CN113592589B - Textile raw material recommendation method, device and processor - Google Patents
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Abstract
The embodiment of the application provides a textile raw material recommending method, a device, a processor and a storage medium. The method comprises the following steps: acquiring behavior data of a user; determining the interest degree of the user on the textile raw material commodity in each dimension according to the behavior data, and determining the user similarity among the users; determining a first recommendation candidate set for the user according to the interest degree; determining the raw material similarity between raw material commodities; determining a second recommendation candidate set of the user according to the user similarity and the raw material similarity; inputting at least one of user behavior data, raw material characteristics of each textile raw material, and context information into a predictive model to determine a third recommended candidate set for the user; determining a final recommendation set of the user according to the first recommendation candidate set, the second recommendation candidate set and the third recommendation candidate set; the raw material commodity included in the final recommended set is determined as a recommended raw material commodity for the user.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for recommending textile materials, a storage medium, and a processor.
Background
The company has a raw material supply service and a raw material production platform service, and hopes to recommend the raw material recommended sequencing to the user through factory data, raw material related data, user behavior of raw material recommended positions and the like of raw material production so as to improve the raw material clicking rate and thus improve the yield.
The prior art has simpler implementation, generally, each related dimension data is obtained through ETL processing, normalized, weighted and summed to obtain a comprehensive score, and then outputted in reverse order. The method for calculating the score in the splitting place to recommend the raw materials has low recommendation accuracy and cannot realize intelligent recommendation.
Disclosure of Invention
The embodiment of the application aims to provide a textile raw material recommending method, a device, a storage medium and a processor.
To achieve the above object, a first aspect of the present application provides a method for recommending textile materials, including:
acquiring behavior data of a user;
determining the interest degree of the user on the textile raw material commodity in each dimension according to the behavior data, and determining the user similarity among the users;
determining a first recommendation candidate set for the user according to the interest degree;
determining the raw material similarity between raw material commodities;
determining a second recommendation candidate set of the user according to the user similarity and the raw material similarity;
inputting at least one of user behavior data, raw material characteristics of each textile raw material, and context information into a predictive model to determine a third recommended candidate set for the user;
determining a final recommendation set of the user according to the first recommendation candidate set, the second recommendation candidate set and the third recommendation candidate set;
the raw material commodity included in the final recommended set is determined as a recommended raw material commodity for the user.
Optionally, the behavior data includes operational behaviors of the user for the raw commodity over a plurality of time periods of different durations; determining the degree of interest of the user in the textile goods based on the behavioral data includes: acquiring commodity attributes of raw commodity corresponding to each operation behavior of a user; and determining the interest degree of the user for each raw material commodity according to the operation behaviors and commodity attributes.
Optionally, the commodity attribute comprises a commodity keyword; determining the interest degree of the user for each raw material commodity according to the operation behaviors and commodity attributes comprises the following steps: for each raw material commodity, determining an interest score of a user for a commodity keyword of the raw material commodity; ordering the interest scores to determine a preference sequence of the user for the commodity keywords; and determining the interest degree of the user in the keyword dimension for each raw material commodity according to the preference sequence.
Optionally, determining the interest score of the user for the commodity keyword of the raw commodity includes: the interest score is determined according to the following formula:
wherein score k The interest score of the user on the keyword k is shown as i, i is a raw material commodity operated by the user i in the behavior data, and a i Refers to the action weight, w, corresponding to the raw material commodity of the ith operation i Means the weight of the keyword k for the raw material commodity of the ith operation, f (t) i ) The user's interest level in the raw commodity of the ith operation at time t is shown as a function of time decay.
Optionally, determining the feedstock similarity between feedstock commodities comprises: acquiring raw material data of each raw material commodity; determining a feature vector of each raw commodity according to the raw data; and clustering the feature vectors to determine the raw material similarity between raw material commodities.
Optionally, determining the raw material commodity included in the final recommended set as the recommended raw material commodity for the user further includes: sequencing the raw material commodities included in the final recommendation set according to the sequence of the recommendation degree from high to low; adjusting the sequence and recommended quantity of raw material commodities belonging to the same type; and selecting the raw material commodities with the preset number and the previous serial number to determine the raw material commodities as recommended raw material commodities of the user.
