CN110570271A - information recommendation method and device, electronic equipment and readable storage medium - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000006243 chemical reaction Methods 0.000 claims description 29
- 238000012163 sequencing technique Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000027326 copulation Effects 0.000 claims description 4
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- 230000008569 process Effects 0.000 description 11
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- 241000209094 Oryza Species 0.000 description 4
- 235000007164 Oryza sativa Nutrition 0.000 description 4
- 235000009566 rice Nutrition 0.000 description 4
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- G06Q30/00—Commerce
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Abstract
The embodiment of the disclosure provides an information recommendation method, an information recommendation device, an electronic device and a readable storage medium, wherein the method comprises the following steps: determining an average consumption interval of a user according to an order amount of a historical order generated by the user aiming at historical recommendation information in a preset time period; performing transaction spread statistics on different average consumption intervals of the preset time period, and taking the average consumption interval with the transaction spread satisfying a preset spread condition as a reference consumption interval; determining a consumption type corresponding to the user according to the reference consumption interval and the average consumption interval corresponding to the user; determining a sorting mode corresponding to the user according to the consumption type of the user; and sorting the candidate recommendation information corresponding to the user according to the sorting mode to obtain the target recommendation information corresponding to the user. The embodiment of the disclosure can improve the accuracy of information recommendation.
Description
Technical Field
Embodiments of the present disclosure relate to the field of network technologies, and in particular, to an information recommendation method and apparatus, an electronic device, and a readable storage medium.
background
With the rapid development of informatization, information provided by the internet to users is increased explosively, the demands of users are increasing day by day, and how to enable users to timely and accurately acquire required information from massive information becomes a problem which needs to be solved urgently.
At present, an e-commerce recommendation system usually estimates the probability of commodity clicking or ordering by a user according to the historical behaviors and preferences of the user, and then sorts and displays commodities according to the probability.
However, for the emerging take-out industry, because one order of a user may include a plurality of different dishes, some users pay attention to the quality of the dishes, some users pay attention to the price of the dishes, if the order is sorted according to the quality, most recommended merchants with high end and high quality are provided, and for some users, the price is too high to place an order, and the user needs cannot be met; if the dishes are sorted according to the prices, users with high quality requirements cannot be met, and the dish recommending accuracy is low.
Disclosure of Invention
The embodiment of the disclosure provides an information recommendation method and device, an electronic device and a readable storage medium, which are used for improving the accuracy of information recommendation.
According to a first aspect of embodiments of the present disclosure, there is provided an information recommendation method, the method including:
Determining an average consumption interval of a user according to an order amount of a historical order generated by the user aiming at historical recommendation information in a preset time period;
Performing transaction spread statistics on different average consumption intervals of the preset time period, and taking the average consumption interval with the transaction spread satisfying a preset spread condition as a reference consumption interval;
determining a consumption type corresponding to the user according to the reference consumption interval and the average consumption interval corresponding to the user;
determining a sorting mode corresponding to the user according to the consumption type of the user;
and sorting the candidate recommendation information corresponding to the user according to the sorting mode to obtain the target recommendation information corresponding to the user.
according to a second aspect of embodiments of the present disclosure, there is provided an information recommendation apparatus including:
the first interval determining module is used for determining an average consumption interval of a user according to the order amount of a historical order generated by the user aiming at historical recommendation information in a preset time period;
the second interval determining module is used for carrying out transaction spread statistics on different average consumption intervals in the preset time period and taking the average consumption interval with the transaction spread meeting the preset spread condition as a reference consumption interval;
A consumption type determining module, configured to determine a consumption type corresponding to the user according to the reference consumption interval and the average consumption interval corresponding to the user;
the sequencing mode determining module is used for determining a sequencing mode corresponding to the user according to the consumption type of the user;
and the target information determining module is used for sequencing the candidate recommendation information corresponding to the user according to the sequencing mode to obtain the target recommendation information corresponding to the user.
according to a third aspect of embodiments of the present disclosure, there is provided an electronic apparatus including:
Processor, memory and computer program stored on the memory and executable on the processor, characterized in that the processor implements the aforementioned information recommendation method when executing the program.
