CN111767458A - Information pushing method, device, system and storage medium - Google Patents
Information pushing method, device, system and storage medium Download PDFInfo
- Publication number
- CN111767458A CN111767458A CN201910860486.9A CN201910860486A CN111767458A CN 111767458 A CN111767458 A CN 111767458A CN 201910860486 A CN201910860486 A CN 201910860486A CN 111767458 A CN111767458 A CN 111767458A
- Authority
- CN
- China
- Prior art keywords
- user
- item
- pushing
- article
- information
- Prior art date
- Legal status (The legal status 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 status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 64
- 238000003860 storage Methods 0.000 title claims abstract description 21
- 238000012549 training Methods 0.000 claims description 32
- 230000015654 memory Effects 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 15
- 238000005304 joining Methods 0.000 claims description 7
- 230000000694 effects Effects 0.000 abstract description 14
- 230000006399 behavior Effects 0.000 abstract description 13
- 238000006243 chemical reaction Methods 0.000 abstract description 13
- 238000013461 design Methods 0.000 description 14
- 230000008569 process Effects 0.000 description 10
- 238000010586 diagram Methods 0.000 description 8
- 238000013135 deep learning Methods 0.000 description 5
- 239000000284 extract Substances 0.000 description 5
- 241000252233 Cyprinus carpio Species 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 238000013136 deep learning model Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 230000004913 activation Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012552 review Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention provides an information pushing method, an information pushing device, an information pushing system and a storage medium, wherein the method comprises the following steps: acquiring basic data of a user; acquiring user characteristics according to the basic data; analyzing the article demand information of the user through a target learning model according to the user characteristics; and pushing the articles in the preset article library to the user according to the article demand information of the user. The method and the device can analyze the articles possibly required by the user according to the online behavior data of the user, so that accurate information pushing can be performed on the user according to the user requirement, the conversion effect of the information pushing is improved, and the user experience is good.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to an information pushing method, an information pushing apparatus, an information pushing system, and a storage medium.
Background
More and more user data on the internet are recorded, and the action data of searching, clicking, purchasing and the like of the user on a website reflect the interest and the demand of the user on products.
At present, a user actively browses a webpage and then selects a needed article from mass data.
However, this method is not suitable for rapid promotion of special price articles (for example, selling goods in a clear warehouse with broken codes), and users cannot know whether the special price articles have needed articles, thereby causing low promotion efficiency of the special price articles.
Disclosure of Invention
The invention provides an information pushing method, an information pushing device, an information pushing system and a storage medium, which can analyze articles possibly required by a user according to online behavior data of the user, so that accurate information pushing can be carried out on the user according to the user requirement, the conversion effect of the information pushing is improved, and the user experience is good.
In a first aspect, an embodiment of the present invention provides an information pushing method, including:
acquiring basic data of a user;
acquiring user characteristics according to the basic data;
analyzing the article demand information of the user through a target learning model according to the user characteristics;
and pushing the articles in the preset article library to the user according to the article demand information of the user.
In one possible design, the base data includes: item browsing records, historical order records, item records joining a shopping cart, item records adding attention.
In one possible design, the user features include: user representation, item features, context features, cross-dimension features.
In one possible design, further comprising:
constructing a training sample through the user characteristics;
taking a real ordering record as a target output result, and iteratively training the constructed initial learning model through the training sample until the error between the analysis result output by the trained initial learning model and the real ordering record is smaller than a preset threshold value; and obtaining the target learning model.
In one possible design, before recommending, to a user, an item in a preset item library according to the item requirement information of the user, the method further includes:
and adding the goods and/or special-value goods which are subjected to code breaking and warehouse clearing into the preset goods warehouse.
In one possible design, pushing the items in the preset item library to the user according to the item demand information of the user includes:
judging whether an article which is consistent with the article demand information of the user exists in the preset article library or not;
and if the articles which are consistent with the article demand information of the user exist, pushing the articles to the user terminal.
In a second aspect, an embodiment of the present invention provides an information pushing apparatus, including:
the first acquisition module is used for acquiring basic data of a user;
the second acquisition module is used for acquiring user characteristics according to the basic data;
the analysis module is used for analyzing the article demand information of the user through a target learning model according to the user characteristics;
and the pushing module is used for pushing the articles in the preset article library to the user according to the article demand information of the user.
