Product Ranking on Online Platforms

Derakhshan, Mahsa; Golrezaei, Negin; Manshadi, Vahideh; Mirrokni, Vahab

doi:10.1287/mnsc.2021.4044

Cited by 33 publications

(30 citation statements)

References 37 publications

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“…The products in the consideration set are then thoroughly evaluated. Derakhshan et al (2020) investigate a product ranking problem where the customers' consideration set is built as a function of the search cost. The authors focus on maximizing the market share or welfare.…”

Section: Consumer Choice Models and Assortment Optimizationmentioning

confidence: 99%

Revenue Maximization and Learning in Products Ranking

Chen

Yang

2020

Preprint

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We consider the revenue maximization problem for an online retailer who plans to display a set of products di ering in their prices and qualities and rank them in order. The consumers have random attention spans and view the products sequentially before purchasing a "satis cing" product or leaving the platform emptyhanded when the attention span gets exhausted. Our framework extends the cascade model in two directions: the consumers have random attention spans instead of xed ones and the rm maximizes revenues instead of clicking probabilities. We show a nested structure of the optimal product ranking as a function of the attention span when the attention span is xed and design a 1/𝑒-approximation algorithm accordingly for the random attention spans. When the conditional purchase probabilities are not known and may depend on consumer and product features, we devise an online learning algorithm that achieves Õ ( √ 𝑇 ) regret relative to the approximation algorithm, despite of the censoring of information: the attention span of a customer who purchases an item is not observable. Numerical experiments demonstrate the outstanding performance of the approximation and online learning algorithms.

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Section: Consumer Choice Models and Assortment Optimizationmentioning

confidence: 99%

Revenue Maximization and Learning in Products Ranking

Chen

Yang

2020

Preprint

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“…They find that without a search cost, all products have the same price-a consequence of the MNL assumption. Derakhshan et al (2018) develop a two-stage model with heterogeneous search costs which are increasing in the item's position. Prices are exogenous and consumers follow a process similar in spirit to Weitzman (1979) to form a consideration set.…”

Section: Literature Reviewmentioning

confidence: 99%

Sales Policies for a Virtual Assistant

Mendelson

Zhu

2020

Preprint

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We study the implications of selling through a voice-based virtual assistant. The seller has a set of products available and the virtual assistant dynamically decides which product to offer in each sequential interaction and at what price. The virtual assistant may maximize the seller's profits; it may be altruistic, maximizing total surplus; or it may serve as a consumer agent maximizing the consumer surplus. The consumer is impatient and rational, seeking to maximize her expected utility given the information available to her. The virtual assistant selects products based on the consumer's request and other information available to it (e.g., consumer profile information) and presents them sequentially. Once a product is presented and priced, the consumer evaluates it and decides whether to make a purchase. The consumer's valuation of each product comprises a pre-evaluation value, which is common knowledge to the consumer and the virtual assistant, and a post-evaluation component which is private to the consumer. We solve for the equilibria and develop efficient algorithms for implementing the solution. In the special case where the private information is exponentially distributed, the profit-maximizing total surplus is distributed equally between the consumer and the seller, and the profit-maximizing ranking also maximizes the consumer surplus. We examine the effects of information asymmetry on the outcomes and study how incentive misalignment depends on the distribution of private valuations. We find that monotone rankings are optimal in the cases of a highly patient or impatient consumer and provide a good approximation for other levels of patience. The relationship between products' expected valuations and prices depends on the consumer's patience level and is monotone increasing (decreasing) when the consumer is highly impatient (patient). Also, the seller's share of total surplus decreases in the amount of private information. We compare the virtual assistant to a traditional web-based interface, where multiple products are presented simultaneously on each page. We find that within a page, the higher-value products are priced lower than the lower-value product when the private valuations are exponentially distributed. This is because increasing one product's valuation increases the matching probability of the other products on the same page, which in turn increases their prices of other products. Finally, the web-based interface generally achieves higher profits for the seller than a virtual assistant due to the greater commitment power inherent in its presentation.

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“…Then, the item ranking is of paramount importance, because user attention is usually restricted to the first few items in the sequence (see e.g., [27,13,16,10]). If a user does not spot an item fitting her interests there, she either leaves the service (in case of news or online shopping, see e.g., the empirical evidence in [12]) or settles on a suboptimal action (in case of paper bidding, see e.g., [11]). To mitigate such situations and increase user retention, modern online services highly optimize item rankings based on user scrolling and click patterns.…”

Section: Introductionmentioning

confidence: 99%

On the Approximability of Multistage Min-Sum Set Cover

Fotakis,

Kostopanagiotis,

Nakos

et al. 2021

Preprint

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We investigate the polynomial-time approximability of the multistage version of Min-Sum Set Cover (Mult-MSSC), a natural and intriguing generalization of the classical List Update problem. In Mult-MSSC, we maintain a sequence of permutations (π 0 , π 1 , . . . , π T ) on n elements, based on a sequence of requests R = (R 1 , . . . , R T ). We aim to minimize the total cost of updating π t−1 to π t , quantified by the Kendall tau distance d KT (π t−1 , π t ), plus the total cost of covering each request R t with the current permutation π t , quantified by the position of the first element of R t in π t .Using a reduction from Set Cover, we show that Mult-MSSC does not admit an O(1)-approximation, unless P = NP, and that any o(log n) (resp. o(r)) approximation to Mult-MSSC implies a sublogarithmic (resp. o(r)) approximation to Set Cover (resp. where each element appears at most r times). Our main technical contribution is to show that Mult-MSSC can be approximated in polynomial-time within a factor of O(log 2 n) in general instances, by randomized rounding, and within a factor of O(r 2 ), if all requests have cardinality at most r, by deterministic rounding.

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Product Ranking on Online Platforms

Cited by 33 publications

References 37 publications

Revenue Maximization and Learning in Products Ranking

Revenue Maximization and Learning in Products Ranking

Sales Policies for a Virtual Assistant

On the Approximability of Multistage Min-Sum Set Cover

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