Optionally, the method further comprises: extracting characteristics of the behavior data to determine corresponding user characteristics; determining commodity characteristics of raw commodity corresponding to each operation behavior contained in the acquired behavior data; determining feature weights of each user feature according to commodity features and behavior data; and adjusting the recommended raw material commodity for the user through the characteristic weight.
Optionally, the method further comprises: acquiring a real-time operation log of a user; determining at least one of recommended exposure rate and exposure click rate of the raw material commodity according to the operation log; and adjusting recommended raw material commodities for the user according to the recommended exposure rate and the exposure click rate.
A second aspect of the present application provides a processor configured to perform the above-described textile material recommendation method.
A third aspect of the present application provides a textile recommendation device comprising a processor as described above.
A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which when executed by a processor, cause the processor to be configured to perform the above-described textile recommendation method.
According to the textile raw material recommendation method, factory capacity big data of the textile industry is utilized, and according to matching relations of user figures, figures and the like of raw material commodities, the more accurate raw material matching recommendation requirements of individual users and authenticated enterprise users are met, and more intelligent raw material commodity recommendation is achieved.
Additional features and advantages of embodiments of the present application will be set forth in the detailed description that follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the present application and are incorporated in and constitute a part of this specification, illustrate embodiments of the present application and together with the description serve to explain, without limitation, the embodiments of the present application. In the drawings:
FIG. 1 schematically shows a flow diagram of a textile recommendation method according to an embodiment of the present application;
FIG. 2 schematically illustrates a schematic diagram of a textile raw material data recall in accordance with an embodiment of the present application;
fig. 3 schematically shows an internal structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following describes in detail the implementation of the embodiments of the present application with reference to the accompanying drawings. It should be understood that the detailed description is presented herein by way of illustration and explanation of the present application examples, and is not intended to limit the present application examples.
Fig. 1 schematically shows a flow diagram of a textile material recommendation method according to an embodiment of the present application. As shown in fig. 1, in an embodiment of the present application, there is provided a method for recommending textile materials, including the steps of:
step 101, obtaining behavior data of a user.
Step 102, determining the interest degree of the user for the textile raw material commodity in each dimension and the user similarity among the users according to the behavior data.
Firstly, the behavior data of the user can be obtained through the log total record of the system. The behavior data of the user includes operational behaviors of the user with respect to the raw commodity over a plurality of time periods of different durations. The effective actions in the operation actions comprise browsing, clicking, playing, praying, commenting, forwarding and other operations. By acquiring behavior data of the user within a period of time, the interest degree of the user on textile raw material commodities in each dimension can be determined, and the user similarity among different users can be determined. Therefore, the preference of the user to the specific dimension can be calculated through a statistical mining mode, the interest preference of different users is carved out in a weighted list mode, the first step of thousands of people and thousands of faces personalized recommendation is achieved, the data is visual, and the interpretability is strong. The obtained user portrait data can be used for other services, and a foundation is laid for personalized recommendation of other services.
In one embodiment, determining the degree of interest of the user in the textile goods and the user similarity between the users based on the behavioral data comprises: acquiring commodity attributes of raw commodity corresponding to each operation behavior of a user; and determining the interest degree of the user for each raw material commodity according to the operation behaviors and commodity attributes.