According to a fourth aspect of embodiments of the present disclosure, there is provided a readable storage medium, wherein instructions, when executed by a processor of an electronic device, enable the electronic device to perform the aforementioned information recommendation method.
the embodiment of the disclosure provides an information recommendation method, an information recommendation device, an electronic device and a readable storage medium, wherein the method comprises the following steps:
Determining an average consumption interval of a user according to an order amount of a historical order generated by the user aiming at historical recommendation information in a preset time period; performing transaction spread statistics on different average consumption intervals of the preset time period, and taking the average consumption interval with the transaction spread satisfying a preset spread condition as a reference consumption interval; determining a consumption type corresponding to the user according to the reference consumption interval and the average consumption interval corresponding to the user; determining a sorting mode corresponding to the user according to the consumption type of the user; and sorting the candidate recommendation information corresponding to the user according to the sorting mode to obtain the target recommendation information corresponding to the user.
According to the embodiment of the disclosure, different recommendation sorting modes can be selected for users with different consumption types, so that the users with different consumption types can obtain recommendation information with different requirements, and the accuracy of information recommendation can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments of the present disclosure will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 shows a flow chart of steps of an information recommendation method in one embodiment of the present disclosure;
FIG. 2 shows a block diagram of an information recommendation device in one embodiment of the present disclosure;
Fig. 3 shows a block diagram of an electronic device provided by an embodiment of the present disclosure.
Detailed Description
Technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present disclosure, belong to the protection scope of the embodiments of the present disclosure.
Example one
Referring to fig. 1, a flow chart illustrating steps of an information recommendation method in one embodiment of the present disclosure includes:
Step 101, determining an average consumption interval of a user according to an order amount of a historical order generated by the user aiming at historical recommendation information in a preset time period;
102, carrying out transaction spread statistics on different average consumption intervals in the preset time period, and taking the average consumption interval with the transaction spread satisfying a preset spread condition as a reference consumption interval;
Step 103, determining a consumption type corresponding to the user according to the reference consumption interval and the average consumption interval corresponding to the user;
104, determining a sorting mode corresponding to the user according to the consumption type of the user;
And 105, sorting the candidate recommendation information corresponding to the user according to the sorting mode to obtain the target recommendation information corresponding to the user.
The information recommendation method provided by the embodiment of the disclosure can be applied to a terminal, and the terminal specifically includes but is not limited to: smart phones, tablet computers, electronic book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop portable computers, car-mounted computers, desktop computers, set-top boxes, smart televisions, wearable devices, and the like.
The information in the embodiment of the present disclosure includes any information that can be acquired by the user through the terminal, and may be any information such as commodity information, store information, dish information, news information, and entertainment. The embodiment of the disclosure mainly explains the dish information in the take-away ordering scene, and information recommendation processes in other application scenes can be referred to each other.
the recommendation information of the embodiment of the present disclosure may be a store, or a dish combination or a package that the store can provide. For example, the recommendation information may include: the dish combination of 'fish-flavor shredded pork + Ma-Po bean curd + rice' of the store A, the hamburger set of the store B and the like.
Before recommending information to a user, the consumption type of the user is determined according to the average consumption interval of the user and the reference consumption interval, for example, whether the user belongs to a high consumption type or a low consumption type is determined. Different recommendation sorting modes can be selected for users with different consumption types. For example, for a high consumption type user, recommendation information with higher quality and relatively higher price may be ranked ahead. For users with low consumption, the recommendation information with higher performance and lower price can be ranked in front. Therefore, users with different consumption types can obtain recommendation information with different requirements, and the accuracy of information recommendation can be improved.
