WO2018014764A1 - Procédé et dispositif pour sélectionner un objet de produit, et déterminer un modèle et une popularité d'utilisation - Google Patents

Procédé et dispositif pour sélectionner un objet de produit, et déterminer un modèle et une popularité d'utilisation Download PDF

Info

Publication number
WO2018014764A1
WO2018014764A1 PCT/CN2017/092588 CN2017092588W WO2018014764A1 WO 2018014764 A1 WO2018014764 A1 WO 2018014764A1 CN 2017092588 W CN2017092588 W CN 2017092588W WO 2018014764 A1 WO2018014764 A1 WO 2018014764A1
Authority
WO
WIPO (PCT)
Prior art keywords
time period
category
recognition model
heat
commodity
Prior art date
Application number
PCT/CN2017/092588
Other languages
English (en)
Chinese (zh)
Inventor
叶舟
王瑜
陈凡
杨洋
董昭萍
钱倩
王吉能
Original Assignee
阿里巴巴集团控股有限公司
叶舟
王瑜
陈凡
杨洋
董昭萍
钱倩
王吉能
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 阿里巴巴集团控股有限公司, 叶舟, 王瑜, 陈凡, 杨洋, 董昭萍, 钱倩, 王吉能 filed Critical 阿里巴巴集团控股有限公司
Publication of WO2018014764A1 publication Critical patent/WO2018014764A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor

Definitions

  • the present application relates to the field of Internet technologies, and in particular, to a commodity object selection, model determination, and usage heat determination method and apparatus.
  • the e-commerce system In order to improve the transaction performance of commodity objects in the e-commerce system, the e-commerce system often establishes various new channels to increase the exposure of the product objects, for example, establishing various special spike activity theme channels, or the main product object tonality.
  • Theme channels and more.
  • the embodiment of the present application provides a method and device for selecting a product object, determining a model, and determining a usage heat, so as to solve the problem of inefficiency existing in the existing product object selection mode.
  • the embodiment of the present application provides a method for selecting a commodity object, including:
  • the object in the second time period uses an identification model of the relationship between the heats; the first time period is a previous specified time period of the second time period;
  • selecting at least one commodity object from the primary selection object as the commodity object in the second time period that matches the commodity object keyword includes:
  • Selecting at least one object from the primary selection object uses a commodity object having a heat not lower than a set heat threshold as a commodity object in the second time period that matches the commodity object keyword.
  • the method before determining, according to the set object identification model and the use feature data of each of the preliminary objects in the first time period, before the object usage heat of each of the preliminary objects in the second time period, the method further includes:
  • the object recognition model training sample data includes basic feature data of each object recognition model sample object, and the basic feature data of each object recognition model sample object includes Using the feature data of the object recognition model sample object in each third time period, and the object use heat of the object recognition model sample object in each corresponding fourth time period; the third time period is the corresponding fourth The previous specified time period of the time period;
  • the initial object recognition model is trained to obtain the object recognition model.
  • the method further includes:
  • each primary selection object according to the set category identification model and the category corresponding to each primary selection object in the category usage heat of one or more historical synchronization time periods corresponding to the second time period
  • the category uses heat in the category of the second time period; wherein the category recognition model is trained Between the use of the category of the commodity object category in the second time period, and the heat usage of the category of the commodity object category in the one or more historical time periods corresponding to the second time period Identification model of the relationship;
  • the hotspot of the second time period is used to update the object usage heat in the second time period according to the category corresponding to the primary selection object.
  • the method further includes:
  • the category identification model training sample data includes basic feature data of various target recognition model sample objects, and each category identifies the model sample object basis
  • the feature data includes the category usage heat of the category corresponding to the category identification model sample object in each fifth time period, and the category corresponding to the category identification model sample object corresponds to each fifth time period.
  • the pre-established use of the category for predicting the commodity object category in the second time period is used, and the commodity object category corresponds to the second time period.
  • the category of one or more historical contemporaneous time periods is trained using an initial category recognition model of the relationship between the heats to obtain the category identification model.
  • the historical time period of each time period refers to a historical time period that is on the same calendar day or lunar day as the time period and corresponds to the time period.
  • the object in the second time period is used in the category according to the category corresponding to the primary selection object, and the object of the primary selection object is in the second time period.
  • the method further includes:
  • the category corresponding to the primary selection object is a category that matches a specific time period corresponding to the second time period, according to the set coefficient And increasing the category usage heat of the category corresponding to the primary selection object in the second time period.
  • At least one commodity object is selected from the preliminary objects as the second time period, Before the commodity object matching the commodity object keyword, the method further includes:
  • the method before acquiring the primary selected object that matches the commodity object keyword, the method further includes:
  • the determined sample words are used as the final desired commodity object keywords.
  • the object recognition model is a regression model; the category recognition model is a linear model.
  • the embodiment of the present application provides another method for selecting a commodity object, including:
  • the product object information is used by the server according to the set object recognition model and the primary selection objects that match the product object topic in the first time period, and the product data from the product.
  • the set object recognition model is used to characterize the relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period.
  • the method before receiving the product object keyword input by the user, the method further includes:
  • the object recognition model training sample data includes basic feature data of each object recognition model sample object, and each object recognition
  • the base feature data of the different model sample object includes the use feature data of the object recognition model sample object in each third time period, and the object use heat of the object recognition model sample object in each corresponding fourth time period;
  • the third time period is a previous specified time period of the corresponding fourth time period;
  • Transmitting the object recognition model training sample data to a server wherein the server identifies the basic feature data of each object recognition model sample object included in the sample data according to the object recognition model, and the pre-established product object for predicting The initial object recognition model using the relationship between the feature data and the object use heat in the second time period in the first time period is trained to obtain the set object recognition model.
  • the embodiment of the present application provides a method for determining a model, including:
  • the object recognition model training sample data includes basic feature data of each object recognition model sample object, and the basic feature data of each object recognition model sample object includes Using the feature data of the object recognition model sample object in each third time period, and the object use heat of the object recognition model sample object in each corresponding fourth time period; the third time period is the corresponding fourth The previous specified time period of the time period;
  • the pre-established use feature data for predicting the product object in the first time period, and the commodity object in the second time is trained using the initial object recognition model of the association relationship between the heats, and is obtained between the use feature data for characterizing the commodity object in the first time period and the object use heat of the commodity object in the second time period.
  • An object recognition model of the association relationship; the first time period is a previous specified time period of the second time period.
  • the usage characteristic data includes at least one or more of the number of browsing times, the number of collections, the number of purchases, the number of transactions, the number of comments, and the number of searches; the usage heat of the object includes at least volume, turnover, And any one or more of the transaction conversion rates.
  • the embodiment of the present application provides another method for determining a model, including:
  • the type training sample data includes basic feature data of various target recognition model sample objects, and the basic feature data of each category recognition model sample object includes the category corresponding to the category identification model sample object at each first time
  • the category of the segment uses the heat, and the category corresponding to the category identification model sample object uses the heat in the category of one or more historical time periods corresponding to the respective first time periods;
  • the category is trained in an initial category recognition model of the association relationship between the categories of heat usage using one or more historical time periods corresponding to the second time period, and is obtained in the second category for characterizing the commodity object.
  • the category of the time period uses a heat classification, a category recognition model that is related to the heat usage of the category of the commodity object category in one or more historical time periods corresponding to the second time period.
  • the heat usage of the category includes at least one or more of a volume, a turnover, and a transaction conversion rate.
  • the embodiment of the present application further provides a method for determining a heat usage, including:
  • each item based on the relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period, and the use feature data of each product object in the first time period
  • the object uses heat in the second time period
  • the first time period is a previous specified time period of the second time period; and the association relationship is based on usage characteristic data of each sample object in each third time period and each sample object is in each corresponding fourth time
  • the object of the segment is established using the heat;
  • the third time period is the previous specified time period of the corresponding fourth time period.
  • the embodiment of the present application further provides a commodity object selection device, including:
  • a keyword receiving unit configured to receive a commodity object keyword sent by the user terminal
  • An object obtaining unit configured to acquire a primary selection object that matches the keyword of the commodity object
  • a heat determining unit for identifying a model based on the set object and each of the primary objects at the first time Using the feature data in the segment, determining the object usage heat of each primary object in the second time period; wherein the object recognition model is the training use characteristic data used to represent the commodity object in the first time period, and a recognition model of an association relationship between the objects of the commodity object in the second time period; the first time period is a previous specified time period of the second time period;
  • An object screening unit configured to select at least one commodity object from the primary selection object as the second time period according to the object usage heat of each primary selection object in the second time period, and the subject matter of the commodity object Matching item object.
  • the embodiment of the present application further provides another commodity object selection device, including:
  • a keyword receiving unit configured to receive a commodity object keyword input by the user
  • a keyword sending unit configured to send the commodity object keyword to a server