In one possible design, the base data includes: item browsing records, historical order records, item records joining a shopping cart, item records adding attention.
In one possible design, the user features include: user representation, item features, context features, cross-dimension features.
In one possible design, further comprising: a training module to:
constructing a training sample through the user characteristics;
taking a real ordering record as a target output result, and iteratively training the constructed initial learning model through the training sample until the error between the analysis result output by the trained initial learning model and the real ordering record is smaller than a preset threshold value; and obtaining the target learning model.
In one possible design, further comprising:
and the adding module is used for adding the goods with broken codes and cleared bins and/or special-price goods into the preset goods warehouse.
In one possible design, the pushing module is specifically configured to:
judging whether an article which is consistent with the article demand information of the user exists in the preset article library or not;
and if the articles which are consistent with the article demand information of the user exist, pushing the articles to the user terminal.
In a third aspect, an embodiment of the present invention provides an information push system, including: the device comprises a memory and a processor, wherein the memory stores executable instructions of the processor; wherein the processor is configured to perform the information push method of any one of the first aspect via execution of the executable instructions.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the information pushing method according to any one of the first aspects.
In a fifth aspect, an embodiment of the present invention provides a program product, where the program product includes: a computer program stored in a readable storage medium, from which at least one processor of a server can read the computer program, the at least one processor executing the computer program causing the server to perform the information push method according to any one of the first aspect.
The invention provides an information pushing method, device, system and storage medium, which is characterized in that basic data of a user is obtained; acquiring user characteristics according to the basic data; analyzing the article demand information of the user through a target learning model according to the user characteristics; and recommending the articles in the preset article library to the user according to the article demand information of the user. The method and the device can analyze the articles possibly required by the user according to the online behavior data of the user, so that accurate information pushing can be performed on the user according to the user requirement, the conversion effect of the information pushing is improved, and the user experience is good.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of an application scenario of the present invention;
fig. 2 is a flowchart of an information pushing method according to an embodiment of the present invention;
fig. 3 is a flowchart of an information pushing method according to a second embodiment of the present invention;
FIG. 4 is a schematic flowchart of deep learning model training according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an information pushing apparatus according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of an information pushing apparatus according to a fourth embodiment of the present invention;
fig. 7 is a schematic structural diagram of an information push system according to a fifth embodiment of the present invention.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
To facilitate understanding of the technical solution, the terms appearing in the present invention are explained.
1) Clearing the warehouse with broken codes: the invention relates to a method for selling goods of an e-commerce platform, which is characterized in that the size of some brands is sold out quickly in the process of selling the goods, and the goods are gathered together to be sold at a high price in order to sell the remaining goods with the sizes in a short time.
2) Deep FM: an artificial intelligence algorithm extracts high-order features and low-order features for combined training, scores the ranking of articles and analyzes the articles the user likes.
3) Personalized pushing: the invention relates to a method for clearing articles in a warehouse from broken codes by combining with a Beijing east user portrait.
4) Deep learning: the multi-layer perceptron structure abstracts high-order representation by fitting low-order features and mines the rule of data distribution, and generally comprises an input layer, a hidden layer and an output layer.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
More and more user data on the internet are recorded, and the action data of searching, clicking, purchasing and the like of the user on a website reflect the interest and the demand of the user on products. At present, a user actively browses a webpage and then selects a needed article from mass data. However, this method is not suitable for rapid promotion of special price articles (for example, selling goods in a clear warehouse with broken codes), and users cannot know whether the special price articles have needed articles, thereby causing low promotion efficiency of the special price articles.