The average consumption interval of the users is used for representing the average consumption level of a single user in a preset time period, and the reference consumption interval is used for representing the average consumption level of a common user in the preset time period.
In the embodiment of the present disclosure, in the case of receiving a recommendation request from a user, candidate recommendation information (such as a dish combination) that meets a distribution condition of the current location of the user, meets a historical behavior habit of the user, and can be placed once by the user can be obtained according to the current location of the user, user identification, and other user information. The recommendation request can be triggered passively through the search behavior of the user or actively when the user browses the information of the take-away merchant.
In the embodiment of the disclosure, the dish combination is recommended to the user as a whole, for example, if the combination only contains "steamed bread + rice", after the user orders the combination, the user needs to additionally order other dishes, so that the dish combination "steamed bread + rice" cannot meet the demand of ordering the user at one time, and the dish combination "rice + tomato-fried egg" can meet the demand of ordering the user at one time.
According to the embodiment of the disclosure, the candidate dish combination corresponding to the recommendation request can be obtained through a plurality of sources, so that the candidate dish combination can meet the requirement of a user for placing an order at one time. The plurality of sources refer to sources for acquiring candidate dish combinations, and specifically may include completed order information in the historical behavior data of the user, dish combination order information of the candidate merchants, and dish information provided by the candidate merchants. Of course, the information of orders already completed by other users, package information collocated through an intelligent algorithm, and the like can also be included. It is to be understood that embodiments of the present disclosure do not impose limitations on the source from which the candidate dish combinations are obtained.
in an optional embodiment of the present disclosure, the determining, according to the reference consumption interval and the average consumption interval corresponding to the user, a consumption type corresponding to the user may specifically include: if the average consumption interval corresponding to the user is higher than the reference consumption interval, determining that the consumption type corresponding to the user is a high consumption type; otherwise, determining the consumption type corresponding to the user as a low consumption type.
In the embodiment of the disclosure, the average consumption interval of the user is determined according to the order amount of the historical orders generated by the user for the historical recommendation information in the preset time period, and if the average consumption interval of the user is higher than the reference consumption interval, it is indicated that the order amount of the historical orders of the user is higher than the average level of the general users, and therefore, the user belongs to a high consumption type, otherwise, the user belongs to a low consumption type.
in an optional embodiment of the present disclosure, the determining, according to the consumption type of the user, a ranking manner corresponding to the user may specifically include:
if the consumption type of the user is a high consumption type, determining that the ordering mode corresponding to the user is as follows: sorting the candidate recommendation information corresponding to the user according to the estimated ordering amount corresponding to the user; or
if the consumption type of the user is a low consumption type, determining that the ordering mode corresponding to the user is as follows: and sequencing the candidate recommendation information corresponding to the user according to the estimated ordering conversion rate corresponding to the candidate recommendation information.
The information recommendation method provided by the embodiment of the disclosure aims to provide a new sequencing mechanism, which can simultaneously take account of the single average price and the conversion rate of a user group. For the users with high consumption types, the users pay more attention to consumption quality rather than price, so the candidate recommendation information can be ranked according to the estimated order amount corresponding to the users.
In a take-away scenario, a user search or order placement is typically at store granularity, with one order typically corresponding to one store. The estimated ordering amount in the embodiment of the disclosure refers to the amount of the ordering possibly made by the estimated user to a certain store, if the user is a high-consumption type user, the candidate recommendation information can be ranked according to the estimated ordering amount corresponding to the user, and the candidate recommendation information with the estimated ordering amount is ranked in front, so as to improve the quality of the recommendation information. For the users with low consumption types, the quality requirements are not too high because the users pay more attention to the consumption amount, so the candidate recommendation information can be ranked according to the estimated ordering conversion rate corresponding to the candidate recommendation information. The estimated ordering conversion rate refers to the conversion probability from displaying of the estimated candidate recommendation information to ordering of the user, and the candidate recommendation information with high ordering conversion rate is ranked in front so as to improve the user conversion rate. Therefore, the purposes of improving consumption quality, improving user conversion rate and increasing user viscosity of the whole user group are achieved.
