CN113127744A - UserCF model-based online service recommendation method for integrated energy company - Google Patents
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Abstract
The invention relates to a UserCF model-based online service recommendation method for an integrated energy company, which comprises the following steps of: constructing an e-commerce service directory on the comprehensive energy line, wherein the e-commerce service directory comprises a plurality of service items; generating a user-service item scoring matrix through a UserCF model based on the service items; acquiring K users with the highest similarity to a target user c based on a user-service item scoring matrix to form a nearest neighbor set N (c) of the target user; and predicting the most possible scores of the items which are not browsed by the target user according to the scoring information of the nearest neighbor set. By constructing a proper service directory and analyzing the recommendation method of the integrated energy online service, the recommendation work of the user can be completed only by knowing the rating condition of the user on the project without knowing more professional knowledge. By formulating the service directory on the comprehensive energy line and constructing the UserCF model, service recommendation suggestions can be effectively made for the comprehensive energy service company, and the pertinence of providing energy services is improved.
Description
Technical Field
The application relates to the technical field of online service recommendation optimization of an integrated energy system, in particular to an online service recommendation method of an integrated energy company based on a UserCF model.
Background
The network technology is rapidly developed, the network resources are increasingly abundant, convenience and selection are provided for the life of people, and users gradually rely on the network to collect and provide information. However, the amount of network resources which are explosively increased also brings trouble to users. The simultaneous presentation of a large amount of resource information enables users to spend more time searching for information really required by themselves, and even the required information may be submerged by useless information, so that the possibility that the required information cannot be found is faced. This is the so-called "information explosion" problem. It is becoming increasingly important how to screen out what the user really needs from dazzling information. The search engine is created to solve the problem, however, the search engine returns the same information to all users, and does not consider that the users still need to spend a lot of time to filter useless information. Therefore, how to filter massive information according to personal preference of a user without losing the user while expanding the information is needed. Recommendation systems are emerging for better understanding of the user's personalized needs.
The recommendation system is used for mining the resources which are possibly interested or required by the users from the mass information according to the interest characteristics of the different users and recommending the resources. As a platform based on mass data mining, it is considered as one of the most effective tools for solving information explosion. The recommendation system essentially evaluates the likeness of some products that the user never touches by analyzing the resources that the user has selected, and feeds back to the user the most liked product in the prediction.
In the research on the personalized recommendation system, the most important is the research on the personalized recommendation algorithm. If a certain item has over-evaluation on only a small part of the items, and the item categories of different user selection evaluations are also greatly different, the sparsity of the user evaluation item matrix is caused, and the recommendation quality is seriously influenced. And for the newly registered user, the system cannot know the interest and hobbies of the newly registered user for recommendation because the system does not have historical information.
Therefore, the method for recommending the online service of the comprehensive energy company based on the UserCF model is provided, and the interest preference of the user is mined according to the interest characteristics of the user and the behavior data of the user in the system, so that the online service scheme of the comprehensive energy company similar to the interest preference of the user is recommended, and the method is a main problem to be solved at present.
Disclosure of Invention
The application provides an online service recommendation method for an integrated energy company based on a UserCF model, which ensures recommendation quality and promotes development and application of a recommendation system.
The technical scheme adopted by the application is as follows:
the invention provides a UserCF model-based online service recommendation method for an integrated energy company, which comprises the following steps:
constructing an e-commerce service directory on the comprehensive energy line, wherein the e-commerce service directory comprises a plurality of service items;
generating a user-service item scoring matrix through a UserCF model based on the service items;
acquiring K users with the highest similarity to a target user c based on the user-service item scoring matrix to form a nearest neighbor set N (c) of the target user;
and predicting the most possible scores of the items which are not browsed by the target user according to the scoring information of the nearest neighbor set.
Further, the building of the comprehensive energy online e-commerce service directory comprises:
establishing a new service directory on the retail line;
establishing an intelligent hardware online service directory;
establishing an on-line service directory of an enterprise proxy;
establishing a service directory on a mobile energy storage line;
and establishing a service directory on the electric equipment leasing line.
Further, the new on-line retail service comprises: household appliances, 3C digital and health protection;
the intelligent hardware online service comprises: the system comprises a meter sensor, an intelligent well cover and an intelligent gateway;
the enterprise maintenance online service comprises the following steps: a dimension generation service and a dimension generation software;
the mobile energy storage online service comprises: the system comprises a mobile energy storage vehicle, a mobile energy storage shelter, a mobile energy storage construction power supply and a rechargeable intelligent rail locomotive;
the electric power equipment leasing on-line service comprises the following steps: on-line leasing service of box transformer, equipment and instruments, power generation equipment and safety tools.
