Product ranking is the core problem for revenue-maximizing online retailers. To design proper product ranking algorithms, various consumer choice models are proposed to characterize the consumers' behaviors when they are provided with a list of products. However, existing works assume that each consumer purchases at most one product or will keep viewing the product list after purchasing a product, which does not agree with the common practice in real scenarios. In this paper, we assume that each consumer can purchase multiple products at will. To model consumers' willingness to view and purchase, we set a random attention span and purchase budget, which determines the maximal amount of products that he/she views and purchases, respectively. Under this setting, we first design an optimal ranking policy when the online retailer can precisely model consumers' behaviors. Based on the policy, we further develop the Multiple-Purchase-with-Budget UCB (MPB-UCB) algorithms with $\tilde{O}(\sqrt{T})$ regret that estimate consumers' behaviors and maximize revenue simultaneously in online settings. Experiments on both synthetic and semi-synthetic datasets prove the effectiveness of the proposed algorithms.

Product ranking is the core problem for revenue-maximizing online retailers.

Product ranking is the core problem for revenue-maximizing online retailers.

Product ranking is the core problem for revenue-maximizing online retailers.

Product ranking is the core problem for revenue-maximizing online retailers. To design proper product ranking algorithms, various consumer choice models are proposed to characterize the consumers' behaviors when they are provided with a list of products. However, existing works assume that each consumer purchases at most one product or will keep viewing the product list after purchasing a product, which does not agree with the common practice in real scenarios. In this paper, we assume that each consumer can purchase multiple products at will. To model consumers' willingness to view and purchase, we set a random attention span and purchase budget, which determines the maximal amount of products that he/she views and purchases, respectively.

"This is a picket line," says Rachel Fagan emphatically. The GMB union's Midlands regional organiser stands in front of a line of striking workers several rows deep at Amazon's vast BHX4 warehouse in Coventry, during industrial action designed to embarrass the online behemoth during a high-profile sales event. About 900 workers at the Coventry warehouse are taking three days of strike action from 11 July to 13 July, coinciding with its Prime Day sales event on Tuesday and Wednesday. Along the picket line, one worker holds up a placard carrying the union's familiar refrain: "I am not a robot." The latest industrial action will bring the total strike days at Amazon to 22 since January, when the first UK strikes in the history of the company took place.

Product ranking is the core problem for revenue-maximizing online retailers. To design proper product ranking algorithms, various consumer choice models are proposed to characterize the consumers' behaviors when they are provided with a list of products. However, existing works assume that each consumer purchases at most one product or will keep viewing the product list after purchasing a product, which does not agree with the common practice in real scenarios. In this paper, we assume that each consumer can purchase multiple products at will. To model consumers' willingness to view and purchase, we set a random attention span and purchase budget, which determines the maximal amount of products that he/she views and purchases, respectively. Under this setting, we first design an optimal ranking policy when the online retailer can precisely model consumers' behaviors. Based on the policy, we further develop the Multiple-Purchase-with-Budget UCB (MPB-UCB) algorithms with $\~O(\sqrt{T})$ regret that estimate consumers' behaviors and maximize revenue simultaneously in online settings. Experiments on both synthetic and semi-synthetic datasets prove the effectiveness of the proposed algorithms.

According to a confidential internal document viewed by Recode, Amazon has been working on an ... [ ] Automated Applicant Evaluation system that will determine which job applicants possess the most potential for success. Instead of humans reading your résumé, artificial intelligence technology is equipped to do the job. But can it do it well? Last week, Amazon offered buyouts to its recruiters and could look to replace them with artificial technology software. This is in addition to the projected thousands of people who will be let go from the giant online retailer.

Almost everyone has some kind of contact with artificial intelligence on a daily basis. From using the face recognition function to unlock your cell phone to asking Alexa to play your favorite song. So, whether they know it or not, most consumers are already interacting with artificial intelligence and enjoying its benefits every day. As so many people only have a vague understanding of what artificial intelligence is, it is little surprise to know that its potential goes unrecognized by the public at large. But, one area where the power of artificial intelligence is undoubtedly getting the attention it deserves is the eCommerce industry.