in an optional embodiment of the present disclosure, the estimated order amount corresponding to the user is determined according to an order amount corresponding to a historical order of the user; and the estimated ordering conversion rate corresponding to the candidate recommendation information is determined according to the historical conversion rate generated from displaying to the user to ordering the user according to the candidate recommendation information.
in the embodiment of the present disclosure, the estimated ordering amount and the estimated ordering conversion rate may be obtained by model prediction. Specifically, a money-placing amount prediction model for predicting a money-placing amount of the user, and a conversion rate prediction model for predicting a conversion rate of the money-placing amount may be trained in advance.
In this way, in the information recommendation process, candidate recommendation information corresponding to a user can be obtained, the consumption type of the user is determined, if the user is determined to be of a high consumption type, the user information of the user and store information corresponding to the candidate recommendation information can be input into an order-placing amount prediction model to output estimated order-placing amount of the user for stores corresponding to the candidate recommendation information, the candidate recommendation information is ranked from high to low according to the estimated order-placing amount corresponding to the store where the candidate recommendation information is located, and the candidate recommendation information of N (N is a positive integer) before ranking is used as target recommendation information to be displayed to the user.
if the user is determined to be of the low consumption type, the user information of the user and the candidate recommendation information can be input into a conversion rate prediction model to output the conversion probability (estimated ordering conversion rate) corresponding to ordering of each candidate recommendation information from the presentation to the user, all the candidate recommendation information is ranked from high to low according to the corresponding estimated ordering conversion rate, and the candidate recommendation information of N before ranking is used as target recommendation information to be presented to the user.
The embodiment of the disclosure can collect and process user historical data, and respectively train a bill amount forecasting model and a conversion rate forecasting model. The user history data used for training the order amount forecasting model can include: the order amount of the historical order of the user, the price of the commodity recently browsed by the user, the point information of the user, the membership grade of the user and the like. The user history data used to train the conversion prediction model may include: the method comprises the steps of displaying historical recommendation information to a user in response to historical search behaviors of the user, actively displaying the historical recommendation information to the user in response to historical browsing behaviors of the user, completing ordering operation by the user in the historical recommendation information, and the like.
It is understood that the embodiments of the present disclosure do not impose any limitation on the model types of the order placing prediction model and the conversion rate prediction model, the training data of the training model, and the training process of the model.
In the embodiment of the present disclosure, the average consumption interval of a single user and the reference consumption interval of a general user may be determined by an online test.
Specifically, taking a take-away scene as an example, information can be recommended to all users using the take-away application according to a sorting mode of the estimated order amount, and a preset time period (for example, 90 days) is tested online. The information recommended to the user comprises recommendation information obtained by the user through searching and recommendation information actively displayed to the user by the takeout application in the browsing process of the user. For example, if the situation that the user searches for the hamburger through the takeaway application is detected, all candidate stores which can provide hamburger dishes and are within the distribution range of the current position of the user can be obtained, the order amount of the user for each candidate store is estimated, the candidate stores are ranked according to the estimated order amount from high to low, and the candidate recommended stores which are ranked at the top N are displayed to the user.
after the online test is carried out for the preset time period, the average consumption interval of the user can be determined according to the order amount of the historical order generated by the user aiming at the historical recommendation information in the preset time period.
In an optional embodiment of the present disclosure, the determining, according to an order amount of a history order generated by a user for history recommendation information in a preset time period, an average consumption interval of the user may specifically include:
Step S11, obtaining the order amount of the historical order generated by the user aiming at the historical recommendation information in a preset time period; the historical recommendation information is determined according to order prices corresponding to the historical candidate information and estimated order-placing amount corresponding to the user;
And step S12, calculating an average consumption interval corresponding to the preset time period according to the order amount corresponding to the historical order.
