Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for carpool dual-pricing in ridesharing are provided. An exemplary method comprises: determining an expected trip count based on a plurality of carpool requests in a pricing unit and a pair of price adjustment multipliers applied to the pricing unit; for each of the plurality of carpool requests, generating a carpool matching probability of the carpool request by a second machine learning model based on the first expected trip count; constructing one or more Key Performance Indicator (KPI) models based on the plurality of carpool matching probabilities and the pair of price adjustment multipliers; and determining optimal values of the pair of price adjustment multipliers based on an optimization model maximizing an aggregated value of the one or more KPI models.

Abstract: Techniques are presented herein for improved search based on group relevance. The techniques include determining account node groups based on the interactions of accounts with content. When a search query is received from a particular account, the group(s) associated with that account are determined, and the content items determined to be relevant to other accounts in the group(s) are given higher relevance in the search results. In some embodiments, groups are determined using partitioning and/or queries can be rewritten based on the group(s).

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for carpool dual-pricing in ridesharing are provided. An exemplary method comprises: obtaining a machine-learning model trained to generate a probability for a carpool request from a rider to convert to a trip based at least on a pair of price adjustment multipliers applied to the carpool request; determining one or more Key Performance Indicator (KPI) models based on the machine-learning model; constructing an optimization model based on the KPI models, the optimization model comprising a plurality of pairs of price adjustment multipliers as decision variables; and determining optimal values of the plurality of pairs of price adjustment multipliers for the plurality of pricing units by solving the optimization model based on a plurality of historical carpool requests.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for carpool dual-pricing in ridesharing are provided. An exemplary method comprises: obtaining a machine-learning model trained to generate a probability for a carpool request from a rider to convert to a trip based at least on a pair of price adjustment multipliers applied to the carpool request; determining one or more Key Performance Indicator (KPI) models based on the machine-learning model; constructing an optimization model based on the KPI models, the optimization model comprising a plurality of pairs of price adjustment multipliers as decision variables; and determining optimal values of the plurality of pairs of price adjustment multipliers for the plurality of pricing units by solving the optimization model based on a plurality of historical carpool requests.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for carpool dual-pricing in ridesharing are provided. An exemplary method comprises: determining an expected trip count based on a plurality of carpool requests in a pricing unit and a pair of price adjustment multipliers applied to the pricing unit; for each of the plurality of carpool requests, generating a carpool matching probability of the carpool request by a second machine learning model based on the first expected trip count; constructing one or more Key Performance Indicator (KPI) models based on the plurality of carpool matching probabilities and the pair of price adjustment multipliers; and determining optimal values of the pair of price adjustment multipliers based on an optimization model maximizing an aggregated value of the one or more KPI models.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining price multipliers in a ride-hailing platform are described. An exemplary method may comprise: obtaining a price multiplier that has been applied in a pricing unit of the ride-hailing platform during a previous period of time and a key performance indicator (KPI) value of the pricing unit during the previous period of time; constructing a hash key; updating a hash table based on the KPI value and the hash key; determining whether to perform exploration or exploitation for a current period of time; when it is determined to perform exploration, selecting a new price multiplier; and when it is determined to perform exploitation: determining the new price multiplier based on one or more entries in the hash table to apply to the pricing unit for the current period of time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for joint optimization of price multipliers and coupons in a ride-hailing platform are disclosed. An exemplary method comprises: obtaining a plurality of spatial-temporal pricing units and a plurality of rider segments; constructing a plurality of key performance indicator (KPI) metrics, wherein each of the KPI metrics takes input comprising (1) the price adjustment multiplier applied to a given trip and (2) the rider coupon applied to a rider of the given trip, and generates output comprising a KPI value of the given trip; constructing an optimization problem that maximizes a weighted sum of the plurality of KPI metrics with a vector of price-adjustment-multiplier decision variables and a matrix of rider-coupon decision variables; and determining, by solving the optimization problem, a plurality of optimal price adjustment multipliers and a plurality of optimal rider coupons.

Abstract: Techniques are presented herein for improved automatic suggestions based on group relevance. The techniques include determining groups of account based on the interactions of those accounts with content items, e.g., on a federated system, and ranking tokens extracted from those content items based at least in part on one or more factors. When a partial input is received from a particular account, tokens are ranked based on groups associated with the account and are presented in the user interface.

Abstract: Techniques are presented herein for improved automatic suggestions based on group relevance. The techniques include determining groups of account based on the interactions of those accounts with content items, e.g., on a federated system, and ranking tokens extracted from those content items based at least in part on one or more factors. When a partial input is received from a particular account, tokens are ranked based on groups associated with the account and are presented in the user interface.

Abstract: Techniques are presented herein for improved search based on group relevance. The techniques include determining account node groups based on the interactions of accounts with content. When a search query is received from a particular account, the group(s) associated with that account are determined, and the content items determined to be relevant to other accounts in the group(s) are given higher relevance in the search results. In some embodiments, groups are determined using partitioning and/or queries can be rewritten based on the group(s).

Abstract: Systems and methods for optimizing power flows using a harmony search, including decoupling phases in a multi-phase power generation system into individual phase agents in a multi-phase power flow model for separately controlling at least one of phase variables or parameters. One or more harmony segments from harmony memory are ranked and selected based on a utility value determined for each of the decoupled phases. A harmony search with gradient descent learning is performed to move the selected harmony segments to a better local neighborhood. A new utility value for each of the selected segments is determined based on historical performance, and the harmony memory is iteratively updated if one or more of the new utility values are higher than a utility value of a worst harmony segment stored in the harmony memory.

Abstract: Systems and methods for optimizing power flows using a harmony search, including decoupling phases in a multi-phase power generation system into individual phase agents in a multi-phase power flow model for separately controlling at least one of phase variables or parameters. One or more harmony segments from harmony memory are ranked and selected based on a utility value determined for each of the decoupled phases. A harmony search with gradient descent learning is performed to move the selected harmony segments to a better local neighborhood. A new utility value for each of the selected segments is determined based on historical performance, and the harmony memory is iteratively updated if one or more of the new utility values are higher than a utility value of a worst harmony segment stored in the harmony memory.

Abstract: Systems and methods are disclosed to method to determine capacity planning under environmental constraints by generating an integrated economic planning model for a decision maker, a utility company and one or more microgrids; maximizing the microgrids' total profit and minimizing the electricity price designed by the utility company; generating proactive strategies for the utility company to comply with the environmental policies, and planning strategies for microgrids to help utility company abate environmental mission; and applying different characteristics of microgrids to invest in different portfolio of renewable energy through gaming simulations.

Abstract: Systems and methods are disclosed to method to determine capacity planning under environmental constraints by generating an integrated economic planning model for a decision maker, a utility company and one or more microgrids; maximizing the microgrids' total profit and minimizing the electricity price designed by the utility company; generating proactive strategies for the utility company to comply with the environmental policies, and planning strategies for microgrids to help utility company abate environmental mission; and applying different characteristics of microgrids to invest in different portfolio of renewable energy through gaming simulations.