The defects of the prior art are as follows: although the goods for clearing the warehouse by the code breaking are cheap, the user needs to browse the needed goods from the massive goods, so that a lot of time is needed, and the order conversion rate is low. In view of the above technical problems, the present invention provides an information push method, apparatus, system and storage medium, which can analyze an item that may be needed by a user according to online behavior data of the user, so as to perform accurate recommendation to the user according to the user requirement, improve the conversion effect of information push, and provide good user experience. Fig. 1 is a schematic diagram of an application scenario of the present invention, as shown in fig. 1, in this embodiment, based on basic data (order record, browsing record, shopping cart record adding, user portrait, user preference, etc.) of a user and context information (weather condition, geographical location, etc.) of the user, a deep learning deep learning deep fm algorithm is used to intelligently recommend personalized documents and personalized articles to the user from a code-breaking warehouse, analyze articles that the user may addict to purchase every day, increase platform liveness in an interactive manner, and improve service evaluation indexes. Therefore, basic information of the user is firstly acquired, and the basic data comprises: item browsing records, historical order records, item records joining a shopping cart, item records adding attention. Then, through the basic data, user characteristics can be further mined to obtain the user characteristics, wherein the user characteristics comprise: user representation, item features, context features, cross-dimension features. According to basic data, the user portrait can be accurately positioned, such as the sex, age and purchasing power grade of a user, whether the user is a plus member or not, a preferred brand of the user and the like, and user characteristic engineering can be established in a fine-grained manner. Specifically, the user representation refers to characteristics of the user dimension, such as the user's gender, the user's age, the user's shopping interval, the user's resident address, the user's purchase level, whether the user is a plus member, the number of times the user has used a full discount coupon during a specified period, the number of categories in each order of the user, the average price in each order of the user, and the like. The item characteristics refer to the number of clicks on the item, the order quantity of the item, the brand of the item, the price of the item, the amount of interest of the item, the number of reviews of the item, and the like. The longitude and latitude, the season, the temperature difference and the weather of the city where the user is located, the searching, clicking, browsing, purchasing and ordering quantities under different network types (2G, 3G and 4G, wifi) and the like are stored in the Kyoto big data hive platform and extracted as the user context characteristics of the invention. The user's category preferences, the user's brand preferences, the user's item preferences, etc. are all used as cross-dimension features of the present invention. Then, the user's item demand information is analyzed through the frontier deep learning deep FM algorithm. Wherein, the Embedding network layer maps the original features input by the user to a low latitude dense vector. The FM network layer extracts the low-order characteristics of the user on the basis of Embedding. The DNN network layer extracts high-level abstract features of users on the basis of Embedding. The joint training means that low-order and high-order characteristics of a user are input into an activation function or loss, and parameters of the network are updated through back propagation. Finally, whether an article which is consistent with the article demand information of the user exists in the preset article library or not can be judged; and if the articles which are consistent with the article demand information of the user exist, pushing the information to the user terminal. And personalized pushing can be carried out, such as brand personalized pushing, clothing personalized pushing, price segment personalized pushing and the like.
By the method, the articles possibly required by the user can be analyzed according to the online behavior data of the user, so that the articles can be accurately recommended to the user according to the user requirements, the conversion effect of information pushing is improved, and the user experience is good.
The following describes the technical solutions of the present invention and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of an information pushing method according to an embodiment of the present invention, and as shown in fig. 2, the method in this embodiment may include:
s101, acquiring basic data of a user.
In the prior art, the goods in the warehouse are cleared in a broken code mode, personalized pushing is not carried out, all the goods are displayed to all users in a generalized mode, and recommendation is not targeted. The deep shopping demand that the user hidden can fully be excavated to this embodiment, and thousand people's thousand faces push disconnected sign indicating number clear storehouse article, have accelerated platform freight flow speed on the one hand, and on the other hand has brought the actual discount for the user. Therefore, basic information of the user is firstly acquired, and the basic data comprises: item browsing records, historical order records, item records joining a shopping cart, item records adding attention.
Specifically, the historical order refers to behavior data of a user purchasing an article, is stored in a hive table of a big data platform, and is extracted to serve as basic data of the invention. The browsing record refers to behavior data of the user browsing the articles, and the behavior data is stored in a hive table of the big data platform and extracted to be used as basic data of the invention. The shopping record refers to the item record of the user in the shopping cart, and the hive standard is stored in the big data platform and extracted as the basic data of the invention. The concerned records refer to behavior records of the concerned articles of the user, are stored in the hive standard of the big data platform and are extracted as basic data of the invention. Through the basic data, user characteristics can be further mined.