In the online test process, all users recommend information to the users according to the ordering mode of pre-estimated order amount, after the online test is carried out for the preset time period, the order amount of the historical orders generated by each user for the historical recommendation information in the preset time period can be counted, and the average consumption interval of each user in the preset time is calculated by summing and averaging, for example, the average consumption interval of a certain user is [50 yuan, 51 yuan ].
and carrying out transaction spread statistics on average consumption intervals of different users in the preset time period, and taking the average consumption interval with the transaction spread satisfying the preset spread condition as a reference consumption interval.
In an optional embodiment of the present disclosure, the performing the transaction spread statistics on different average consumption intervals of the preset time period, and taking the average consumption interval in which the transaction spread satisfies the preset spread condition as a reference consumption interval may specifically include:
step S21, counting average consumption intervals corresponding to the preset time periods of different users;
Step S22, calculating the corresponding transaction amount fluctuation of the user groups in different average consumption intervals;
Step S23, determining the average consumption interval of the transaction amount with the expansion satisfying the preset expansion condition as a reference consumption interval; wherein, the condition that the amplitude of the copulation satisfies the preset amplitude of fluctuation comprises the following steps: the rise of the volume.
After the online test is carried out for the preset time period, the order amount of the historical orders generated by each user aiming at the historical recommendation information can be counted, the order amount of all the historical orders of each user is summed and averaged, and the average consumption interval corresponding to each user in the preset time period is obtained through calculation. Then, the transaction amount fluctuation corresponding to each average consumption interval is calculated, for example, the transaction amount fluctuation of the user group with the average consumption interval of [50 yuan, 51 yuan ], the transaction amount fluctuation of the user group with the average consumption interval of (51 yuan, 52 yuan ], the transaction amount fluctuation of the user group with the average consumption interval of (52 yuan, 53 yuan), and the like are calculated.
the transaction amount fluctuation range may be GMV (Gross transaction Volume, total transaction amount within a certain time) fluctuation range, or may also be rpm (recommended per mill, average single total amount per 1000 presentations).
For example, the rpm fluctuation of the user population with the average consumption interval of [50 yuan, 51 yuan ] is calculated, and first, within a preset time period (for example, the last 90 days), the users with the average consumption interval of [50 yuan, 51 yuan ] are obtained through screening. Then randomly dividing the screened users into an experimental group and a control group, calculating the total order-placing amount and the total store amount displayed by the users in the experimental group within a preset time period, dividing the total order-placing amount by the total store amount displayed and multiplying by 1000 to obtain the rpm of the user group in the experimental group; in the same way, the rpm of the user population in the control group can be calculated. And finally, dividing the rpm of the experimental group user population by the rpm of the control group user population, and subtracting 1, and expressing the result in a percentage mode to obtain the rpm fluctuation of the user population with the average consumption interval of [50 yuan, 51 yuan ].
the embodiment of the disclosure determines that the transaction amount fluctuation is 0, or an average consumption interval of which the transaction amount fluctuation is greater than 0 and is greater than a preset value is a reference consumption interval. The preset value can be a preset small value, and the reference consumption interval selected in the embodiment of the disclosure is an interval in which the rpm rise is 0 or just greater than 0, so that an average consumption interval in which the rise is negative can be excluded, and the total rise of the rpm can be maximized.
In summary, before recommending information to a user, the average consumption interval of the user may be determined according to the order amount of a historical order generated by the user for the historical recommendation information in a preset time period; carrying out transaction amount fluctuation statistics on average consumption intervals of different users in the preset time period, and taking the average consumption interval with transaction amount fluctuation satisfying preset fluctuation conditions as a reference consumption interval; further, in the process of recommending information to the user, the consumption type of the user may be determined according to the average consumption interval of the user and the reference consumption interval, for example, it is determined whether the user belongs to a high consumption type or a low consumption type. For users with different consumption types, different recommendation sorting modes can be selected, so that the users with different consumption types can obtain recommendation information with different requirements, and the accuracy of information recommendation can be improved.
example two
Referring to fig. 2, a block diagram of an information recommendation device in one embodiment of the present disclosure is shown, specifically as follows.