Further, generating a user-service item scoring matrix through a UserCF model based on the service items, including: and performing data conversion based on the service items to form a user-item scoring matrix, wherein the user-item scoring matrix reflects the scoring of the corresponding items by the user.
Further, the user-item scoring matrix reflects the scoring of the corresponding item by the user as: the scoring range is 1-5.
Further, based on the user-service item scoring matrix, obtaining K users with the highest similarity to the target user c, and forming a nearest neighbor set n (c) of the target user, including:
and acquiring K users with the highest similarity to the target user c based on the user-service item scoring matrix, and forming a nearest neighbor set N (c) { c1, c2 …, ck, } of the target user.
Further, in the target user's nearest neighbor set n (c) { c1, c2 …, ck, }:
c ≠ N (c), and the users ck in N (c) are arranged in descending order according to the similarity degree sim (ck, c) with the target user c, the range of values of sim (ck, c) is [ -1, 1], and the closer to 1, the higher the similarity degree between the users ck and the target user c is.
Further, by setting the number k of nearest neighbors, the first k neighbors with the highest similarity to the target user can be obtained from the nearest neighbor set n (c).
Further, predicting the most likely score of the target user's unviewed items according to the scoring information of the nearest neighbor set includes:
generating a target user's score for a particular item i and predicting a target user's score for all items from the target user's nearest neighbor set n (c) { c1, c2 …, ck, };
and selecting the top N items to recommend to the target user according to the grading finger size of the target user for grading all the items.
The technical scheme of the application has the following beneficial effects:
the invention relates to a comprehensive energy company online service recommendation method based on a UserCF model, which comprises the following steps: constructing an e-commerce service directory on the comprehensive energy line, wherein the e-commerce service directory comprises a plurality of service items; generating a user-service item scoring matrix through a UserCF model based on the service items; acquiring K users with the highest similarity to a target user c based on a user-service item scoring matrix to form a nearest neighbor set N (c) of the target user; and predicting the most possible scores of the items which are not browsed by the target user according to the scoring information of the nearest neighbor set.
The comprehensive energy company online service recommendation method based on the UserCF model can realize online service item recommendation of the comprehensive energy company through reasonable analysis of historical data of different customers, and provides certain guidance for service ratings of different types of services;
(2) the on-line service recommendation algorithm based on the UserCF model has important significance on an on-line service recommendation system, the suitable matching degree of different on-line service items to target customers is mapped, and a certain reference value is provided for an on-line service recommendation mechanism of a related energy system service company.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an online service recommendation method for an integrated energy company based on a UserCF model according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following examples do not represent all embodiments consistent with the present application. But merely as exemplifications of systems and methods consistent with certain aspects of the application, as recited in the claims.
The comprehensive energy online e-commerce service is a key component of the future power grid intelligent energy and is an important way for transition of energy supply sources from power grid energy integration service to competition supply strategy transformation of multi-energy service providers. The interest preference of the user is mined according to the interest characteristics of the user and the behavior data of the user in the system, so that the comprehensive energy online service scheme similar to the interest preference of the user is recommended, and the method plays an important role in the occupation of the comprehensive energy company in the energy competition. The online service recommendation method of the comprehensive energy company is constructed based on a UserCF model algorithm, and by constructing a proper service directory and analyzing the online service recommendation method of the comprehensive energy company, the recommendation work of the user can be completed only by knowing the grading condition of the user on the project without knowing more professional knowledge. By formulating the service directory on the comprehensive energy line and constructing the UserCF model, service recommendation suggestions can be effectively made for the comprehensive energy service company, and the pertinence of providing energy services is improved.
Specifically, referring to fig. 1, a flowchart of an online service recommendation method for an integrated energy company based on the UserCF model is shown.