  • An object information receiving unit configured to receive commodity object information returned by the server according to the commodity object keyword
  • An object determining unit configured to determine a product object corresponding to the product object information as a product object that matches the product object keyword in a second time period
  • the product object information is used by the server according to the set object recognition model and the primary selection objects that match the product object topic in the first time period, and the product data from the product.
  • the set object recognition model is used to characterize the relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period.
  • the embodiment of the present application further provides a model determining apparatus, including:
  • a data receiving unit configured to receive object recognition model training sample data sent by the user terminal, where the object recognition model training sample data includes basic feature data of each object recognition model sample object, and each object recognition model sample object
  • the basic feature data includes the use feature data of the object recognition model sample object in each third time period, and the object use heat of the object recognition model sample object in each corresponding fourth time period; the third time The segment is the corresponding fourth time The previous specified time period of the segment;
  • a model training unit configured to perform, according to the basic feature data of each object recognition model sample object included in the sample identification model training sample data, the pre-established use characteristic data for predicting the commodity object in the first time period, and
  • the commodity object is trained in the initial object recognition model of the relationship between the objects using the heat in the second time period, and the use feature data for characterizing the commodity object in the first time period and the second time period of the commodity object are obtained.
  • An object recognition model in which an object uses an association relationship between heats.
  • the embodiment of the present application further provides another model determining apparatus, including:
  • a data receiving unit configured to receive the category identification model training sample data sent by the user terminal, wherein the category identification model training sample data includes basic feature data of various target recognition model sample objects, and each category Identifying the basic feature data of the model sample object includes the category usage heat of the category corresponding to the category identification model sample object in each first time period, and the category corresponding to the category identification model sample object in each category The usage of the category of one or more historical time periods corresponding to a period of time;
  • a model training unit configured to: according to the category identification model, the basic feature data of the sample objects of the various types of mesh recognition models included in the sample data, and the pre-established categories for predicting the category of the commodity object in the second time period Training is performed using the initial category recognition model of the relationship between the heat and the category of the commodity object category in the one or more historical time periods corresponding to the second time period, and is used to characterize the commodity.
  • the category of the object category in the second time period is the category of the association between the heat usage and the category usage heat of the commodity object category in one or more historical time periods corresponding to the second time period. Identify the model.
  • the embodiment of the present application further provides a heat determining device, including:
  • a data obtaining unit configured to acquire usage characteristic data of each commodity object in a first time period
  • a heat determining unit configured to use an association relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period, and use of each commodity object in the first time period Feature data, determining the object usage heat of each commodity object in the second time period;
  • the first time period is a previous specified time period of the second time period; and the association relationship is based on usage characteristic data of each sample object in each third time period and each sample object is in each corresponding fourth time
  • the object of the segment is established using the heat;
  • the third time period is the previous specified time period of the corresponding fourth time period.
  • the embodiment of the present application provides a method and device for selecting a product object, determining a model, and determining a usage heat, and automatically selecting at least one product object from a mass object based on a product object keyword input by the user and a set object recognition model.
  • FIG. 1 is a schematic diagram of a possible application scenario of a method for selecting a commodity object according to Embodiment 1 of the present application;
  • FIG. 2 is a schematic flowchart diagram of a method for selecting a commodity object in the first embodiment of the present application
  • FIG. 3 is a schematic flowchart diagram of a method for determining a model in the first embodiment of the present application
  • FIG. 4 is a schematic flowchart diagram of another method for determining a model in the first embodiment of the present application
  • FIG. 5 is a schematic flowchart diagram of a method for determining a heat usage according to Embodiment 1 of the present application
  • FIG. 6 is a schematic diagram showing a possible structure of a product object selection device in Embodiment 2 of the present application.
  • FIG. 7 is a schematic diagram showing a possible structure of another commodity object selection device in the second embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of a model determining apparatus in Embodiment 2 of the present application.
  • FIG. 9 is a schematic diagram showing a possible structure of another model determining apparatus in Embodiment 2 of the present application.
  • FIG. 10 is a schematic diagram showing a possible structure of a heat determining device according to Embodiment 2 of the present application.
  • Embodiment 1 is a diagrammatic representation of Embodiment 1:
  • FIG. 1 A schematic diagram of an application scenario, which may include, for example, a user terminal 11 and a server 12, where:
  • the user terminal 11 can receive the product object keyword input by the user 10 and send the product object topic word to the server 12; the server 12 can obtain the product object subject word according to the product object keyword sent by the user terminal 11 Matching the primary selection object, and based on the set object recognition model and the usage characteristic data of each primary selection object in the first time period, determining the object usage heat of each primary selection object in the second time period, and according to each primary selection
  • the object uses the heat in the second time period, and selects at least one product object from the primary selection object as the commodity object in the second time period that matches the product object keyword; the user terminal 11 can receive The product object information of the at least one product object returned by the server 12 after selecting at least one product object, and the product object information Corresponding commodity object as a commodity object in the second time period that matches the commodity object keyword; wherein the set object recognition model can be used to characterize the use of the commodity object in the first time period The relationship between the feature data and the object usage heat of the commodity object in the second time period; the first time period is
  • the user terminal 11 and the server 12 can perform a communication connection through a communication network, and the network can be a local area network, a wide area network, or the like.
  • the user terminal 11 may be a terminal device such as a mobile phone, a tablet computer, a notebook computer, a personal computer, or even a client installed in the terminal device;
  • the server 12 may be any server device capable of supporting processing operations such as screening of commodity objects. .
  • At least one product object can be automatically selected from the mass object as the final object satisfying the user's demand based on the product object keyword input by the user and the set object recognition model, thereby The efficiency of selecting commodity objects is greatly improved, thereby reducing the cost of manual inventory and improving operational efficiency.
  • the method for selecting a product object may include the following steps:
  • Step 201 The user terminal receives the object recognition model training sample data input by the user, and sends the object recognition model training sample data to the server.
  • the object recognition model training sample data includes basic feature data of each object recognition model sample object, and the basic feature data of each object recognition model sample object includes the object recognition model sample object in each third time period.
  • the use feature data within, and the object recognition model sample object uses heat in each corresponding fourth time period; the third time period is a previous specified time period of the corresponding fourth time period.
  • the usage feature data of the commodity object such as each object recognition model sample object may include at least Any one or more of the number of times of browsing, the number of times of collection, the number of purchases (adding to the shopping cart), the number of transactions, the number of comments, and the number of searches;
  • the object usage heat of each object object such as the object recognition model sample object may include at least a deal Any one or more of quantity, turnover, and transaction conversion rate.
  • the use feature data of the product object such as each object recognition model sample object can be acquired from the product object operation information of each e-commerce website, and the object usage heat of each product object such as the object recognition model sample object can be based on each The feature data of the product object such as the object recognition model sample object is calculated and will not be described here.
  • third and fourth time periods may generally be historical time segments, that is, the basic feature data of each object recognition model sample object may generally be corresponding historical data; and, third, fourth The time period, and the time period of the specified time period can be flexibly set according to the actual situation, such as 1 day, 1 week, 1 month, etc. (usually a minimum of one day).
  • the lengths of the third and fourth time periods may be the same or different, for example, the fourth time period may be one day, and the third time period corresponding to the fourth time period (ie, the previous designation of the fourth time period)
  • the time period may be one month or one year, etc., or the fourth time period may be one month, and the third time period corresponding to the fourth time period may be one day, etc.;
  • the three time periods may be the previous specified time period adjacent to the fourth time period, and may or may not be adjacent.
  • Step 202 The server receives the object recognition model training sample data sent by the user terminal, and according to the object recognition model, the basic feature data of each object recognition model sample object in the sample data is trained, and the pre-established initial object recognition model is trained to obtain the The required object recognition model.