And S102, acquiring user characteristics according to the basic data.
In this embodiment, the user characteristics may be obtained according to the basic data. Wherein the user characteristics include: user representation, item features, context features, cross-dimension features. According to basic data, the user portrait can be accurately positioned, such as the sex, age and purchasing power grade of a user, whether the user is a plus member or not, a preferred brand of the user and the like, and user characteristic engineering can be established in a fine-grained manner.
Specifically, the user representation refers to characteristics of the user dimension, such as the user's gender, the user's age, the user's shopping interval, the user's resident address, the user's purchase level, whether the user is a plus member, the number of times the user has used a full discount coupon during a specified period, the number of categories in each order of the user, the average price in each order of the user, and the like. The item characteristics refer to the number of clicks on the item, the order quantity of the item, the brand of the item, the price of the item, the amount of interest of the item, the number of reviews of the item, and the like. The longitude and latitude, the season, the temperature difference and the weather of the city where the user is located, the searching, clicking, browsing, purchasing and ordering quantities under different network types (2G, 3G and 4G, wifi) and the like are stored in the Kyoto big data hive platform and extracted as the user context characteristics of the invention. The user's category preferences, the user's brand preferences, the user's item preferences, etc. are all used as cross-dimension features of the present invention.
And S103, analyzing the article demand information of the user through a target learning model according to the user characteristics.
In the embodiment, the article demand information of the user can be analyzed through the target learning model according to the characteristics of the user, so that the potential consumption demand of the user can be judged.
Specifically, the user's item demand information is analyzed by the frontier deep learning deep fm algorithm. First, the Embedding network layer maps the original features input by the user to a low latitude dense vector. The FM network layer extracts the low-order characteristics of the user on the basis of Embedding. The DNN network layer extracts high-level abstract features of users on the basis of Embedding. The joint training means that low-order and high-order characteristics of a user are input into an activation function or loss, and parameters of the network are updated through back propagation. The training process of the target learning model will be described in detail later.
And S104, pushing the articles in the preset article library to the user according to the article demand information of the user.
In this embodiment, it may be determined whether an item that matches the item demand information of the user exists in the preset item library; and if the articles which are consistent with the article demand information of the user exist, pushing the information to the user terminal.
Specifically, a clear warehouse is taken as a preset goods warehouse. The personalized pushing of the clothes can be carried out. For example, a men XL size half sleeve is in the spot size warehouse, and at this time, the half sleeve is pushed to a user who purchased the men XL half sleeve in combination with a user portrait of the kyoto. And when a user logs in the APP, a poster ' half-sleeve fire heat doing activities interesting to the user ' is popped up ', if discount offers are added to the demand, the purchasing possibility is much higher than that of the user who is shown to all users, and the personalized pushing accelerates the goods flow of the E-commerce platform on one hand and brings real benefits to the user who really wants to purchase on the other hand. Price segment personalized pushing can also be carried out. For example, a pair of Adedas shoes is arranged in a code breaking warehouse, the shoes are pushed to a user with a high price range by combining with a user portrait of the Kyoto, and if the shoes are a pair of miscellaneous shoes, the shoes are pushed to a common user with a low price range, and the personalized consumption pushing is beneficial to the user and a platform to realize mutual benefits and win-win. In order to stimulate the shopping desire of the user to the maximum extent, the invention sets up a lucky fancy carp card prize, calculates the score of each user participating in the code-breaking warehouse cleaning activity by using the following formula, and obtains the preferential welfare of the lucky fancy carp with the highest score.
Lucky carp welfare is w1*f1+w2*f2+w3*f3+w4*f4+
Wherein: w is a1Weight representing the amount of the order, f1Indicates the amount of the order, w2Weight representing number of views, f2Indicates the number of views, w3Weight representing number of interest in code-breaking bin clearing, f3Representing the number of times of interest for code breaking and bin clearing, w4Weight representing weather, f4And the conversion numerical value corresponding to the weather represents a basic parameter, and the multiplication operation is represented.