The application provides a comprehensive energy company online service recommendation method based on a UserCF model, which comprises the following steps:
s01: constructing an e-commerce service directory on the comprehensive energy line, wherein the e-commerce service directory comprises a plurality of service items;
specifically, the building of the integrated energy online e-commerce service directory comprises the following steps:
establishing a comprehensive new on-line service catalog, wherein the comprehensive new on-line service is mainly concerned with a series of new retail products brought by the appearance of a novel power grid, and comprises the following steps: household appliances, 3C digital and health protection;
the method comprises the following steps of establishing an intelligent hardware online service directory, wherein the electric energy replacement service mainly aims at improving the existing equipment in an intelligent mode so that the existing equipment can be used for remote monitoring, automatic induction and the like, and the intelligent hardware online service comprises the following steps: the system comprises a meter sensor, an intelligent well cover and an intelligent gateway;
the method is characterized in that an online service directory of an enterprise maintenance agent is established, partial maintenance work is stripped by introducing a maintenance agent company, and transmission network layered maintenance is realized in a cooperative win-win mode, so that the method is a maintenance mode which is in line with the current development trend. The enterprise maintenance online service comprises the following steps: the maintenance-substituting service and the maintenance-substituting software can provide powerful support for the layered maintenance of the power system;
establishing a mobile energy storage online service directory, wherein the mobile energy storage online service directory comprises: the system comprises a mobile energy storage vehicle, a mobile energy storage shelter, a mobile energy storage construction power supply and a rechargeable intelligent rail locomotive;
establishing an on-line service directory of the electric power equipment leasing line, wherein the on-line service of the electric power equipment leasing line comprises the following steps: on-line leasing service of box transformer, equipment and instruments, power generation equipment and safety tools. The equipment leasing can be carried out on the multi-class professional electrician equipment on line, and the interconnection and intercommunication of the power equipment are realized. This service will drive the development and maintenance of the future power market.
S02: generating a user-service item scoring matrix through a UserCF model based on the service items;
specifically, data conversion is carried out based on the service items to form a user-item scoring matrix, wherein the user-item scoring matrix reflects the scoring of the corresponding items by the user. As shown in table 1, an m × n dimensional matrix R (m, n) represents evaluation information of a user on an item, i.e., a user-item scoring matrix. As shown in Table 1, where rows represent users, columns represent items, RijRepresenting the value of the user i's credit to item j. The score value is usually in [1,5 ]]Within, a higher value indicates a higher preference for the corresponding item.
TABLE 1 user-service item scoring matrix
S03: acquiring K users with the highest similarity to a target user c based on the user-service item scoring matrix to form a nearest neighbor set N (c) of the target user;
specifically, based on the user-service item scoring matrix, K users with the highest similarity to the target user c are obtained, and a nearest neighbor set n (c) { c1, c2 …, ck, } of the target user is formed. The method of obtaining user neighbors is typically the k-neighbor method.
c ≠ N (c), and user c in N (c)kAccording to its degree of similarity sim (c) with the target user ckC) in descending order from big to small, sim (c)kAnd c) has a value range of [ -1, 1]，sim(ckAnd c) the closer to 1, the user c is representedkThe higher the similarity with the target user c. By setting the number k of nearest neighbors, the target can be obtained from the nearest neighbor set N (c)The first k neighbors with the highest user similarity.
In general, the set of nearest neighbors may be partitioned according to similarity. In the user-based collaborative filtering algorithm, the similarity is measured by using a constraint Person correlation coefficient. Let the common scoring item set of users u and v be Iuv＝{i∈I|Rui≠0,RviNot equal to 0}, specifically as follows:
wherein R ismedRepresents the median of the systematic scoring interval. For example, a 5-point system, which is 3, is used.
In constraining the Person correlation coefficients, the joint scores of two users are typically used to compute the correlation similarity. Generally, if the common scoring items of two users are more, the similarity degree between the users can be more accurately described by adopting the Person algorithm.
The nearest neighbor query and construction is the most core part of the collaborative filtering algorithm based on the user. The goodness of the whole algorithm is largely determined by the accuracy and efficiency of the nearest neighbor query. Essentially, this step is the modeling phase of the overall algorithm.
S04: and predicting the most possible scores of the items which are not browsed by the target user according to the scoring information of the nearest neighbor set.
From the target user's nearest neighbor set n (c) { c1, c2 …, ck }, mainly two types of recommendation results can be produced, one is to generate a target user's score for a particular item i; and the other method is that the scores of all the items of the target user are predicted, then the items are sorted according to the scores, and the top N items are selected for recommendation. The most common methods for predicting the value of a project's score are:
wherein, Pu,jIs the predicted score for item j for user u,
is the average score of the evaluated items of user u, and N is the number of nearest neighbors of user u.