  • the initial object recognition model is a recognition model for predicting an association relationship between the use feature data of the commodity object in the first time period and the object use heat of the commodity object in the second time period; the object recognition The model is a training model for characterizing the relationship between the use feature data of the commodity object in the first time period and the heat usage of the object object in the second time period, the first time period is The previous specified time period of the second time period.
  • the sizes of the first and second time periods and the like may also be flexibly set according to actual conditions, and the lengths of the first and second time periods may be different. The same may be different (however, the size of the first time period may be the same as the third time period, and the size of the second time period may be the same as the fourth time period), which is not limited thereto.
  • the object usage identification object model object and the object object use heat as the transaction volume, for example, the corresponding object recognition model can be obtained by the following steps:
  • A1 Establish an initial object recognition model related to volume.
  • the third and fourth time periods can be set to 1 day; and it is assumed that y(t) can represent the volume of a certain commodity object on the date t, and the commodity object is represented by x1(t-1)
  • x1(t-1) The number of times of the date t-1, x2 (t-1) indicates the number of times the item is stored on the date t-1, and x3(t-1) indicates the number of times the item is purchased on the date t-1, etc.
  • A2 Calculating the transaction volume of each object recognition model sample object in each fourth time period (such as the daily sales volume at the date t), and using the feature data of the third time period corresponding to each object recognition model sample object ( For example, the feature data such as browsing, collection, purchase, transaction, comment, search, etc. of the date t-1 are associated to obtain a plurality of associated data; and based on the obtained associated data, the established initial object recognition model is trained to obtain The actual values of the coefficients a(1), a(2), a(3), etc., to obtain the desired object recognition model.
  • the regression model can better cross the features, improve the predictive ability, and prevent the merchant from cheating, thereby improving the accuracy of the prediction.
  • the initial object recognition model and the object recognition model may generally be regression models such as a Gradient Boost Regression Tree model.
  • the initial object recognition model and the object recognition model may also adopt a linear model, which is not limited herein.
  • the object recognition model may be updated in real time or periodically according to the latest object recognition model sample data to improve the accuracy of the object recognition model.
  • the use feature data of each object recognition model sample object in each third time period, and the object use heat of each object recognition model sample object in each corresponding fourth time period may be replaced with each object recognition.
  • the corresponding usage feature data of the model sample object under the channel and the object usage heat and the like are used to better predict the object usage heat of the product object under the channel, and details are not described herein again.
  • steps 201 and 202 are steps of establishing an object recognition model in advance, and are not required to be performed every time the product object is selected, unless the object recognition model trains sample data. A corresponding update has occurred. That is, after performing step 201 and step 202, the subsequent steps may be repeatedly executed multiple times, and details are not described herein.
  • Step 203 The user terminal receives the product object keyword input by the user, and sends the product object keyword to the server.
  • the user terminal may further extend the received product object keyword to the server, and may expand the received product object keyword and The expanded product object subject is sent to the server to increase the richness of the product object keywords.
  • the user terminal expands the product object keyword input by the user, and can also avoid the situation that the server needs to simultaneously expand the received large number of product object keywords when a large number of user terminals simultaneously send the product object keyword to the server. Occurs to save the processing resources of the server and reduce the working pressure of the server, thereby further improving the speed and efficiency of subsequent product object selection.
  • the user terminal may augment the received product object keyword in the following manner:
  • the set sample corpus may be a corpus of e-commerce news and the like crawled from an external website by a crawler; and, in addition, based on the set sample corpus, determine a phase with each product object keyword
  • the language model capable of characterizing the word as a real-value vector such as the word2vec model
  • the word2vec model may be firstly trained based on the set sample corpus, and based on the trained word2vec
  • a language model such as a model converts each product object keyword input by the user and each word in the sample corpus into a vector; after that, the calculated similarity calculation formula, such as the Cosine formula, can be used to calculate each word in the sample corpus.
  • the similarity between the keyword objects of each product object input by the user; finally, the word above the value (which may include the value) is selected as the final desired topic word by setting a corresponding similarity threshold.
  • the user terminal may perform the three words based on the set sample corpus. Expansion, such as expansion to get “fashion”, “trend”, “shirt”, “suit”, “dress”, “jeans” and other words, and the expanded words as the final product object keywords.
  • Step 204 The server receives the commodity object keyword sent by the user terminal, and acquires a primary selection object that matches the commodity object keyword.
  • the server may search for the corresponding product object title from the product object information of each e-commerce website according to the product object keyword transmitted by the user terminal, and match the product object keyword sent by the user terminal.
  • a product object (such as partial matching, etc.), and each of the searched product objects is a preliminary selection object that matches the product object keyword transmitted by the user terminal.
  • Each of the product object information in the e-commerce website may include basic information such as an ID (identification), a name (ie, a title), a place of origin, a seller user information, and a category of the product object, and details are not described herein.
  • the server may further augment the received product object keyword before acquiring the primary object that matches the product object keyword according to the product object keyword sent by the received user terminal. In order to obtain the corresponding primary selection object based on the expanded commodity object subject terms.
  • the specific implementation manner in which the server augments the received product object keyword is similar to the specific implementation manner in which the user terminal expands the received product object keyword in step 203, and details are not described herein.
  • the product object theme input to the user is executed by the server instead of the user terminal.
  • the operation of the word expansion can reduce the performance requirements of the user terminal, so that the method described in the embodiment of the present application has a wider scope of application; in addition, for the user terminal, the processing resources of the user terminal can be saved, and the user terminal can be saved. Work pressure.
  • Step 205 The server determines, according to the object recognition model obtained by the training and the use feature data of each primary selection object in the first time period, the object usage heat of each primary selection object in the second time period.
  • the object recognition model is that the server recognizes the volume of the model sample object on each date according to each object, and the browsing, collection, and purchase of each object recognition model sample object on one or more dates before the corresponding date.
  • the object recognition model trained by the feature data such as transaction, comment, search, etc. may be based on the object recognition model, and browse, collect, purchase, and trade according to one or more dates of each primary object before the date t+1.
  • Characteristic data such as comments, searches, etc., predict the volume of each primary object at the date t+1.
  • the prediction may not be well predicted, and therefore, the transaction amount may not be directly predicted.
  • the volume of each commodity object is predicted, and then the transaction amount is multiplied by the corresponding price to improve the accuracy of the prediction.
  • Step 206 The server selects at least one commodity object from the primary selection objects according to the object usage heat of each primary selection object in the second time period, and matches the keyword of the commodity object in the second time period.
  • the server may select at least one object from the primary selection object according to the heat usage of the objects in the second time period of each primary selection object, and the usage heat is not lower than the setting.
  • the product object of the heat threshold (which can be flexibly set according to actual conditions) is used as the product object in the second time period that matches the keyword of the product object.
  • the server may also sort the primary objects according to the order in which the object usage heat is used, and take the pre-K (K is any positive integer) primary selection objects as the second time period.
  • K is any positive integer
  • the primary screening object may be manually filtered according to actual needs, or the short-term object is not used hotly. Or a product object whose price does not meet the user's needs (for example, a commodity object with only 5 transactions within three days and a price between 10 and 200 yuan), so as to select a desired commodity object based on the selected primary objects; and / or,
  • the selected commodity object may be manually screened according to actual needs, or Deleting the short-term object using the commodity object whose heat is not high or the price does not meet the user's demand, and using the filtered commodity object as the final desired commodity object in the second time period and matching the commodity object keyword I will not repeat them.
  • the object usage heat of each primary selection object may be adjusted according to the time information, thereby adjusting the order of each primary selection object.
  • the time series model can be used to predict the heat of the category in the second time period, so that some seasonal commodity objects can emerge in advance to further improve the accuracy of the selection of the commodity object.
  • the method may further include:
  • each primary selection object according to the set category identification model and the category corresponding to each primary selection object in the category usage heat of one or more historical synchronization time periods corresponding to the second time period
  • the category uses heat in the category of the second time period; wherein the category identification model is trained to represent the category of the commodity object category in the second time period using the heat, and the commodity object category Between the heat usage of the category of one or more historical time periods corresponding to the second time period Identification model of association relationship;
  • the hotspot of the second time period is used to update the object usage heat in the second time period according to the category corresponding to the primary selection object.
  • the category corresponding to the primary selection object may be in the category of the second time period and the category of the primary selected object in the second time period.
  • the updated object in the second time period as the primary selection object uses the heat.