Specifically, after the behavior data of the user is obtained, the raw material commodity corresponding to each operation behavior of the user and the commodity attribute corresponding to the raw material commodity can be obtained. And then determining the interest degree of the user for each raw material commodity according to the operation behaviors and commodity attributes. Further, the merchandise attributes include merchandise keywords. Determining the interest degree of the user for each raw material commodity according to the operation behaviors and commodity attributes comprises the following steps: for each raw material commodity, determining an interest score of a user for a commodity keyword of the raw material commodity; ordering the interest scores to determine a preference sequence of the user for the commodity keywords; and determining the interest degree of the user in the keyword dimension for each raw material commodity according to the preference sequence. Specifically, the clicking operation of the user and the raw material commodity corresponding to the clicking operation can be counted in a period of time. The interest degree of a user on a certain attribute value can be calculated through the attribute weight of the raw material commodity. For deep actions such as praise, comment, collection and the like, the user can be indicated to have better preference for the content, and the weighting processing can be performed for the operation. The interest score may be determined according to the following formula:
wherein score k The interest score of the user on the keyword k is shown as i, i is a raw material commodity operated by the user i in the behavior data, and a i The action weight corresponding to the raw material commodity of the ith operation is referred to, for example, the weight of the praise operation may be set higher than the weight of the click operation. w (w) i Means the weight of the keyword k for the raw material commodity of the ith operation, f (t) i ) As a time decay function, the user's interest in the ith operational feedstock commodity is shown to decay to a degree that indicates a higher degree of interest as the current time is longer. The denominator is the number of all behavioural raw materials. And calculating score values of all keywords, and then carrying out normalized sorting to obtain preference sequences of users on the interests of the keywords. Other dimension-dependent computation approaches are similar, but each dimension computation result is a multi-valued list with weight scores.
After the interest score of the commodity keyword of the user for each raw commodity is calculated, the interest scores can be ranked, so that the preference sequence of the user for the commodity keyword can be determined, and then the interest degree of the user for each raw commodity in the keyword dimension can be determined according to the preference sequence.
Step 103, determining a first recommendation candidate set for the user according to the interest level.
Step 104, determining the raw material similarity between raw material commodities.
Step 105, determining a second recommendation candidate set of the user according to the user similarity and the raw material similarity.
At step 106, at least one of the user's behavioral data, raw material characteristics of each textile raw material, and contextual information is input to the predictive model to determine a third recommended candidate set for the user.
Step 107, determining a final recommendation set of the user according to the first recommendation candidate set, the second recommendation candidate set and the third recommendation candidate set.
And step 108, determining the raw material commodities included in the final recommendation set as recommended raw material commodities for the user.
After determining the interest degree of the user in the keyword dimension for each raw material commodity, a first recommendation candidate set for the user, that is, a recommendation candidate set for the raw material commodity of the user, may be determined according to the interest degree of the user. Then, the determination of the raw material similarity between each raw material commodity may be continued. Further, in one embodiment, determining the feedstock similarity between feedstock commodities comprises: acquiring raw material data of each raw material commodity; determining a feature vector of each raw commodity according to the raw data; and clustering the feature vectors to determine the raw material similarity between raw material commodities.
Specifically, as shown in fig. 2, a flow chart of data recall is given. In this embodiment, the similarity between materials (raw material commodities) can be calculated based on ICF recall requirements, for example, vector expression emplacement of each raw material commodity can be calculated comprehensively according to raw material categories such as text, audio, image and video through NLP, image understanding, video understanding and other modes, raw materials with the most similar raw materials are clustered through emplacement, so that the raw material similarity between each raw material commodity is calculated, and then other raw material commodities similar to the target raw material can be determined rapidly according to the raw material similarity. Then, a second recommendation candidate set for each user may be determined based on the user similarity and the raw material similarity. In other words, there may be various recall modes in data recall, including recall based on interest content, recall based on collaborative filtering, recall based on algorithm, and the like. The final set of second recommended candidates may be determined based on the result of the combination of the three recall modes. Specifically, the recall of the interest content class means that raw material commodities with different dimensions can be matched for the user based on the interest image of the user. For example, assuming that the user likes the cotton, the latest/hottest content recall may be selected from the cotton content, i.e., as the second recommended candidate set for the user, in a very intuitive and interpretable manner. Multiple-channel interest recall is formed according to one or multiple-level index formed by different dimensions, and the number of recall corresponding raw materials can be adaptively adjusted according to the weights because interest points in each dimension in the user portrait are weighted. Collaborative filtering type recall refers to recommending raw commodity products to a user based on user similarity and raw similarity. The user similarity recommendation can be simply understood as "a user who looks at the same raw material as you, and the material they look at is recommended to you", i.e. User Collaborative Filtering-UCF, and the raw material similarity recommendation based principle can be simply understood as "a similar raw material which you look at raw material is recommended to you", i.e. Item Collaborative Filtering-ICF, so that the recall mode is based on the process of finding a similar user or a similar raw material. The algorithm recall means that in an offline state, the model is trained through the operation record, the context and the raw materials and raw material characteristics related in the operation record of the user and sample data, the trained model is synchronized to the prediction service, then the user id and the user context can be obtained on line, and the user characteristic and the context characteristic can be generated by being transmitted to the prediction service. And calculating the scoring of each raw material commodity by combining the raw material characteristics and the model, and taking topN as a recall set to be transmitted back to a recommendation engine after sequencing. The context may include information such as a user operation time series, a commodity creation time series, and the like.
