Learning to Rank an Assortment of Products

Ferreira, Kris Johnson; Parthasarathy, Sunanda; Sekar, Shreyas

doi:10.2139/ssrn.3395992

Cited by 15 publications

(25 citation statements)

References 8 publications

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“…Putting all these pieces together allows us to provide an optimal (1 − 1/e)-approximation algorithm for the problem of maximizing user engagement -an unconstrained sequential submodular maximization problem. We note that this generalizes the result of Ferreira et al (2019) who provide a 1/2-approximation ratio for the special case of this problem where the underlying submodular functions are coverage functions.…”

Section: Introductionsupporting

confidence: 83%

“…As we mentioned earlier, no policy can achieve a better than (1 − 1/e)-approximation even when f i (·)'s are coverage functions. Coverage functions have appeared in special cases of our problem studied in previous work (e.g., Ferreira et al (2019)). For such functions, it is possible to get a (1 − 1/e)-approximation using an LP-based approach which might be of independent interest.…”

Section: Algorithm 2 Optimal Approximation Algorithm For Maximizing Umentioning

confidence: 98%

“…A work closely related to ours is that of Ferreira et al (2019) who, in the context of online retailing, consider the problem of ranking assortments with the objective of maximizing engagement. This problem is a special case of sequential submodular optimization, where the submodular functions of interest are all coverage functions.…”

Section: Related Workmentioning

confidence: 99%

“…This problem is a special case of sequential submodular optimization, where the submodular functions of interest are all coverage functions. Ferreira et al (2019) provide a 0.5-approximation greedy algorithm for this problem (while showing a (1−1/e)-approximation is admissible under the assumption that click probabilities and patience levels are independent). They eventually feed their algorithm to an online "learning-then-earning" algorithm which poses the interesting question of whether or not our algorithm can be turned into an online learning algorithm as well.…”

Section: Related Workmentioning

confidence: 99%

“…Some online platforms mainly focus on maximizing their market share through improving user engagement. One such example is Wayfair (www.wayfair.com), a multi-billion dollar online retailer of home goods (Ferreira et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Ranking an Assortment of Products Via Sequential Submodular Optimization

et al. 2020

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We study an optimization problem capturing a core operational question for online retailing platforms.Given models for the users' preferences over products as well as the number of items they are willing to observe before clicking on one or abandoning the search, what is the best way to rank the relevant products in response to a search query?In order to capture both popularity and diversity effects, we model the probability that a user clicks on an element from a subset of products as a monotone submodular function of this set. We also assume that the patience level of the users, or the number of items they are willing to observe before clicking on one or abandoning the search, is a given random variable. Under those assumptions, the objective functions capturing user engagement or platform revenue can be written as a new family of submodular optimization problems over a sequence of elements.We call this family of natural optimization problems sequential submodular optimization. By building on and extending the literature on submodular maximization subject to matroid constraints, we derive a 1 − 1/e optimal approximation algorithm for maximizing user engagement and a bi-criteria approximation algorithm for maximizing revenue subject to a lower bound on user engagement.

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Section: Introductionsupporting

confidence: 83%

Section: Algorithm 2 Optimal Approximation Algorithm For Maximizing Umentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ranking an Assortment of Products Via Sequential Submodular Optimization

et al. 2020

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Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz

Tang

Yuan

2021

Production and Operations Management

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Personality quiz is a powerful tool that enables costumer segmentation by actively asking them questions, and marketers are using it as an effective method of generating leads and increasing e‐commerce sales. We study the problem of how to select and sequence a group of quiz questions so as to optimize the quality of customer segmentation. We assume that the customer will sequentially scan the list of questions. After reading a question, the customer makes two, possibly correlated, random decisions: (i) she first decides whether to answer this question or not, and then (ii) decides whether to continue reading the next question or not. We further assume that the utility of questions that have been answered can be captured by a monotone and submodular function. In general, our problem falls into the category of non‐adaptive active learning‐based customer profiling. Note that under our model, the probability of a question being answered depends on the location of that question, as well as the set of other questions placed ahead of that question, this makes our problem fundamentally different from existing studies on submodular optimization. We develop a series of question selection and sequencing strategies with provable performance bound. Although we focus on the application of quiz design in this study, our results apply to a broad range of applications, including assortment optimization with position bias effect.

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Developing operations management data analytics

Feng

Shanthikumar

2022

Production and Operations Management

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In this article, we describe representative contributions in several major application areas of data analytics in operations management to summarize the recent development, discuss the common themes, identify the current trends, and speculate the future directions. Certainly, many important contributions have been made in various application areas that are either directly or indirectly related to data analytics, and there are important theoretical developments made by scholars in our field. It is not our intention to provide a complete survey for data‐analytics work in our field. Instead, we focus only on the aspect of data integration in operational decision‐making by describing the most popular applications of data analytics.

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Learning to Rank an Assortment of Products

Cited by 15 publications

References 8 publications

Ranking an Assortment of Products Via Sequential Submodular Optimization

Ranking an Assortment of Products Via Sequential Submodular Optimization

Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz

Developing operations management data analytics

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