  • the heat usage of the object is similar to the heat usage of the object, and the heat usage of the category may include at least one or more of a volume, a turnover, and a transaction conversion rate, and the category of the commodity object such as each sample object is used. It can be calculated based on the use characteristic data of the product object such as each sample object, and is not limited thereto.
  • the historical time period of each time period refers to a historical time period that is on the same calendar day or lunar day as the time period and corresponds to the time period; for example, for the time period January 01, 2016 ⁇
  • the historical period of the period can be from January 01, 2015 to January 05, 2015, January 01, 2014 to January 05, 2014, etc. This is not to be repeated.
  • the category based on the set category identification model and the category corresponding to each primary selection object is one or more historical synchronization time periods corresponding to the second time period.
  • the server can obtain the category identification model by:
  • the category identification model training sample data includes basic feature data of various target recognition model sample objects, and each category identifies the model sample object basis
  • the feature data includes the category usage heat of the category corresponding to the category identification model sample object in each fifth time period, and the category corresponding to the category identification model sample object corresponds to each fifth time period.
  • the pre-established use of the category for predicting the commodity object category in the second time period, and the commodity object category and the second time period is trained using an initial category recognition model of the relationship between the heats to obtain the category identification model.
  • the fifth time period may be a historical time period; and, similar to the foregoing descriptions regarding the first, second, third, and fourth time periods, the size of the fifth time period may also be based on The actual situation is flexible (although the size of the fifth time period is usually the same as the second time period), which is not limited.
  • the corresponding category identification model can be obtained by the following steps:
  • B1 Establish an initial category recognition model related to volume.
  • each fifth time period can be set to 1 month; the initial category recognition model is a linear model; and it is assumed that the volume of a certain category in this year is z(t), and the volume of the category in the same period last year.
  • the linear model is used as the category recognition model because the model parameters are small and the historical data of the category is relatively stable.
  • other models such as a regression model, may be used as the category recognition model to improve the accuracy of the heat prediction using the category, which is not limited herein.
  • B2 Use the historical data of the category of the latest period of time (such as the latest 3 months of the category history volume of this year) and its corresponding historical data (as in the same period of at least two years on a calendar or lunar calendar) Historical data), the established initial category recognition model is trained to obtain the actual values of the coefficients b(1), b(2), etc., to obtain the final category recognition model.
  • the category identification model may be updated in real time or periodically according to the latest category identification model sample data to improve the category recognition model.
  • Accuracy again, taking a product object as an example, after the channel is online, it can also use the heat of the categories of the various target recognition model sample objects in each fifth time period, and various types of object recognition.
  • the model sample object is replaced with a category of the target recognition model sample object under the channel in one or more historical synchronization time periods corresponding to each fifth time period.
  • the corresponding categories use heat, etc., to better predict the heat usage of the category of the product object category under the channel, and will not be described here.
  • the category identification model obtained by the training and the class corresponding to each primary selection object may be Determining the category corresponding to each primary object by using the heat of the category in the one or more historical time periods corresponding to the next month (for example, the historical time period of the previous year or the previous two years) The heat is used in the category of the next month.
  • the corresponding initial category recognition model may be established in units of days, such as establishing the following initial class.
  • the recognition model z1(t) b1(1)*z1(t-1)+b1(2)*z1(t-2)+..., where z1(t) is a category of this year date t
  • the heat used for the purpose, z1(t-1) is the heat used for the category of the same category last year, z1(t-2) is the heat used for the category of the same period of the previous year, and so on; b1(1), b1 (2) Wait for the coefficient to be estimated; after that, use the historical data of the latest period of time (such as 90 days, etc.) and its corresponding historical data to train the established initial category recognition model to obtain b1.
  • the values of the coefficients (1) and b1(2) are used to obtain the final category identification model. After that, the category identification model obtained by the training is used to calculate the 30-day category of each category. The heat, then, the average is used to obtain a more stable category of heat usage for each month, and will not be repeated here.
  • the second time period when the second time period is determined to be a specific time period such as a holiday, the second time period may be corresponding to
  • the various categories of destinations associated with a particular time period are additionally weighted using heat (the degree of additional weighting can be based on actual demand) to ensure that commodity objects corresponding to these categories can emerge in time. For example, the Mid-Autumn Festival moon cake will be hot, and thus, when the second time period is the Mid-Autumn Festival time period, the weight category corresponding to the moon cake can be additionally weighted.
  • the method may further include:
  • the second time period is a specific time period (such as a Mid-Autumn Festival, a Dragon Boat Festival, and the like, a holiday time period, etc.)
  • the category corresponding to the primary selection object is a specific time corresponding to the second time period
  • the category matched by the segment is based on the set coefficient (the coefficient can be flexibly adjusted according to the actual situation. For example, if the degree of matching between the category and the specific time period is higher, the coefficient can be larger, if the matching degree is lower, Then, the coefficient may be smaller, etc., and the category corresponding to the primary selection object is used to increase the heat usage of the category in the second time period.
  • each commodity object may result in the appearance of some homogeneous commodity objects, such as "Mid-Autumn Festival Gifts Italian Imports Ferrero Chocolate Roses DIY Gift Boxes Birthday Lovers” and "SF” Italian Ferrero chocolate DIY heart-shaped rose gift box Mid-Autumn Festival birthday gift”
  • some homogeneous commodity objects such as "Mid-Autumn Festival Gifts Italian Imports Ferrero Chocolate Roses DIY Gift Boxes Birthday Lovers” and "SF” Italian Ferrero chocolate DIY heart-shaped rose gift box Mid-Autumn Festival birthday gift”
  • the method may further include:
  • a collection of objects For each group of at least one primary selection object whose similarity between each other is not lower than a set similarity threshold (the similarity threshold may be the same or different from the first similarity threshold mentioned in the foregoing) a collection of objects, retaining an object in the collection of objects, and deleting other objects, so that at least one commodity object can be selected from each primary selection object obtained after performing the deletion operation as the second time period, The commodity object whose product object keywords match.
  • the similarity between the respective product objects can be obtained by calculating the similarity of the product object titles.
  • other similar similarity calculation formulas may be used to calculate the similarity between the commodity objects, which is not limited thereto.
  • each group of objects consisting of at least one primary selection object whose similarity between them is not lower than a set similarity threshold
  • an object in the object set is retained, it is usually retained.
  • the corresponding object uses the object with the highest heat to improve the transaction performance of the commodity object and improve the user's application experience.
  • the related logic can also be configured so that the product object finally displayed by the channel is not repeated within a few days, here is not Let me repeat.
  • each primary selection object in addition to selecting at least one product object from the primary selection object as the commodity object in the second time period that matches the product object keyword, each primary selection object may be performed. In addition to the same operation, the selected ones may be selected in a similar manner after selecting at least one commodity object from the primary selection objects as the commodity objects in the second time period that match the product object keywords.
  • the commodity object performs the same operation.
  • the server may select at least one product object from the primary selection object as the product object in the second time period that matches the product object keyword, and may further display the product object information of the selected product object (The unique identification information such as the ID of the product object or the link of the product object is stored and/or transmitted to the user terminal.
  • Step 207 The user terminal receives the product object information of the product object that is returned by the server and matches the product object keyword, and uses the product object corresponding to the product object information as the second time period, and the The item object whose product object keyword matches.
  • the user terminal may display the received product object information, and/or display the product object corresponding to the product object information for the user to view, which is not described herein.
  • step 201, step 203, and step 207 independently constitute a product object selection process executed on the user terminal side, and step 202, step 204 to step 206 are independent.
  • the structure of the product object selection executed on the server side is not described herein.
  • the embodiment of the present application further provides two methods for determining a model.
  • the method for determining a model may include the following steps:
  • Step 301 Receive object recognition model training sample data sent by the user terminal.
  • the object recognition model training sample data includes basic feature data of each object recognition model sample object, and the basic feature data of each object recognition model sample object includes the object recognition model sample object in each third time period.
  • the use feature data within, and the object recognition model sample object uses heat in each corresponding fourth time period; the third time period is a previous specified time period of the corresponding fourth time period.
  • Step 302 According to the basic feature data of each object recognition model sample object included in the object recognition model training sample data, the pre-established initial object recognition model is trained to obtain a desired object recognition model.
  • the initial object recognition model is configured to predict a relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period; the first time period is The previous specified time period of the second time period.