A first interval determination module 201, configured to determine an average consumption interval of a user according to an order amount of a history order generated by the user for history recommendation information in a preset time period;
a second interval determining module 202, configured to perform cross-amount fluctuation statistics on different average consumption intervals of the preset time period, and use an average consumption interval in which cross-amount fluctuation meets a preset fluctuation condition as a reference consumption interval;
A consumption type determining module 203, configured to determine a consumption type corresponding to the user according to the reference consumption interval and the average consumption interval corresponding to the user;
a sorting mode determining module 204, configured to determine, according to the consumption type of the user, a sorting mode corresponding to the user;
the target information determining module 205 is configured to rank the candidate recommendation information corresponding to the user according to the ranking mode, so as to obtain the target recommendation information corresponding to the user.
Optionally, the first interval determining module 201 includes:
the information acquisition submodule is used for acquiring the order amount of a historical order generated by a user aiming at the historical recommendation information in a preset time period; the historical recommendation information is determined according to order prices corresponding to the historical candidate information and estimated order-placing amount corresponding to the user;
And the interval calculation submodule is used for calculating the average consumption interval corresponding to the preset time period of the user according to the order amount corresponding to the historical order.
optionally, the second interval determining module 202 includes:
the interval counting submodule is used for counting average consumption intervals corresponding to different users in the preset time period;
the expansion calculation submodule is used for calculating the corresponding transaction expansion of the user groups in different average consumption intervals;
The benchmark determining submodule is used for determining an average consumption interval of which the transaction amount fluctuation meets a preset fluctuation condition as a benchmark consumption interval; wherein, the condition that the amplitude of the copulation satisfies the preset amplitude of fluctuation comprises the following steps: the rise of the volume.
Optionally, the consumption type determining module 203 is specifically configured to determine that the consumption type corresponding to the user is a high consumption type if the average consumption interval corresponding to the user is higher than the reference consumption interval; otherwise, determining the consumption type corresponding to the user as a low consumption type.
optionally, the sorting manner determining module 204 includes:
A first ordering determining submodule, configured to determine, if the consumption type of the user is a high consumption type, that the ordering manner corresponding to the user is: sorting the candidate recommendation information corresponding to the user according to the estimated ordering amount corresponding to the user; or
A second ranking determining sub-module, configured to determine, if the consumption type of the user is a low consumption type, that a ranking manner corresponding to the user is: and sequencing the candidate recommendation information corresponding to the user according to the estimated ordering conversion rate corresponding to the candidate recommendation information.
optionally, the estimated order amount corresponding to the user is determined according to an order amount corresponding to the historical order of the user; and the estimated ordering conversion rate corresponding to the candidate recommendation information is determined according to the historical conversion rate generated from displaying to the user to ordering the user according to the candidate recommendation information.
in summary, an embodiment of the present disclosure provides an information recommendation apparatus, where the apparatus includes: a first interval determination module 201, configured to determine an average consumption interval of a user according to an order amount of a history order generated by the user for history recommendation information in a preset time period; a second interval determining module 202, configured to perform cross-amount fluctuation statistics on different average consumption intervals of the preset time period, and use an average consumption interval in which cross-amount fluctuation meets a preset fluctuation condition as a reference consumption interval; a consumption type determining module 203, configured to determine a consumption type corresponding to the user according to the reference consumption interval and the average consumption interval corresponding to the user; a sorting mode determining module 204, configured to determine, according to the consumption type of the user, a sorting mode corresponding to the user; the target information determining module 205 is configured to rank the candidate recommendation information corresponding to the user according to the ranking mode, so as to obtain the target recommendation information corresponding to the user. The information recommendation device can select different recommendation sequencing modes for users with different consumption types, so that the users with different consumption types can obtain recommendation information with different requirements, and the accuracy of information recommendation can be improved.