In the embodiment, basic data of a user is obtained; acquiring user characteristics according to the basic data; analyzing the article demand information of the user through a target learning model according to the user characteristics; and recommending the articles in the preset article library to the user according to the article demand information of the user. The method and the device can analyze the articles possibly required by the user according to the online behavior data of the user, so that accurate information pushing can be performed on the user according to the user requirement, the conversion effect of the information pushing is improved, and the user experience is good.
Fig. 3 is a flowchart of an information pushing method provided in the second embodiment of the present invention, and as shown in fig. 3, the method in this embodiment may include:
s201, constructing a training sample and an initial learning model, and performing iterative training on the initial learning model through the training sample to obtain a target learning model.
In the embodiment, a training sample can be constructed through user characteristics; taking a real ordering record as a target output result, and iteratively training the constructed initial learning model through a training sample until the error between the analysis result output by the trained initial learning model and the real ordering record is smaller than a preset threshold value; and obtaining the target learning model.
Specifically, fig. 4 is a schematic flow chart of deep learning model training provided by an embodiment of the present invention, and as shown in fig. 4, the deep learning model is composed of an input layer, an output layer and some hidden layers sandwiched therebetween, similar to a multi-layer perceptron, but there are many hidden layers, each layer has a plurality of connected neurons, the hidden layers are connected with the input layer, they combine and weight the input values to generate new actual values, which are then transferred to the output layer, and the output layer uses abstract features calculated in the hidden layers for classification or analysis.
S202, adding the goods and/or special-value goods which are cleared in the bin with broken codes into a preset goods warehouse.
In this embodiment, the articles cleared by the broken codes and/or the special-value articles can be added to the preset article library. The goods and/or special price goods with broken codes and cleared in the warehouse can be added into the preset goods warehouse manually, and a mechanism can be established to automatically add the goods and/or special price goods with broken codes and cleared in the preset goods warehouse. For example, the automatic processing is carried out by using the code-breaking warehouse clearing article recalling. The code-breaking warehouse clearing article recall refers to that goods which are in shortage of the size of a sold article pool are recalled to a unified code-breaking warehouse by an e-commerce platform by applying a collaborative filtering and strategy statistical method so as to conduct activity sale at a more favorable price.
S203, acquiring basic data of the user.
And S204, acquiring user characteristics according to the basic data.
And S205, analyzing the article demand information of the user through a target learning model according to the user characteristics.
S206, pushing the articles in the preset article library to the user according to the article demand information of the user.
In this embodiment, please refer to the relevant description in steps S101 to S104 in the method shown in fig. 2 for the specific implementation process and technical principle of steps S203 to S206, which is not described herein again.
In the embodiment, basic data of a user is obtained; acquiring user characteristics according to the basic data; analyzing the article demand information of the user through a target learning model according to the user characteristics; and recommending the articles in the preset article library to the user according to the article demand information of the user. The method and the device can analyze the articles possibly required by the user according to the online behavior data of the user, so that accurate information pushing can be performed on the user according to the user requirement, the conversion effect of the information pushing is improved, and the user experience is good.
In addition, the embodiment can also add the goods and/or special price goods with broken codes and cleared bins into the preset goods warehouse; and constructing a training sample and an initial learning model, and performing iterative training on the initial learning model through the training sample to obtain a target learning model. And accurate recommendation can be carried out on the user according to the user requirements, the conversion effect of information pushing is improved, and the user experience is good.
Fig. 5 is a schematic structural diagram of an information pushing apparatus according to a third embodiment of the present invention, and as shown in fig. 5, the information pushing apparatus according to the third embodiment may include:
a first obtaining module 31, configured to obtain basic data of a user;
a second obtaining module 32, configured to obtain a user characteristic according to the basic data;
the analysis module 33 is used for analyzing the article demand information of the user through the target learning model according to the user characteristics;
and the pushing module 34 is configured to push the items in the preset item library to the user according to the item demand information of the user.
In one possible design, the base data includes: item browsing records, historical order records, item records joining a shopping cart, item records adding attention.
In one possible design, the user features include: user representation, item features, context features, cross-dimension features.
In one possible design, the recommendation module 34 is specifically configured to:
judging whether an article which is consistent with the article demand information of the user exists in a preset article library or not;
and if the articles which are consistent with the article demand information of the user exist, pushing the articles to the user terminal.