The algorithm needs data, the data needs characteristics, the characteristics are the most important parts in model training data, the essence of each type of algorithm is to fit a probability distribution function closest to the actual situation according to the existing sample distribution, the parameters to be fit are the weights of the characteristics, and the interested degree (clicking probability) of the user on the raw materials under the characteristics of the specific user or the raw materials can be obtained through the function. User characteristics, including context characteristics, are counted according to an offline behavior log of a user, and are similar to a user portrait, the user attribute types, the dimension preferences, statistics of behaviors and the like are assumed to be processed by characteristic engineering at the moment, the matching degree and the like related to factory portrait attributes are stored in a hive table in a structuring mode, the attribute type characteristics of raw materials are obtained in a resource pool, statistical type information of each dimension of the raw materials is obtained through a client log and is stored in the hive table, feature data in the hive are integrated through a timing task, and therefore the offline calculated user and raw material dimension characteristics are stored in the hive table and updated to be obtained on a redis supply line in real time.
Based on these basic recalls can be taken as candidate sets. The first recommendation candidate set, the second recommendation candidate set and the third recommendation candidate set determined according to the above manner can determine the final recommendation set recommendation of the user, and then the raw material commodities included in the final recommendation set can be determined as recommended raw material commodities for the user. The system can also perform the work of sequencing, reordering and the like on the recommendation set backwards, and realize a plurality of subsequent recommendation functions based on the work. Specifically, in one embodiment, determining the raw material commodity included in the final recommended set as the recommended raw material commodity for the user further includes: sequencing the raw material commodities included in the final recommendation set according to the sequence of the recommendation degree from high to low; adjusting the sequence and recommended quantity of raw material commodities belonging to the same type; and selecting the raw material commodities with the preset number and the previous serial number to determine the raw material commodities as recommended raw material commodities of the user. At least one of the user's behavioral data, raw material characteristics of the individual textile raw materials, contextual information may be input to the predictive model to determine a third recommended candidate set for the user. That is, the input data may be analyzed by the predictive model to target raw commodity products that may be of interest to the user, i.e., to determine a third recommended candidate set for each user. The final recommendation set for the user may be determined according to the first recommendation candidate set, the second recommendation candidate set, and the third recommendation candidate set, and the raw material items included in the final recommendation set may be determined as recommended raw material items for the user.
In one embodiment, the method further comprises: acquiring a real-time operation log of a user; determining at least one of recommended exposure rate and exposure click rate of the raw material commodity according to the operation log; and adjusting recommended raw material commodities for the user according to the recommended exposure rate and the exposure click rate.
The real-time stream is used for acquiring effective information from the log in real time, belongs to a necessity for applications with higher access frequency of users, can feed back real-time behaviors of the users and real-time statistical information of raw materials, and is processed by using a stream computing tool flink. The offline portrait data which can be generated from the data warehouse is a timing task of the day level or the hour level, the requirements of some scenes which need to feed back the user behavior in real time cannot be met, for example, the data which are just exposed by the user need to be filtered or the weight of the data is reduced, the just-clicked raw material commodity can push similar objects when being recommended by one brush, some negative feedback information needs to be recorded and takes effect in real time, the information can be quickly obtained by real-time calculation according to different requirement logics through user logs collected in real time, and the information is fed back to the system to quickly change. Specifically, through collecting the operation log of the user on the APP in real time, calculating the engine link and the like in real time through big data, and calculating indexes such as recommended exposure rate, exposure click rate and the like of raw material commodities on the APP. Recommended exposure = number of raw material goods actually visible to the user after recommendation/number of raw material goods recommended in real time. Exposure click rate = number of raw material goods actually clicked/number of actual raw material goods actually visible to the user after recommendation. If the recommended exposure rate is high, the user is willing to always brush the raw material commodity list automatically recommended to the user by the system, so that the user always brushes the list and always exposes different raw material commodities. If the exposure click rate is high, the user is not willing to brush the raw material commodity list recommended to the user, not only the abstract and the like on the list can be seen, but also the detail content of the specific raw material commodity can be clicked. The real-time calculation results are generally output to different redis keys according to different functions for online access. The calculation targets on the raw material side are similar, such as the real-time condition of the raw material being exposed, clicked, praise and other data, particularly after the new raw material is subjected to cold start exposure, the effect trend of the raw material can be rapidly reflected, the quality of the raw material is judged, and the follow-up recommended strategy is influenced. Real-time calculation results of raw materials also exist in redis.