  • the object recognition model is used to characterize the relationship between the use feature data of the commodity object in the first time period and the object use heat of the commodity object in the second time period.
  • the another method for determining a model may include the following steps:
  • Step 401 Receive the category identification model training sample data sent by the user terminal.
  • the category identification model training sample data includes basic feature data of various target recognition model sample objects, and the basic feature data of each category recognition model sample object includes the category identification model sample object corresponding to the category identification data.
  • the category is in each set sample time period (such as the fifth time period in the foregoing product object selection method; in addition, if the aforementioned product object selection method is not considered, the set sample time period may also be expressed as the first time Segment, in the second time period mentioned later with this model determination method.
  • the category usage heat which is not described in detail, and the category of the category identification model sample object corresponding to one or more historical synchronization time periods corresponding to each set sample time period Use heat.
  • Step 402 Train the basic feature data of the sample objects of the various types of mesh recognition models included in the sample data according to the category identification model, and train the pre-established initial category recognition model to obtain a desired category recognition model.
  • the initial category identification model is configured to predict a category usage heat of the commodity object category in the second time period, and one or more historical synchronization time periods corresponding to the commodity object category corresponding to the second time period.
  • the category uses the relationship between the heats.
  • the category identification model is used to characterize the category usage heat of the commodity object category in the second time period, and the category of one or more historical synchronization time periods corresponding to the commodity object category corresponding to the second time period. Use the relationship between the heats.
  • execution bodies of the model determination method shown in FIG. 3 and FIG. 4 may all be servers; and the specific implementation of each step of the model determination method shown in FIG. 3 and FIG. I will not repeat them.
  • the usage heat determination method may include the following steps:
  • Step 501 Acquire usage characteristic data of each commodity object in a first time period
  • Step 502 based on the relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period, and the use feature data of each commodity object in the first time period, Determining the object usage heat of each commodity object in the second time period;
  • the first time period is a previous specified time period of the second time period; and the association relationship is based on the use feature data of each sample object in each third time period and each corresponding sample object in each corresponding The object of the four time period is established using the heat; the third time period is the previous specified time period of the corresponding fourth time period.
  • association relationship is similar to the set object recognition model described above.
  • specific implementation of each step of using the heat determination method shown in FIG. 5 can be referred to the foregoing related description, and no further description is provided herein. .
  • At least one product object can be automatically selected from the mass object based on the product object keyword input by the user and the set object recognition model.
  • the ultimate object that meets the user's needs thereby greatly improving the efficiency of the selection of commodity objects, thereby reducing the cost of manual inventory and improving operational efficiency.
  • the object can be used in the second time period according to each of the primary objects, at least one object selected from the primary objects is used as the final desired product object, and the product object having the heat not lower than the set heat threshold is used as the final desired product object. Can improve the accuracy of the selection of commodity objects.
  • the time series model that is, the category identification model
  • the heat is used, and the order of each commodity object is adjusted according to the category, so that the season can be Holidays and other timely adjustment of commodity objects to further reduce the cost of manual inventory and improve operational efficiency.
  • the solution described in the embodiment of the present application has no language, software or hardware limitation, and can be implemented based on a general cloud computing platform.
  • a high-performance programming language such as C, C++, or Java
  • high-performance hardware etc., which is not described in this embodiment.
  • Embodiment 2 is a diagrammatic representation of Embodiment 1:
  • the second embodiment of the present application provides a product object selection device.
  • the product object selection device refer to the related description of the server in the first embodiment of the method.
  • the details of the product object selection device may include:
  • the keyword receiving unit 601 is configured to receive a product object keyword sent by the user terminal;
  • the object obtaining unit 602 is configured to obtain a primary selection object that matches the keyword of the product object
  • the heat determining unit 603 can be used to identify the model based on the object and each of the primary objects in the first Using the feature data for a period of time, determining the object usage heat of each of the preliminary objects in the second time period; wherein the object recognition model is the trained use characteristic data for characterizing the commodity object in the first time period And a recognition model of an association relationship between the object usage heat of the commodity object in the second time period; the first time period being a previous specified time period of the second time period;
  • the object screening unit 604 is configured to select, according to the object usage heat of the first selected object in the second time period, at least one commodity object from the primary selection object as the keyword in the second time period Matching product objects.
  • the object screening unit 604 is specifically configured to select at least one object from the primary selection object to use a commodity object whose heat is not lower than a set heat threshold, as the second time period, and the commodity object theme.
  • the product object that matches the word is specifically configured to select at least one object from the primary selection object to use a commodity object whose heat is not lower than a set heat threshold, as the second time period, and the commodity object theme. The product object that matches the word.
  • the commodity object selection device may further include an object recognition sample data receiving unit 605 and an object recognition model determining unit 606:
  • the object identification sample data receiving unit 605 is configured to determine an object of each primary selection object in the second time period based on the set object recognition model and the usage feature data of each primary selection object in the first time period. Before using the heat, receiving the object recognition model training sample data sent by the user terminal, wherein the object recognition model training sample data includes basic feature data of each object recognition model sample object, and each object identifies a model sample object basis
  • the feature data includes usage feature data of the object recognition model sample object in each third time period, and object usage heat of the object recognition model sample object in each corresponding fourth time period; the third time period is a corresponding specified time period of the corresponding fourth time period;
  • the object recognition model determining unit 606 is configured to: according to the basic feature data of each object recognition model sample object, use the pre-established use feature data for predicting the commodity object in the first time period, and the commodity object in the second time
  • the object of the segment is trained using an initial object recognition model of the relationship between the heats to obtain the object recognition model.
  • the commodity object selection device may further include a category heat determination unit 607 and an object heat update unit 608:
  • the category heat determining unit 607 is configured to: before selecting at least one product object from the primary selection object as the commodity object in the second time period that matches the product object keyword, based on the set class Determining the model, and the category corresponding to each primary object in the category of one or more historical time periods corresponding to the second time period, determining the category corresponding to each primary object in the The category of the second time period uses heat; wherein the category identification model is trained to represent the category of the commodity object category in the second time period, and the commodity object category is in the second A recognition model of the relationship between the heat usage of the category of one or more historical time periods corresponding to the time period;
  • the object heat update unit 608 may be configured to use, for each primary selection object, a heat in the category of the second time period according to the category corresponding to the primary selection object, and the second selected time in the second time period The objects of the segment are updated with heat.
  • the commodity object selection device may further include a category identification sample data receiving unit 609 and a category identification model determining unit 610:
  • the category identification sample data receiving unit 609 is configured to: at the one or more historical synchronization times corresponding to the second time period, based on the set category identification model and the category corresponding to each primary selection object
  • the category of the segment uses the heat to determine that the category corresponding to each of the primary selected objects receives the category identification model training sample data sent by the user terminal before the category usage heat of the second time period, wherein the category identification
  • the model training sample data includes basic feature data of various target recognition model sample objects, and the basic feature data of each category recognition model sample object includes the category corresponding to the category identification model sample object at each fifth time
  • the category usage heat of the segment, and the category corresponding to the category identification model sample object uses the heat in the category of one or more historical synchronization time periods corresponding to each fifth time period;
  • the category identification model determining unit 610 is configured to use, according to the basic feature data of the sample objects of the various types of mesh recognition models, the heat usage and the commodity object used for predicting the category of the commodity object category in the second time period.
  • the category is trained in an initial category recognition model of the association relationship between the categories of heat usage of one or more historical time periods corresponding to the second time period, and the category is obtained Identify the model.
  • the historical time period of each time period refers to a historical time period that is on the same calendar day or lunar day as the time period and corresponds to the time period.
  • the commodity object selection device may further include a category heat update unit 611:
  • the category heat update unit 611 may be configured to use the heat in the category of the second time period according to the category corresponding to the primary selection object for any primary selection object, and the primary selection object is in the Before determining that the second time period is a specific time period, the object corresponding to the first time period is determined to be a specific time corresponding to the second time period. The category matched by the segment increases the category usage heat of the category corresponding to the primary selection object in the second time period according to the set coefficient.
  • the commodity object selection device may further include a commodity object de-same unit 612:
  • the commodity object de-same unit 612 can be configured to select at least one commodity object from the primary selection object as the commodity object in the second time period that matches the product object keyword, according to each primary selection object. Correlating degrees between corresponding titles, determining similarity between each primary selection object; for each set of object collections consisting of at least one primary selection object whose similarity between the two is not lower than the first similarity threshold , retain an object in the collection of objects, and delete other objects.
  • the commodity object selection device may further include a keyword expansion unit 613:
  • the keyword expansion unit 613 may be configured to determine, for each product object keyword sent by the user terminal, based on the set sample corpus, before acquiring the primary object that matches the product object keyword
  • the similarity between the subject keywords is not lower than the at least one sample word of the second similarity threshold; the determined sample words are taken as the final desired commodity object keywords.
  • the object recognition model may be a regression model; the category recognition model may be a linear model.
  • the second embodiment of the present application further provides another product object selection device.
  • the other product object selection device refer to the first embodiment of the method.
  • the other commodity object selection device can mainly include:
  • the keyword receiving unit 701 is configured to receive a product object keyword input by the user
  • a keyword sending unit 702 configured to send the product object keyword to a server
  • the object information receiving unit 703 is configured to receive the commodity object information returned by the server according to the product object keyword;
  • the object determining unit 704 is configured to determine the product object corresponding to the product object information as the product object that matches the product object keyword in the second time period;
  • the product object information is used by the server according to the set object recognition model and the primary selection objects that match the product object topic in the first time period, and the product data from the product.
  • the set object recognition model is used to characterize the relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period.
  • the another commodity object selection device may further include an object recognition sample data receiving unit 705 and an object recognition sample data transmitting unit 706:
  • the object recognition sample data receiving unit 705 is configured to receive the object recognition model training sample data input by the user before receiving the commodity object keyword input by the user, wherein the object recognition model training sample data includes each object recognition model Base feature data of the sample object, and the base feature data of each object recognition model sample object includes usage feature data of the object recognition model sample object in each third time period, and the object recognition model sample object is in each The object of the corresponding fourth time period uses the heat; the third time period is the previous specified time period of the corresponding fourth time period;
  • the object identification sample data sending unit 706 is configured to send the object recognition model training sample data to a server, and the server identifies the basic features of each object recognition model sample object included in the sample data according to the object recognition model. Data, used in advance for predicting the use of feature data of the product object in the first time period, and the use of the object object in the second time period The initial object recognition model of the relationship between the two is trained to obtain the set object recognition model.
  • the another commodity object selection device may further include a category identification sample data receiving unit 707 and a category identification sample data sending unit 708:
  • the category identification sample data receiving unit 707 is configured to receive, after receiving the product object information returned by the server according to the product object subject word, the category identification model training sample data input by the user, wherein the category The recognition model training sample data includes basic feature data of various target recognition model sample objects, and the basic feature data of each category recognition model sample object includes the category corresponding to the category identification model sample object in each fifth The category usage heat of the time period, and the category corresponding to the category identification model sample object uses the heat in the category of one or more historical synchronization time periods corresponding to each fifth time period;
  • the category identification sample data sending unit 708 is configured to send the category identification model training sample data to a server, and the server trains the sample of various types of eye recognition models included in the sample data according to the category identification model.
  • the basic feature data of the object, for the pre-established category use heat for predicting the item object category in the second time period, and one or more historical time periods corresponding to the item object category corresponding to the second time period.
  • the category of the segment is trained using the initial category recognition model of the relationship between the heats, and the category used to characterize the commodity object category in the second time period is used, and the commodity object category is in the second time.
  • the category identification model of the association relationship between the heats of the one or more historical time periods corresponding to the segments are examples of the association relationship between the heats of the one or more historical time periods corresponding to the segments.
  • the second embodiment of the present application further provides a model determining device.
  • the model determining device refer to the first model in the first embodiment of the method.
  • the model determining device may mainly include:
  • the data receiving unit 801 is configured to receive the object recognition model training sample data sent by the user terminal, where the object recognition model training sample data includes basic feature data of each object recognition model sample object, and each object recognition model sample
  • the base feature data of the object includes the object Identifying the use feature data of the model sample object in each of the third time periods, and the object use heat of the object recognition model sample object in each corresponding fourth time period; the third time period is the corresponding fourth time period The previous specified time period;
  • the model training unit 802 is configured to: according to the basic feature data of each object recognition model sample object included in the object recognition model training sample data, the pre-established use feature data for predicting the commodity object in the first time period, Training with the initial object recognition model of the relationship between the object objects in the second time period and the heat usage of the objects, obtaining the use feature data for characterizing the product object in the first time period, and the second time period with the product object
  • the object uses an object recognition model of the relationship between the heats; the first time period is a previous specified time period of the second time period.
  • the second embodiment of the present application further provides another model determining device.
  • the other model determining device refer to the related method in the first embodiment of the method. Another description of the method for determining the model is not repeated here.
  • the other model determining device may mainly include:
  • the data receiving unit 901 is configured to receive the category identification model training sample data sent by the user terminal, where the category identification model training sample data includes basic feature data of various target recognition model sample objects, and each class
  • the base feature data of the target model sample object includes the category corresponding to the category object of the sample identification model, and the fifth time period in the sample object selection method (in the foregoing, the fifth time period in the foregoing commodity object selection method;
  • the commodity object selection method may also represent the set sample time period as the first time period to distinguish the second time period mentioned later by the model determining device, and the category use heat is not described herein.
  • the category corresponding to the category identification model sample object uses heat in a category of one or more historical synchronization time periods corresponding to each set sample period;
  • the model training unit 902 is configured to: according to the category identification data of the category identification model sample objects included in the sample data of the category identification model, the pre-established class for predicting the commodity object category in the second time period.
  • the initial category identification of the relationship between the heat usage and the heat usage of the category of the commodity object category in one or more historical time periods corresponding to the second time period The model is trained to obtain a category used to characterize the category of the commodity object category in the second time period, and the category of the commodity object category in the one or more historical time periods corresponding to the second time period.
  • the second embodiment of the present application further provides a heat determining device, and the specific implementation of the heat determining device can be referred to the heat usage in the first embodiment of the method.
  • the heat determining device may mainly include:
  • the data obtaining unit 1001 is configured to obtain usage characteristic data of each commodity object in a first time period
  • the heat determining unit 1002 is configured to use an association relationship between the use feature data of the product object in the first time period and the object use heat of the product object in the second time period, and the product object in the first time period Using the feature data to determine the object usage heat of each commodity object in the second time period;
  • the first time period is a previous specified time period of the second time period; and the association relationship is based on the use feature data of each sample object in each third time period and each corresponding sample object in each corresponding The object of the four time period is established using the heat; the third time period is the previous specified time period of the corresponding fourth time period.
  • embodiments of the present application can be provided as a method, apparatus (device), or computer program product.
  • the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware.
  • the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Cash Registers Or Receiving Machines (AREA)