An embodiment of the present disclosure also provides an electronic device, referring to fig. 3, including: a processor 301, a memory 302 and a computer program 3021 stored on and executable on the memory, the processor implementing the information recommendation method of the foregoing embodiments when executing the programs.
embodiments of the present disclosure also provide a readable storage medium, and instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the information recommendation method of the foregoing embodiments.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present disclosure are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the embodiments of the present disclosure as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the embodiments of the present disclosure.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the disclosure, various features of the embodiments of the disclosure are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, claimed embodiments of the disclosure require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of an embodiment of this disclosure.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
the various component embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a sequencing device according to embodiments of the present disclosure. Embodiments of the present disclosure may also be implemented as an apparatus or device program for performing a portion or all of the methods described herein. Such programs implementing embodiments of the present disclosure may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit embodiments of the disclosure, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
the above description is only for the purpose of illustrating the preferred embodiments of the present disclosure and is not to be construed as limiting the embodiments of the present disclosure, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the embodiments of the present disclosure are intended to be included within the scope of the embodiments of the present disclosure.
The above description is only a specific implementation of the embodiments of the present disclosure, but the scope of the embodiments of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the embodiments of the present disclosure, and all the changes or substitutions should be covered by the scope of the embodiments of the present disclosure. Therefore, the protection scope of the embodiments of the present disclosure shall be subject to the protection scope of the claims.
Claims (14)
1. An information recommendation method, characterized in that the method comprises:
determining an average consumption interval of a user according to an order amount of a historical order generated by the user aiming at historical recommendation information in a preset time period;
performing transaction spread statistics on different average consumption intervals of the preset time period, and taking the average consumption interval with the transaction spread satisfying a preset spread condition as a reference consumption interval;
Determining a consumption type corresponding to the user according to the reference consumption interval and the average consumption interval corresponding to the user;
determining a sorting mode corresponding to the user according to the consumption type of the user;
And sorting the candidate recommendation information corresponding to the user according to the sorting mode to obtain the target recommendation information corresponding to the user.
2. the method of claim 1, wherein the determining the average consumption interval of the user according to the order amount of the historical order generated by the user for the historical recommendation information in a preset time period comprises:
Acquiring the order amount of a historical order generated by a user aiming at the historical recommendation information in a preset time period; the historical recommendation information is determined according to order prices corresponding to the historical candidate information and estimated order-placing amount corresponding to the user;
And calculating the average consumption interval corresponding to the preset time period of the user according to the order amount corresponding to the historical order.
3. The method according to claim 1 or 2, wherein the performing of the transaction spread statistics on different average consumption intervals of the preset time period, and taking the average consumption interval in which the transaction spread satisfies a preset spread condition as a reference consumption interval comprises:
counting average consumption intervals corresponding to different users in the preset time period;
Calculating the corresponding transaction amount fluctuation of the user groups in the different average consumption intervals;
determining an average consumption interval of which the transaction amount expansion amplitude meets a preset expansion amplitude condition as a reference consumption interval; wherein, the condition that the amplitude of the copulation satisfies the preset amplitude of fluctuation comprises the following steps: the rise of the volume.
4. The method according to claim 1, wherein the determining the consumption type corresponding to the user according to the reference consumption interval and the average consumption interval corresponding to the user comprises:
if the average consumption interval corresponding to the user is higher than the reference consumption interval, determining that the consumption type corresponding to the user is a high consumption type; otherwise, determining the consumption type corresponding to the user as a low consumption type.