In one embodiment, the method further comprises: extracting characteristics of the behavior data to determine corresponding user characteristics; determining commodity characteristics of raw commodity corresponding to each operation behavior contained in the acquired behavior data; determining feature weights of each user feature according to commodity features and behavior data; and adjusting the recommended raw material commodity for the user through the characteristic weight.
The three types of recall are combined and used, multiple recall channels jointly form a candidate set, data such as user exposure, clicking or negative feedback are filtered according to business rules in the recall process, meanwhile, the recall layer also needs to be considered for user interest and diversity exploration, otherwise, users possibly sink into more and more refined contents, the operation space of the back fine arrangement is smaller, and the content which is unknown to the user cannot be pushed out. Therefore, the candidate set category needs to be more diversified in the recall stage, and the principle of collaborative filtering can generate a certain diversity but also turn in similar circles, so that recall can be explored for more categories or similar categories. The user experience can be optimized through rearrangement, the control quantity or scattering of the similar content is also required to be regulated in a strategy according to the user behavior, and the measurement of the scattering of the next brushing control quantity is determined according to the continuous brushing points or the non-brushing points of the user. And meanwhile, the content diversity needs to be adjusted to search for interests, especially for users with insufficient portraits and behaviors. At the same time, the ordering of the operation business is also required to be adjusted here, such as the setting of the top of the related policy content of textile industry, the weighting of the hot content, the raising or lowering of the right of the operation content, and the like. And (3) after reordering, all the raw materials are packaged and ready to be sent, topN interception is carried out according to the quantity of returned results required by front-end calling, and the raw materials are displayed to a user. Therefore, the recommended exposure rate and the exposure click rate of the raw material commodity can be determined according to the real-time operation log of the user, and the recommended raw material commodity for the user can be adjusted according to the recommended exposure rate and the exposure click rate.
In this application, an online prediction function may also be provided. The prediction inference service can be provided in real time by using the form of the prediction service online, and the raw material id, the user id and the request context information of the candidate set to be sequenced are transmitted to the prediction service through a recommendation engine interface. The prediction service is also divided into a feature extraction module, a raw material scoring and sorting module and the like. Features can be extracted on line from the feature library through the incoming raw material ids and the user ids, feature information of all candidate sets is obtained by combining the contextual features, and then scoring of each raw material is calculated through each feature weight in the model. In the process, the extracted feature id is kept consistent with the feature id in the trained model, and meanwhile, the system performance is improved through a parallelization mode in the process of extracting and scoring the features of all raw materials. The trained model is timed to synchronize to the online predictive service machine by an offline training process.
According to the textile raw material recommendation method, factory productivity big data of the textile industry is utilized, according to matching relations of user images, images of raw material commodities and the like, more accurate raw material matching recommendation requirements of individual users and authenticated enterprise users are met, more intelligent raw material commodity recommendation is achieved, and recommendation accuracy is higher.
The embodiment of the application provides a storage medium, on which a program is stored, which when executed by a processor, implements the above-described textile material recommendation method.
The embodiment of the application provides a processor, which is used for running a program, wherein the program runs to execute the method for recommending the textile raw materials.
In one embodiment, a textile material recommendation apparatus is provided comprising a processor as described above.