Abstract

L'invention concerne un procédé et un dispositif pour sélectionner un objet de produit, et déterminer un modèle et une popularité d'utilisation. L'invention peut sélectionner automatiquement, à partir d'un grand nombre d'objets, et sur la base d'un mot-clé associé à un objet de produit et entré par un utilisateur (10) et un modèle d'identification d'objet défini, au moins un objet de produit en tant qu'objet répondant à une exigence de l'utilisateur (10), ce qui permet d'améliorer de manière significative l'efficacité de sélection d'objets de produit, de réduire les coûts encourus lors de l'exécution du stockage par le personnel, et d'améliorer l'efficacité du fonctionnement.
PCT/CN2017/092588 2016-07-19 2017-07-12 Procédé et dispositif pour sélectionner un objet de produit, et déterminer un modèle et une popularité d'utilisation WO2018014764A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201610569600.9 2016-07-19
CN201610569600.9A CN107632989B (zh) 2016-07-19 2016-07-19 一种商品对象选取、模型确定及使用热度确定方法与装置

Publications (1)

Publication Number Publication Date
WO2018014764A1 true WO2018014764A1 (fr) 2018-01-25

Family

ID=60991980

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/092588 WO2018014764A1 (fr) 2016-07-19 2017-07-12 Procédé et dispositif pour sélectionner un objet de produit, et déterminer un modèle et une popularité d'utilisation

Country Status (3)

Country Link
CN (1) CN107632989B (fr)
TW (1) TW201816685A (fr)
WO (1) WO2018014764A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111259302A (zh) * 2020-01-19 2020-06-09 腾讯科技（深圳）有限公司 信息推送方法、装置及电子设备

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110309253A (zh) * 2018-03-01 2019-10-08 北京京东尚科信息技术有限公司 选品方法、装置和计算机可读存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012036736A1 (fr) * 2010-09-15 2012-03-22 Alibaba Group Holding Limited Génération de recommandations de produit
CN102591876A (zh) * 2011-01-14 2012-07-18 阿里巴巴集团控股有限公司 搜索结果排序方法及装置
CN104317945A (zh) * 2014-10-31 2015-01-28 亚信科技（南京）有限公司 一种基于搜索行为的电商网站商品推荐方法
CN104820879A (zh) * 2015-05-27 2015-08-05 北京京东尚科信息技术有限公司 一种用户行为信息的分析方法和装置
CN105630836A (zh) * 2014-11-05 2016-06-01 阿里巴巴集团控股有限公司 搜索结果的排序方法和装置

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103729359B (zh) * 2012-10-12 2017-03-01 阿里巴巴集团控股有限公司 一种推荐搜索词的方法及系统
CN105095210A (zh) * 2014-04-22 2015-11-25 阿里巴巴集团控股有限公司 一种筛选推广关键词的方法和装置
CN105096152A (zh) * 2014-05-20 2015-11-25 阿里巴巴集团控股有限公司 一种基于商品热度的操作执行方法及装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012036736A1 (fr) * 2010-09-15 2012-03-22 Alibaba Group Holding Limited Génération de recommandations de produit
CN102591876A (zh) * 2011-01-14 2012-07-18 阿里巴巴集团控股有限公司 搜索结果排序方法及装置
CN104317945A (zh) * 2014-10-31 2015-01-28 亚信科技（南京）有限公司 一种基于搜索行为的电商网站商品推荐方法
CN105630836A (zh) * 2014-11-05 2016-06-01 阿里巴巴集团控股有限公司 搜索结果的排序方法和装置
CN104820879A (zh) * 2015-05-27 2015-08-05 北京京东尚科信息技术有限公司 一种用户行为信息的分析方法和装置

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111259302A (zh) * 2020-01-19 2020-06-09 腾讯科技（深圳）有限公司 信息推送方法、装置及电子设备
CN111259302B (zh) * 2020-01-19 2023-04-07 深圳市雅阅科技有限公司 信息推送方法、装置及电子设备

Also Published As

Publication number Publication date
CN107632989B (zh) 2021-04-13
CN107632989A (zh) 2018-01-26
TW201816685A (zh) 2018-05-01

Similar Documents

Publication Publication Date Title
JP6134444B2 (ja) 情報を推薦するための方法およびシステム
CN102760163B (zh) 一种特征信息的个性化推荐方法及装置
CN107332910B (zh) 信息推送方法和装置
JP2020514860A (ja) アクション可能なウィジェットカード
CN107656969A (zh) 一种信息推荐方法及装置
CN107003996A (zh) 语音商务
CN106600302A (zh) 基于Hadoop的商品推荐系统
CN103886487A (zh) 基于分布式的b2b平台的个性化推荐方法与系统
WO2018126740A1 (fr) Dispositif et procédé destinés à pousser des informations
WO2018014771A1 (fr) Procédé, dispositif et système de poussée d'objet de données
CN106296208B (zh) 一种商品分类方法及装置
Khaskheli et al. M-commerce and mobile apps: opportunities for MSMEs in developing countries
CN102063453A (zh) 一种用于基于用户的需求进行搜索的方法和设备
WO2017019424A1 (fr) Procédés et systèmes pour classer des commerçants
US11947548B2 (en) Systems and methods for providing search results based on a primary intent
KR102068820B1 (ko) 온라인 마켓 상에서 상품의 등록 및 통합 관리를 수행하는 컴퓨터 프로그램 및 이의 동작 방법
KR102384728B1 (ko) 산지 경매가 기반의 농산물 공급자 중심 역경매 가격 산출 방법 및 시스템
WO2018014764A1 (fr) Procédé et dispositif pour sélectionner un objet de produit, et déterminer un modèle et une popularité d'utilisation
CN111967924A (zh) 商品推荐方法、商品推荐装置、计算机设备和介质
KR20190081671A (ko) 온라인 쇼핑몰 통합 관리 시스템에서의 유사상품을 검색하는 방법 및 그 서버
JP7078784B1 (ja) 提供装置、提供方法及び提供プログラム
CN107408229A (zh) 用于优化诸如旅游设施等的未充分使用的多个物理设施的使用的系统和方法
KR102026907B1 (ko) 로봇 저널리즘 장치 및 방법
KR20140122695A (ko) 초간단 인터넷 쇼핑몰 제작시스템 및 방법
CN112862553A (zh) 一种商品推荐的方法和装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17830398

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17830398

Country of ref document: EP

Kind code of ref document: A1