The invention provides a UserCF model-based online service recommendation method for an integrated energy company, which is established by providing a set of online service catalogs of integrated energy users and analyzing a recommendation method for integrated energy services and developing the online service recommendation method for the integrated energy company based on the UserCF model aiming at the current situation that the large power grid and the modern Internet technology are gradually fused and developed and the online service development demand is continuously improved, and researches are carried out on a user-based collaborative filtering algorithm and an integrated energy online service recommendation system. A valid conclusion is reached: (1) the comprehensive energy company online service recommendation method based on the UserCF model can realize online service item recommendation of the comprehensive energy service company through reasonable analysis of historical data of different customers, and provides certain guidance for service ratings of different types of services; (2) the on-line service recommendation algorithm based on the UserCF model has important significance on an on-line service recommendation system, the suitable matching degree of different on-line service items to target customers is mapped, and a certain reference value is provided for an on-line service recommendation mechanism of a related energy system service company.
The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments extended according to the scheme of the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.
Claims (9)
1. A UserCF model-based integrated energy company online service recommendation method is characterized by comprising the following steps:
constructing an e-commerce service directory on the comprehensive energy line, wherein the e-commerce service directory comprises a plurality of service items;
generating a user-service item scoring matrix through a UserCF model based on the service items;
acquiring K users with the highest similarity to a target user c based on the user-service item scoring matrix to form a nearest neighbor set N (c) of the target user;
and predicting the most possible scores of the items which are not browsed by the target user according to the scoring information of the nearest neighbor set.
2. The UserCF model-based integrated energy company online service recommendation method according to claim 1, wherein the constructing of the integrated energy company online e-commerce service directory comprises:
establishing a new service directory on the retail line;
establishing an intelligent hardware online service directory;
establishing an on-line service directory of an enterprise proxy;
establishing a service directory on a mobile energy storage line;
and establishing a service directory on the electric equipment leasing line.
3. The UserCF model-based integrated energy company online service recommendation method of claim 2, wherein said new retail online service comprises: household appliances, 3C digital and health protection;
the intelligent hardware online service comprises: the system comprises a meter sensor, an intelligent well cover and an intelligent gateway;
the enterprise maintenance online service comprises the following steps: a dimension generation service and a dimension generation software;
the mobile energy storage online service comprises: the system comprises a mobile energy storage vehicle, a mobile energy storage shelter, a mobile energy storage construction power supply and a rechargeable intelligent rail locomotive;
the electric power equipment leasing on-line service comprises the following steps: on-line leasing service of box transformer, equipment and instruments, power generation equipment and safety tools.
4. The UserCF model-based method for recommending online services for an integrated energy company according to claim 1, wherein generating a user-service item scoring matrix based on said service items through a UserCF model comprises:
and performing data conversion based on the service items to form a user-item scoring matrix, wherein the user-item scoring matrix reflects the scoring of the corresponding items by the user.
5. The UserCF model-based integrated energy company online service recommendation method of claim 4, wherein said user-item scoring matrix reflects user scores for corresponding items:
the scoring range is 1-5.
6. The UserCF model-based integrated energy company online service recommendation method according to claim 1, wherein the step of obtaining K users with the highest similarity to a target user c based on the user-service item scoring matrix to form a nearest neighbor set N (c) of the target user comprises the steps of:
and acquiring K users with the highest similarity to the target user c based on the user-service item scoring matrix, and forming a nearest neighbor set N (c) { c1, c2 …, ck, } of the target user.
7. The UserCF model-based integrated energy company online service recommendation method according to claim 6, wherein in said target user's nearest neighbor set N (c) { c1, c2 …, ck, }:
c ≠ N (c), and the users ck in N (c) are arranged in descending order according to the similarity degree sim (ck, c) with the target user c, the range of values of sim (ck, c) is [ -1, 1], and the closer to 1, the higher the similarity degree between the users ck and the target user c is.
8. The UserCF model-based integrated energy company online service recommendation method according to claim 7, wherein the first k neighbors with the highest similarity to the target user can be obtained from the nearest neighbor set N (c) by setting the number k of nearest neighbors.
9. The UserCF model-based integrated energy company online service recommendation method according to claim 1, wherein predicting the most likely score of the target user's unviewed items according to the scoring information of the nearest neighbor set comprises:
generating a target user's score for a particular item i and predicting a target user's score for all items from the target user's nearest neighbor set n (c) { c1, c2 …, ck, };
and selecting the top N items to recommend to the target user according to the grading finger size of the target user for grading all the items.
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