The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can be provided with one or more than one, and the method for recommending the textile raw materials is realized by adjusting kernel parameters.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor a01, a network interface a02, a memory (not shown) and a database (not shown) connected by a system bus. Wherein the processor a01 of the computer device is adapted to provide computing and control capabilities. The memory of the computer device includes internal memory a03 and nonvolatile storage medium a04. The nonvolatile storage medium a04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory a03 provides an environment for the operation of the operating system B01 and the computer program B02 in the nonvolatile storage medium a04. The network interface a02 of the computer device is used for communication with an external terminal through a network connection. The computer program B02 is executed by the processor a01 to implement a textile material recommendation method.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
The embodiment of the application provides equipment, which comprises a processor, a memory and a program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the following steps: acquiring behavior data of a user; determining the interest degree of the user on the textile raw material commodity in each dimension according to the behavior data, and determining the user similarity among the users; determining a first recommendation candidate set for the user according to the interest degree; determining the raw material similarity between raw material commodities; determining a second recommendation candidate set of the user according to the user similarity and the raw material similarity; inputting at least one of user behavior data, raw material characteristics of each textile raw material, and context information into a predictive model to determine a third recommended candidate set for the user; determining a final recommendation set of the user according to the first recommendation candidate set, the second recommendation candidate set and the third recommendation candidate set; the raw material commodity included in the final recommended set is determined as a recommended raw material commodity for the user.
In one embodiment, the behavioral data includes operational behaviors of the user for the feedstock commodity over a plurality of time periods of different durations; determining the degree of interest of the user in the textile goods based on the behavioral data includes: acquiring commodity attributes of raw commodity corresponding to each operation behavior of a user; and determining the interest degree of the user for each raw material commodity according to the operation behaviors and commodity attributes.
In one embodiment, the merchandise attributes include merchandise keywords; determining the interest degree of the user for each raw material commodity according to the operation behaviors and commodity attributes comprises the following steps: for each raw material commodity, determining an interest score of a user for a commodity keyword of the raw material commodity; ordering the interest scores to determine a preference sequence of the user for the commodity keywords; and determining the interest degree of the user in the keyword dimension for each raw material commodity according to the preference sequence.
In one embodiment, determining the interest score of the user for the commodity keyword for the raw commodity comprises: the interest score is determined according to the following formula:
wherein score k The interest score of the user on the keyword k is shown as i, i is a raw material commodity operated by the user i in the behavior data, and a i Refers to the action weight, w, corresponding to the raw material commodity of the ith operation i Means the weight of the keyword k for the raw material commodity of the ith operation, f (t) i ) The user's interest level in the raw commodity of the ith operation at time t is shown as a function of time decay.
In one embodiment, determining the feedstock similarity between feedstock commodities comprises: acquiring raw material data of each raw material commodity; determining a feature vector of each raw commodity according to the raw data; and clustering the feature vectors to determine the raw material similarity between raw material commodities.
In one embodiment, determining the raw material good included in the final recommended set as the recommended raw material good for the user further comprises: sequencing the raw material commodities included in the final recommendation set according to the sequence of the recommendation degree from high to low; adjusting the sequence and recommended quantity of raw material commodities belonging to the same type; and selecting the raw material commodities with the preset number and the previous serial number to determine the raw material commodities as recommended raw material commodities of the user.
In one embodiment, the method further comprises: extracting characteristics of the behavior data to determine corresponding user characteristics; determining commodity characteristics of raw commodity corresponding to each operation behavior contained in the acquired behavior data; determining feature weights of each user feature according to commodity features and behavior data; and adjusting the recommended raw material commodity for the user through the characteristic weight.
In one embodiment, the method further comprises: acquiring a real-time operation log of a user; determining at least one of recommended exposure rate and exposure click rate of the raw material commodity according to the operation log; and adjusting recommended raw material commodities for the user according to the recommended exposure rate and the exposure click rate.
