A Bi-Objective Approach for Product Recommendations

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 partner...

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Bibliographic Details
Published in2019 IEEE International Conference on Big Data (Big Data) pp. 2159 - 2168
Main Authors Benouaret, Idir, Amer-Yahia, Sihem, Kamdem-Kengne, Christiane, Chagraoui, Jalil
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Bi-Objective optimization
Big Data
Companies
Dynamic programming
Heuristic algorithms
History
Optimization
Recommendation Customer Margin
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Summary: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.
DOI:10.1109/BigData47090.2019.9006503