Quick Learning Recommendation Systems for Baskets of Goods

    公开(公告)号:US20190318410A1

    公开(公告)日:2019-10-17

    申请号:US15955054

    申请日:2018-04-17

    Abstract: Embodiments provide a recommendation for an additional item in response to receiving a basket of goods determine a type for the basket of goods from a set of basket types, receive a set of additional targeted items as target recommendations and receive a history of received types of baskets of goods. Embodiments iteratively perform a clustering into a plurality of clusters of each of the basket types based on the history of received types of baskets of goods, and preference updating for each of the targeted items into each of the plurality of clusters. The iteratively performing, after a plurality of iterations, outputs a sequence of mappings and a sequence of preference parameters. Embodiments generate a frequency of tabulation of mappings from the sequence of mappings and then generate the recommendation based on the sequence of mappings, the sequence of preference parameters and the frequency of tabulation of mappings.

    Artificial Intelligence Based Room Assignment Optimization System

    公开(公告)号:US20210117873A1

    公开(公告)日:2021-04-22

    申请号:US16736284

    申请日:2020-01-07

    Abstract: Embodiments provide optimized room assignments for a hotel in response to receiving a plurality of hard constraints and soft constraints and receiving reservation preferences and room features. The optimization includes determining a guest satisfaction assignment cost based on the reservation preferences and room features, determining an operational efficiency assignment cost, generating a weighted cost matrix based on the guest satisfaction assignment cost and the operational efficiency assignment cost, and generating preliminary room assignments based on the weighted cost matrix. When the preliminary room assignments are feasible, the preliminary room assignments are the optimized room assignments comprising a feasible selection of elements of the matrix. When the preliminary room assignments are infeasible, embodiments relax one or more constraints and repeat the performing optimization until the preliminary room assignments are feasible.

    Price Optimization System
    3.
    发明申请

    公开(公告)号:US20200342475A1

    公开(公告)日:2020-10-29

    申请号:US16380185

    申请日:2019-04-10

    Abstract: Embodiments determine a price schedule for an item by, for each item, receiving a set of prices for the item, an inventory quantity for the item, a per-segment demand model for the item, and an objective function that is a function of the per-segment demand model and maximizes revenue based at least on a probability of a return of the item and a cost of the return. Embodiments allocate the inventory quantity among a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. Embodiments determine a markdown portion of the price schedule for the item that maximizes the objective function, where the markdown portion assigns a series of prices selected from the set of prices for respective time periods during a clearance season for the item.

Patent Agency Ranking