The present application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with the method steps of: acquiring behavior data of a user; determining the interest degree of the user on the textile raw material commodity in each dimension according to the behavior data, and determining the user similarity among the users; determining a first recommendation candidate set for the user according to the interest degree; determining the raw material similarity between raw material commodities; determining a second recommendation candidate set of the user according to the user similarity and the raw material similarity; inputting at least one of user behavior data, raw material characteristics of each textile raw material, and context information into a predictive model to determine a third recommended candidate set for the user; determining a final recommendation set of the user according to the first recommendation candidate set, the second recommendation candidate set and the third recommendation candidate set; the raw material commodity included in the final recommended set is determined as a recommended raw material commodity for the user.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (6)
1. A method of recommending a textile material, the method comprising:
acquiring behavior data of a user; the behavior data comprises operation behaviors of the user for raw material commodities in a plurality of time periods with different durations;
determining the interest degree of the user on textile raw material commodities in each dimension and the user similarity among the users according to the behavior data; the step of determining the interest degree of the user in the textile raw material commodity and the user similarity between the users according to the behavior data comprises the following steps:
acquiring commodity attributes of raw commodity corresponding to each operation behavior of the user; the commodity attributes include: commodity keywords;
determining the interest degree of the user for each raw material commodity according to the operation behaviors and the commodity attributes; the determining the interest degree of the user for each raw material commodity according to the operation behaviors and the commodity attributes comprises the following steps:
for each raw commodity, determining an interest score of the user for a commodity keyword of the raw commodity; the determining the interest score of the user for the commodity keywords of the raw commodity comprises: determining the interest score according to the following formula:
wherein score k The interest score of the user on the keyword k is indicated, i is the raw material commodity operated by the user i in the behavior data, and a is indicated by the user i i Refers to the ith operationBehavior weight, w, corresponding to raw material commodity i Means the weight of the keyword k for the raw material commodity of the ith operation, f (t) i ) The interest attenuation degree of the user on the raw material commodity of the ith operation at time t is shown as a time attenuation function;
sorting the interest scores to determine a preference sequence of the user for commodity keywords;
determining the interest degree of the user in the keyword dimension aiming at each raw material commodity according to the preference sequence;
determining a first recommendation candidate set for the user according to the interest degree;
determining the raw material similarity between the raw material commodities; the determining of the raw material similarity between the raw material commodities comprises:
acquiring raw material data of each raw material commodity; raw material data comprises text, audio, image and video;
determining a feature vector of each raw commodity according to the raw data, including: calculating the feature vector of each raw material commodity through NLP, image understanding or video understanding modes;
clustering the feature vectors to determine raw material similarity between the raw material commodities; other raw material commodities similar to the target raw material can be rapidly determined according to the raw material similarity;
determining a second recommendation candidate set of the user according to the user similarity and the raw material similarity;
inputting at least one of behavioral data of the user, raw material characteristics of each textile raw material, contextual information to a predictive model to determine a third recommended candidate set for the user;
determining a final recommendation set of the user according to the first recommendation candidate set, the second recommendation candidate set and the third recommendation candidate set;
and determining the raw material commodities included in the final recommendation set as recommended raw material commodities for the user.
2. The method of claim 1, wherein the determining the raw material good included in the final recommended set as the recommended raw material good for the user further comprises:
sequencing the raw material commodities included in the final recommendation set according to the sequence of the recommendation degree from high to low;
adjusting the sequence and recommended quantity of raw material commodities belonging to the same type;
and selecting the raw material commodities with the preset number and the previous serial number to determine the raw material commodities as recommended raw material commodities of the user.
3. The method according to claim 1, wherein the method further comprises:
extracting characteristics of the behavior data to determine corresponding user characteristics;
determining and acquiring commodity characteristics of raw commodity corresponding to each operation behavior contained in the behavior data;
determining feature weights of each user feature according to the commodity features and the behavior data;
and adjusting the recommended raw material commodity aiming at the user through the characteristic weight.
4. The method according to claim 1, wherein the method further comprises:
acquiring a real-time operation log of the user;
determining at least one of recommended exposure rate and exposure click rate of the raw material commodity according to the operation log;
and adjusting recommended raw material commodities for the user according to the recommended exposure rate and the exposure click rate.
5. A processor, characterized by being configured to perform the textile recommendation method according to any one of claims 1 to 4.
6. A textile recommendation device, characterized in that it comprises a processor according to claim 5.
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