

A Bi-Objective Approach for Product Recommendations

Abstract

We propose a bi-objective formulation for product recommendations. Our formulation goes beyond traditional recommendations by capturing two conflicting objectives: utility that serves customers' interests, and profit margin, a business-oriented goal. To satisfy the needs of our business partners, we formulate a new problem, namely generating a result containing all sets of k products such that there does not exist any other set of k products that dominates the returned sets, i.e., whose cumulative values for each objective is higher than a set of k products in the result. We study properties of k-Pareto sets that enable us to reduce the number of candidates, as well as the number of dominance tests between candidate sets. We develop a dynamic programming algorithm that leverages those properties to prune the space of solutions. We generalize traditional measures of recommendation accuracy to be applicable to sets of k products. Our experiments on a large set of real customer transactions validate the need for a bi-objective optimization to reconcile customer and business interests, and the scalability of our solution.
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Dates and versions

hal-02972603 , version 1 (08-11-2020)

Identifiers

Cite

Idir Benouaret, Sihem Amer-Yahia, Christiane Kamdem-Kengne, Jalil Chagraoui. A Bi-Objective Approach for Product Recommendations. 2019 IEEE International Conference on Big Data (Big Data), Dec 2019, Los Angeles, France. pp.2159-2168, ⟨10.1109/BigData47090.2019.9006503⟩. ⟨hal-02972603⟩
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