The information pushing apparatus of this embodiment may execute the technical solution in the method shown in fig. 2, and for the specific implementation process and the technical principle, reference is made to the relevant description in the method shown in fig. 2, which is not described herein again.
In the embodiment, basic data of a user is obtained; acquiring user characteristics according to the basic data; analyzing the article demand information of the user through a target learning model according to the user characteristics; and recommending the articles in the preset article library to the user according to the article demand information of the user. The method and the device can analyze the articles possibly required by the user according to the online behavior data of the user, so that accurate information pushing can be performed on the user according to the user requirement, the conversion effect of the information pushing is improved, and the user experience is good.
Fig. 6 is a schematic structural diagram of an information pushing apparatus according to a fourth embodiment of the present invention, as shown in fig. 6, the information pushing apparatus according to the present embodiment may further include, on the basis of the apparatus shown in fig. 5:
the training module 35 is specifically configured to:
constructing a training sample through the user characteristics;
taking a real ordering record as a target output result, and iteratively training the constructed initial learning model through a training sample until the error between the analysis result output by the trained initial learning model and the real ordering record is smaller than a preset threshold value; and obtaining the target learning model.
In one possible design, further comprising:
and the adding module 36 is used for adding the goods cleared by the broken codes and/or the special-value goods into a preset goods warehouse.
The information pushing apparatus of this embodiment may execute the technical solutions in the methods shown in fig. 2 and fig. 3, and the specific implementation process and technical principle of the information pushing apparatus refer to the related descriptions in the methods shown in fig. 2 and fig. 3, which are not described herein again.
In the embodiment, basic data of a user is obtained; acquiring user characteristics according to the basic data; analyzing the article demand information of the user through a target learning model according to the user characteristics; and recommending the articles in the preset article library to the user according to the article demand information of the user. The method and the device can analyze the articles possibly required by the user according to the online behavior data of the user, so that accurate information pushing can be performed on the user according to the user requirement, the conversion effect of the information pushing is improved, and the user experience is good.
In addition, the embodiment can also add the goods and/or special price goods with broken codes and cleared bins into the preset goods warehouse; and constructing a training sample and an initial learning model, and performing iterative training on the initial learning model through the training sample to obtain a target learning model. And accurate recommendation can be carried out on the user according to the user requirements, the conversion effect of information pushing is improved, and the user experience is good.
Fig. 7 is a schematic structural diagram of an information push system according to a fifth embodiment of the present invention, and as shown in fig. 7, the information push system 40 according to this embodiment may include: a processor 41 and a memory 42.
A memory 42 for storing programs; the Memory 42 may include a volatile Memory (RAM), such as a Random Access Memory (SRAM), a Double Data Rate Synchronous Dynamic Random Access Memory (DDR SDRAM), and the like; the memory may also comprise a non-volatile memory, such as a flash memory. The memory 42 is used to store computer programs (e.g., applications, functional modules, etc. that implement the above-described methods), computer instructions, etc., which may be stored in one or more of the memories 42 in a partitioned manner. And the above-mentioned computer program, computer instructions, data, etc. can be called by the processor 41.
The computer programs, computer instructions, etc. described above may be stored in one or more memories 42 in partitions. And the above-mentioned computer program, computer instructions, data, etc. can be called by the processor 41.
A processor 41 for executing the computer program stored in the memory 42 to implement the steps of the method according to the above embodiments.
Reference may be made in particular to the description relating to the preceding method embodiment.
The processor 41 and the memory 42 may be separate structures or may be integrated structures integrated together. When the processor 41 and the memory 42 are separate structures, the memory 42 and the processor 41 may be coupled by a bus 43.
The information push system of this embodiment may execute the technical solutions in the methods shown in fig. 2 and fig. 3, and the specific implementation process and technical principle of the information push system refer to the related descriptions in the methods shown in fig. 2 and fig. 3, which are not described herein again.
In addition, embodiments of the present application further provide a computer-readable storage medium, in which computer-executable instructions are stored, and when at least one processor of the user equipment executes the computer-executable instructions, the user equipment performs the above-mentioned various possible methods.
Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. Additionally, the ASIC may reside in user equipment. Of course, the processor and the storage medium may reside as discrete components in a communication device.
The present application further provides a program product comprising a computer program stored in a readable storage medium, from which the computer program can be read by at least one processor of a server, the execution of the computer program by the at least one processor causing the server to carry out the method of any of the embodiments of the invention described above.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An information pushing method, comprising:
acquiring basic data of a user;
acquiring user characteristics according to the basic data;
analyzing the article demand information of the user through a target learning model according to the user characteristics;
and pushing the articles in the preset article library to the user according to the article demand information of the user.
2. The method of claim 1, wherein the base data comprises: item browsing records, historical order records, item records joining a shopping cart, item records adding attention.
3. The method of claim 1, wherein the user characteristics comprise: user representation, item features, context features, cross-dimension features.
4. The method of claim 1, further comprising:
constructing a training sample through the user characteristics;
taking a real ordering record as a target output result, and iteratively training the constructed initial learning model through the training sample until the error between the analysis result output by the trained initial learning model and the real ordering record is smaller than a preset threshold value; and obtaining the target learning model.
5. The method according to claim 1, before recommending the items in the preset item library to the user according to the item demand information of the user, further comprising:
and adding the goods and/or special-value goods which are subjected to code breaking and warehouse clearing into the preset goods warehouse.
6. The method according to claim 5, wherein pushing the items in the preset item library to the user according to the item demand information of the user comprises:
judging whether an article which is consistent with the article demand information of the user exists in the preset article library or not;
and if the articles which are consistent with the article demand information of the user exist, pushing the articles to the user terminal.
7. An information pushing apparatus, comprising:
the first acquisition module is used for acquiring basic data of a user;
the second acquisition module is used for acquiring user characteristics according to the basic data;
the analysis module is used for analyzing the article demand information of the user through a target learning model according to the user characteristics;
and the pushing module is used for pushing the articles in the preset article library to the user according to the article demand information of the user.
8. The apparatus of claim 7, wherein the base data comprises: item browsing records, historical order records, item records joining a shopping cart, item records adding attention.
9. An information push system, comprising: the memory is used for storing executable instructions of the processor; wherein the processor is configured to perform the information push method of claims 1-6 via execution of the executable instructions.
10. A computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the information pushing method according to claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910860486.9A CN111767458A (en) | 2019-09-11 | 2019-09-11 | Information pushing method, device, system and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910860486.9A CN111767458A (en) | 2019-09-11 | 2019-09-11 | Information pushing method, device, system and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111767458A true CN111767458A (en) | 2020-10-13 |
Family
ID=72718280
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910860486.9A Pending CN111767458A (en) | 2019-09-11 | 2019-09-11 | Information pushing method, device, system and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111767458A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112288540A (en) * | 2020-11-02 | 2021-01-29 | 北京每日优鲜电子商务有限公司 | Item customization information pushing method and device, electronic equipment and readable medium |
| CN113076482A (en) * | 2021-04-23 | 2021-07-06 | 支付宝(杭州)信息技术有限公司 | Business information pushing method, device, system, computer equipment and storage medium |
| CN113793180A (en) * | 2021-09-15 | 2021-12-14 | 北京沃东天骏信息技术有限公司 | User preference analysis method, device, equipment and computer storage medium |
| CN114697889A (en) * | 2020-12-31 | 2022-07-01 | 广州三星通信技术研究有限公司 | Method and device for processing information |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009079928A1 (en) * | 2007-12-18 | 2009-07-02 | Xiwei Tang | A method and system of electronic business application |