5. The method according to claim 1, wherein the determining the ranking mode corresponding to the user according to the consumption type of the user comprises:
If the consumption type of the user is a high consumption type, determining that the ordering mode corresponding to the user is as follows: sorting the candidate recommendation information corresponding to the user according to the estimated ordering amount corresponding to the user; or
If the consumption type of the user is a low consumption type, determining that the ordering mode corresponding to the user is as follows: and sequencing the candidate recommendation information corresponding to the user according to the estimated ordering conversion rate corresponding to the candidate recommendation information.
6. the method according to claim 5, wherein the estimated order amount corresponding to the user is determined according to an order amount corresponding to the historical order of the user; and the estimated ordering conversion rate corresponding to the candidate recommendation information is determined according to the historical conversion rate generated from displaying to the user to ordering the user according to the candidate recommendation information.
7. an information recommendation apparatus, characterized in that the apparatus comprises:
the first interval determining module is used for determining an average consumption interval of a user according to the order amount of a historical order generated by the user aiming at historical recommendation information in a preset time period;
The second interval determining module is used for carrying out transaction spread statistics on different average consumption intervals in the preset time period and taking the average consumption interval with the transaction spread meeting the preset spread condition as a reference consumption interval;
A consumption type determining module, configured to determine a consumption type corresponding to the user according to the reference consumption interval and the average consumption interval corresponding to the user;
The sequencing mode determining module is used for determining a sequencing mode corresponding to the user according to the consumption type of the user;
And the target information determining module is used for sequencing the candidate recommendation information corresponding to the user according to the sequencing mode to obtain the target recommendation information corresponding to the user.
8. The apparatus of claim 7, wherein the first interval determining module comprises:
the information acquisition submodule is used for acquiring the order amount of a historical order generated by a user aiming at the historical recommendation information in a preset time period; the historical recommendation information is determined according to order prices corresponding to the historical candidate information and estimated order-placing amount corresponding to the user;
and the interval calculation submodule is used for calculating the average consumption interval corresponding to the preset time period of the user according to the order amount corresponding to the historical order.
9. The apparatus of claim 7 or 8, wherein the second interval determining module comprises:
the interval counting submodule is used for counting average consumption intervals corresponding to different users in the preset time period;
The expansion calculation submodule is used for calculating the corresponding transaction expansion of the user groups in different average consumption intervals;
The benchmark determining submodule is used for determining an average consumption interval of which the transaction amount fluctuation meets a preset fluctuation condition as a benchmark consumption interval; wherein, the condition that the amplitude of the copulation satisfies the preset amplitude of fluctuation comprises the following steps: the rise of the volume.
10. the apparatus according to claim 7, wherein the consumption type determining module is specifically configured to determine that the consumption type corresponding to the user is a high consumption type if the average consumption interval corresponding to the user is higher than the reference consumption interval; otherwise, determining the consumption type corresponding to the user as a low consumption type.
11. the apparatus of claim 7, wherein the ranking mode determining module comprises:
a first ordering determining submodule, configured to determine, if the consumption type of the user is a high consumption type, that the ordering manner corresponding to the user is: sorting the candidate recommendation information corresponding to the user according to the estimated ordering amount corresponding to the user; or
A second ranking determining sub-module, configured to determine, if the consumption type of the user is a low consumption type, that a ranking manner corresponding to the user is: and sequencing the candidate recommendation information corresponding to the user according to the estimated ordering conversion rate corresponding to the candidate recommendation information.
12. The apparatus according to claim 11, wherein the estimated amount of orders placed corresponding to the user is determined according to an order amount corresponding to a historical order of the user; and the estimated ordering conversion rate corresponding to the candidate recommendation information is determined according to the historical conversion rate generated from displaying to the user to ordering the user according to the candidate recommendation information.
13. An electronic device, comprising:
Processor, memory and computer program stored on the memory and executable on the processor, characterized in that the processor implements the information recommendation method according to one or more of claims 1-6 when executing the program.
14. a readable storage medium, characterized in that instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the information recommendation method according to one or more of method claims 1-6.
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