Walmart Apollo, LLC

Changzheng Liu

Wei Shen

Lu Wang

Anurag Das

Georgios Rovatsos

Qianqian Zhang

Guangwei Yang

Changfu Li

Ahinoam Pollack

Saeed Ahmadian

Boning Zhang

Sandip Bhattacharjee

Jing Lu

Aman Avilash

Systems and methods for automatically computing biddings for cold items are disclosed. A first bid value is computed and submitted for a first bidding for a first set of items each having a predicted sales metric larger than a first threshold in a first time period. From the first set of items, a second set of items are selected that have a traffic metric smaller than a second threshold in a second time period. Each item in the second set is assigned to one of a plurality of clusters. A second bid value is computed for each cluster based on cluster-level metrics and a click distribution among the plurality of clusters. An updated bid value is computed, for each item in the second set, to be submitted for a second bidding, based on the second bid value for a cluster to which the item is assigned.

System and method for automatically computing and updating advertisement biddings for cold items

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: tracking touchpoints by a user over a first time period; after receiving an order, determining, using a machine-learning model, a respective contribution of each of the touchpoints, wherein the machine-learning model is trained to predict a probability of the user placing the order during a second time period based on an input feature vector representing a set of touchpoints; and allocating a respective percentage of credit for the order to the each of the touchpoints based on the respective contributions of the each of the touchpoints. Other embodiments are disclosed.

Perceived value attribution model

A method including obtaining historical revenue per click (RPC) data. The method also can include generating hourly RPC prediction data for a predetermined time period based on the historical RPC data. The method additionally can include determining (i) time intervals from within the predetermined time period and (ii) a respective modifier for each of the time intervals, based on the hourly RPC prediction data. The acts method can include uploading the time intervals and the respective modifiers for the time intervals to a dayparting system of a search engine. Other embodiments are described.

Real-time dayparting management

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving one or more objectives and one or more constraints from a user electronic device of a user; generating a combined objective using the one or more objectives; calculating, using the combined objective, a return per click for an advertisement campaign; determining one or more bids based on (a) the return per click for the advertisement campaign and (b) a return on advertising spend for the advertisement campaign; estimating, using a first predictive algorithm and the one or more bids, an average cost per click; estimating an expected number of clicks for the advertisement campaign based on the average cost per click; determining a total amount used of the one or more constraints for the one or more bids based on the expected number of clicks for the advertisement campaign; and when the total amount used of the one or more constraints for the one or more bids exceeds at least one of the one or more constraints, lowering the one or more bids. Other embodiments are disclosed herein.

Systems and methods for training of multi-objective machine learning algorithms

Growing community of inventors

Sunnyvale, CA, United States of America

Changzheng Liu