| CN103400286A (en) * | 2013-08-02 | 2013-11-20 | 世纪禾光科技发展（北京）有限公司 | Recommendation system and method for user-behavior-based article characteristic marking |
| CN106326375A (en) * | 2016-08-15 | 2017-01-11 | 合肥华凌股份有限公司 | Commodity recommending method, commodity recommending device and refrigerator |
| CN109102127A (en) * | 2018-08-31 | 2018-12-28 | 杭州贝购科技有限公司 | Method of Commodity Recommendation and device |
| CN109509054A (en) * | 2018-09-30 | 2019-03-22 | 平安科技（深圳）有限公司 | Method of Commodity Recommendation, electronic device and storage medium under mass data |
| CN109829775A (en) * | 2018-12-03 | 2019-05-31 | 苏州大学 | A kind of item recommendation method, device, equipment and readable storage medium storing program for executing |
-
2019
- 2019-09-11 CN CN201910860486.9A patent/CN111767458A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009079928A1 (en) * | 2007-12-18 | 2009-07-02 | Xiwei Tang | A method and system of electronic business application |
| CN103400286A (en) * | 2013-08-02 | 2013-11-20 | 世纪禾光科技发展（北京）有限公司 | Recommendation system and method for user-behavior-based article characteristic marking |
| CN106326375A (en) * | 2016-08-15 | 2017-01-11 | 合肥华凌股份有限公司 | Commodity recommending method, commodity recommending device and refrigerator |
| CN109102127A (en) * | 2018-08-31 | 2018-12-28 | 杭州贝购科技有限公司 | Method of Commodity Recommendation and device |
| CN109509054A (en) * | 2018-09-30 | 2019-03-22 | 平安科技（深圳）有限公司 | Method of Commodity Recommendation, electronic device and storage medium under mass data |
| CN109829775A (en) * | 2018-12-03 | 2019-05-31 | 苏州大学 | A kind of item recommendation method, device, equipment and readable storage medium storing program for executing |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112288540A (en) * | 2020-11-02 | 2021-01-29 | 北京每日优鲜电子商务有限公司 | Item customization information pushing method and device, electronic equipment and readable medium |
| CN114697889A (en) * | 2020-12-31 | 2022-07-01 | 广州三星通信技术研究有限公司 | Method and device for processing information |
| CN113076482A (en) * | 2021-04-23 | 2021-07-06 | 支付宝(杭州)信息技术有限公司 | Business information pushing method, device, system, computer equipment and storage medium |
| CN113793180A (en) * | 2021-09-15 | 2021-12-14 | 北京沃东天骏信息技术有限公司 | User preference analysis method, device, equipment and computer storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111784455B (en) | Article recommendation method and recommendation equipment | |
| CN111767458A (en) | Information pushing method, device, system and storage medium | |
| CN107871244B (en) | Method and device for detecting advertising effect | |
| CN109299994B (en) | Recommendation method, device, equipment and readable storage medium | |
| Sudirjo et al. | The Application of Extended Expectation-Confirmation Model to Identify Influencing Factors Digital Loyalty for Mobile-Based Travel Platform | |
| CN108805598B (en) | Similarity information determination method, server and computer-readable storage medium | |
| CN107016587B (en) | Personalized page pushing method and device | |
| CN108230009B (en) | User preference prediction method and device and electronic equipment | |
| CN106202516A (en) | A kind of e-commerce platform merchandise display method according to timing node | |
| CN103345695A (en) | Commodity recommendation method and device | |
| US20170186065A1 (en) | System and Method of Product Selection for Promotional Display | |
| CA2846025A1 (en) | Recommendations based upon explicit user similarity | |
| CN112435067A (en) | Intelligent advertisement putting method and system for cross-e-commerce platform and social platform | |
| CN113744016B (en) | Object recommendation method and device, equipment and storage medium | |
| CN110969512B (en) | Commodity recommendation method and device based on user purchasing behavior | |
| JP6976207B2 (en) | Information processing equipment, information processing methods, and programs | |
| CN112001754A (en) | User portrait generation method, device, equipment and computer readable medium | |
| CN107016006B (en) | Page display method and system | |
| JPWO2005024689A1 (en) | Method and apparatus for analyzing consumer purchasing behavior | |
| CN108305181B (en) | Social influence determination method and device, information delivery method and device, equipment and storage medium | |
| CN112579876A (en) | Information pushing method, device and system based on user interest and storage medium | |
| CN110602532A (en) | Entity article recommendation method, device, server and storage medium | |
| CN103843026B (en) | Information processor, information processing method | |
| CN111680213B (en) | Information recommendation method, data processing method and device | |
| CN111177581A (en) | Multi-platform-based social e-commerce website commodity recommendation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |