METHOD AND APPARATUS FOR ONLINE BIDDING, DEVICE, AND MEDIUM

    公开(公告)号:US20240346494A1

    公开(公告)日:2024-10-17

    申请号:US18755518

    申请日:2024-06-26

    CPC classification number: G06Q20/3825 G06Q30/08

    Abstract: A method and apparatus for online bidding, a device, and a medium is provided according to the embodiments of the present disclosure. According to the method, encrypted information from a verification device of a bidding event is received at a participation device of the bidding event. The participation device transmits verification information associated with a bid of the bidding event to a management device of the bidding event. The participation device receives a first signature and a verification information set from the management device. The verification information set includes at least an association relation between the participation device and the verification information. The first signature is generated by the verification device for the verification information set. The participation device verifies a bidding process of the bidding event based on the first signature, the verification information set and the encrypted information.

    RECOMMENDATION MODEL TRAINING METHOD, ARTICLE RECOMMENDATION METHOD AND SYSTEM, AND RELATED DEVICE

    公开(公告)号:US20240346343A1

    公开(公告)日:2024-10-17

    申请号:US18747287

    申请日:2024-06-18

    CPC classification number: G06N5/022 G06N5/04

    Abstract: The present disclosure relates to a recommendation model training method, an article recommendation method and system, and a related device. The recommendation model training method includes: processing data for training by using a recommendation model to obtain a category-independent representation and a category-dependent representation, wherein the data for training comprises a feature of a user and a feature of an article; processing the category-independent representation and the category-dependent representation respectively by using a discriminator to obtain discrimination results; determining a prediction result according to at least one of the category-independent representation or the category-dependent representation; and training the recommendation model and the discriminator according to training targets comprising the category-independent representation not corresponding to any one of the plurality of categories, the category-dependent representation corresponding to the pre-marked category, and the prediction result matching with the pre-marked recommendation information.

    METHOD, APPARATUS, DEVICE AND MEDIUM FOR MANAGING MODEL BASED ON DISTANCE BETWEEN SAMPLES

    公开(公告)号:US20240346318A1

    公开(公告)日:2024-10-17

    申请号:US18504220

    申请日:2023-11-08

    Inventor: Hao WU Cheng Yang

    CPC classification number: G06N3/084 G06N3/094

    Abstract: A method, apparatus, device, and medium for managing a model based on a distance between samples. In one method, a basic sample for training a contrastive learning model and a plurality of negative samples associated with the basic sample is obtained; a sequence of the plurality of negative samples is generated based on distances between the plurality of negative samples and the basic sample; the sequence of the plurality of negative samples is divided into a first set of negative samples and a second set of negative samples; an update parameter for updating the contrastive learning model is determined based on the basic sample, the first set of negative samples and a first weight of the first set of negative samples, and the second set of negative samples and a second weight of the second set of negative samples, the first weight is greater than the second weight.

    Vocabulary generation for neural machine translation

    公开(公告)号:US12112139B2

    公开(公告)日:2024-10-08

    申请号:US17535365

    申请日:2021-11-24

    CPC classification number: G06F40/58 G06F40/237 G06F40/284

    Abstract: Implementations of the present disclosure relate to methods, devices, and computer program products for generating a destination vocabulary from a source vocabulary. In a method, a group of candidate vocabularies are determined from the source vocabulary based on a corpus, a size of a candidate vocabulary in the group of candidate vocabularies being different from a size of the source vocabulary. A group of marginal scores are obtained for the group of candidate vocabularies, respectively, a marginal score in the group of marginal scores being obtained for the candidate vocabulary based on a corpus entropy of the candidate vocabulary and a size of the candidate vocabulary. The destination vocabulary is selected from the group of candidate vocabularies based on the group of marginal scores. With these implementations, both of the corpus entropy and the vocabulary size are considered in the vocabulary generation, and thus a balance may be achieved therebetween, which may increase the performance of the generated vocabulary.

    QUANTIZATION METHOD AND APPARATUS FOR TEXT FEATURE EXTRACTION MODEL, AND DEVICE AND STORAGE MEDIUM

    公开(公告)号:US20240296283A1

    公开(公告)日:2024-09-05

    申请号:US18259210

    申请日:2021-12-10

    CPC classification number: G06F40/279 G06F18/213

    Abstract: A quantization method and apparatus for a text feature extraction model, and a device and a storage medium. The method includes: in a training process of a text feature extraction model, determining, according to a target quantization parameter, a quantization interval corresponding to the target quantization parameter, where the quantization interval includes a part of floating-point values of the target quantization parameter; constructing a mapping relationship between floating-point values and fixed-point values of the target quantization parameter based on the quantization interval, where a floating-point value smaller than a left end point of the quantization interval—is mapped to a quantized minimum fixed-point value, and a floating-point values larger than a right end point of the quantization interval is mapped to a quantized maximum fixed-point value; and performing a quantization operation on the target quantization parameter based on the mapping relationship.

    METHOD, APPARATUS, DEVICE AND STORAGE MEDIUM FOR DELIVERY COST ESTIMATION

    公开(公告)号:US20240289732A1

    公开(公告)日:2024-08-29

    申请号:US18588735

    申请日:2024-02-27

    CPC classification number: G06Q10/08345 G06Q30/0631

    Abstract: According to embodiments of the present disclosure, method, apparatus, device, and storage medium for delivery cost estimation are provided. The method comprises: extracting first feature information specific to a target recommended content item from related data of the target recommended content item, and the target recommended content item is to be delivered to recommend target resources; at least based on the first feature information, using a trained cost estimation model, determining an expected initial cost in a contending delivery of the target recommended content item; and determining a target initial cost used in the contending delivery of the target recommended content item based on the expected initial cost. According to the solution, the initial cost of each recommended content item during contending delivery can be accurately estimated specifically to improve delivery performance.

    METHOD, APPARATUS, ELECTRONIC DEVICE AND MEDIUM FOR EVENT EXTRACTION

    公开(公告)号:US20240265027A1

    公开(公告)日:2024-08-08

    申请号:US18415327

    申请日:2024-01-17

    CPC classification number: G06F16/254

    Abstract: Embodiments of the present disclosure relate to a method, an apparatus, an electronic device and a medium for event extraction. The method comprises: extracting a plurality of named entities from a document as a plurality of event arguments. The method further includes determining an event type and a template corresponding to the event type in the document. The method also includes filling the plurality of event arguments in respective locations in the template to generate a plurality of candidate event records, and filtering the plurality of candidate event records to obtain one or more target event record. In this way, respective candidate event records are generated through iteration while event extraction is performed at a document level, which can avoid performance fluctuations caused by manual selection of an event role generation sequence, and can also avoid under-fitting brought about by parallel generation to thus improve the event extraction accuracy.

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