CN111177559A - Text travel service recommendation method and device, electronic equipment and storage medium - Google Patents

Text travel service recommendation method and device, electronic equipment and storage medium Download PDF

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CN111177559A
CN111177559A CN201911404349.0A CN201911404349A CN111177559A CN 111177559 A CN111177559 A CN 111177559A CN 201911404349 A CN201911404349 A CN 201911404349A CN 111177559 A CN111177559 A CN 111177559A
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CN111177559B (en
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宋雨伦
贾一羽
何中诚
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China United Network Communications Group Co Ltd
Unicom Big Data Co Ltd
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Unicom Big Data Co Ltd
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Abstract

The invention provides a method and a device for recommending travel service, electronic equipment and a storage medium, which are used for recommending travel service by acquiring user characteristic information of a target user; extracting a knowledge chain group from a preset text travel knowledge graph according to the user characteristic information; calculating target text travel service information matched with the user characteristic information according to the knowledge chain group; the target travel service information is recommended to the target user, and compared with the original travel knowledge map, the knowledge chain group extracted from the preset travel knowledge map according to the user characteristic information has smaller data volume and higher calculation efficiency, so that the dynamic interaction behavior in the use process of the user can be analyzed in real time and fed back in time, and the accuracy of the result recommended by the travel service to the user is improved.

Description

Text travel service recommendation method and device, electronic equipment and storage medium
Technical Field
The invention relates to the technical field of big data, in particular to a method and a device for recommending a travel service, an electronic device and a storage medium.
Background
With the development of big data technology, the maturity of various user identification technologies provides support for a personalized recommendation system, and provides a personalized real-time recommendation function according to the characteristics of a user, so that frequent clicking and error correction behaviors of the user in a man-machine interaction system can be reduced, the user experience is optimized, and the user satisfaction is improved.
In the prior art, a traditional method for realizing recommendation of travel services by using a knowledge graph is to construct the knowledge graph through priori knowledge, and the knowledge graph is complex in structure and difficult to calculate in real time, so that a large-scale calculation matrix is constructed, dynamic interaction behaviors of a user in a using process are difficult to analyze in real time and feed back in time, the problems of low calculation efficiency, inaccurate result of travel service recommendation to the user and the like are caused, and the experience of the user in a human-computer interaction process is influenced.
Disclosure of Invention
The invention provides a method and a device for recommending travel services, electronic equipment and a storage medium, which are used for solving the problem that the result of recommending the travel services to a user is inaccurate.
According to a first aspect of the disclosed embodiments, the present invention provides a method for recommending travel services, the method comprising:
acquiring user characteristic information of a target user;
extracting a knowledge chain group from a preset text travel knowledge graph according to the user characteristic information;
calculating target travel service information matched with the user characteristic information according to the knowledge chain group;
recommending the target travel service information to the target user.
Optionally, the obtaining of the user characteristic information of the target user includes:
acquiring portrait information and interest information of the target user;
and determining the user characteristic information of the target user according to the portrait information and the interest information.
Optionally, the acquiring portrait information of the target user includes:
acquiring identity information of a target user;
classifying the target users according to the identity information of the target users, and determining the group category of the target users;
and determining the portrait information of the target user according to the group category.
Optionally, the obtaining interest information of the target user includes:
acquiring browsing information and comment information of the target user;
and determining interest information of the target user according to the browsing information and the comment information of the target user.
Optionally, the determining interest information of the target user according to the browsing information and the comment information of the target user includes:
determining the interest field of the target user according to the browsing information of the target user;
determining interest information of the target user according to the comment information of the target user in the interest field;
correspondingly, the determining the user characteristic information of the target user according to the portrait information and the interest information includes:
and determining the portrait information and the interest information as the user characteristic information.
Optionally, the knowledge graph includes a plurality of knowledge chains, where the knowledge chains are used to map relationships between users corresponding to different user characteristic information and travel service information, and the extracting a knowledge chain group from a preset travel knowledge graph according to the user characteristic information includes:
determining a target knowledge chain related to the user characteristic information in a knowledge graph;
and extracting a preset number of target knowledge chains from the knowledge graph to generate a knowledge chain group.
Optionally, the calculating, according to the knowledge chain group, target travel service information matched with the user feature information includes:
converting the knowledge chain group into a multi-dimensional feature matrix;
training a recommendation model matched with the user characteristic information by using a multi-dimensional characteristic matrix;
and inputting the user characteristic information into the recommendation model to obtain the output target travel service information.
Optionally, the converting the knowledge chain set into a multidimensional feature matrix includes:
extracting user characteristic information and travel service information corresponding to each knowledge chain in the knowledge chain group;
generating a plurality of first characteristic rectangles according to the corresponding user characteristic information;
generating a plurality of second feature matrixes according to the corresponding travel service information;
correspondingly combining each first characteristic rectangle and each second characteristic rectangle into a plurality of characteristic matrixes;
and combining the plurality of feature matrixes into a multi-dimensional feature matrix.
Optionally, before the obtaining of the user feature information of the target user, the method further includes:
acquiring the prior knowledge of the travel service;
and constructing a text travel knowledge map according to the text travel service priori knowledge.
According to a second aspect of the disclosed embodiments, the present invention provides a travel service recommendation device, including:
the user characteristic acquisition module is used for acquiring user characteristic information of a target user;
the knowledge chain group extraction module is used for extracting a knowledge chain group from a preset text travel knowledge map according to the user characteristic information;
the travel service calculation module is used for calculating target travel service information matched with the user characteristic information according to the knowledge chain group;
and the text travel service recommending module is used for recommending the target text travel service information to the target user.
Optionally, the user characteristic obtaining module is specifically configured to:
acquiring portrait information and interest information of the target user;
and determining the user characteristic information of the target user according to the portrait information and the interest information.
Optionally, when the user characteristic obtaining module obtains the portrait information of the target user, the user characteristic obtaining module is specifically configured to:
acquiring identity information of a target user;
classifying the target users according to the identity information of the target users, and determining the group category of the target users;
and determining the portrait information of the target user according to the group category.
Optionally, when the user characteristic obtaining module obtains the interest information of the target user, the user characteristic obtaining module is specifically configured to:
acquiring browsing information and comment information of the target user;
and determining interest information of the target user according to the browsing information and the comment information of the target user.
Optionally, when determining the interest information of the target user according to the browsing information and the comment information of the target user, the user characteristic obtaining module is specifically configured to:
determining the interest field of the target user according to the browsing information of the target user;
determining interest information of the target user according to the comment information of the target user in the interest field;
correspondingly, when determining the user feature information of the target user according to the portrait information and the interest information, the user feature acquisition module is specifically configured to:
and determining the portrait information and the interest information as the user characteristic information.
Optionally, the knowledge graph includes a plurality of knowledge chains, the knowledge chains are used to map relationships between users corresponding to different user feature information and travel service information, and the knowledge chain group extraction module is specifically configured to:
determining a target knowledge chain related to the user characteristic information in a knowledge graph;
and extracting a preset number of target knowledge chains from the knowledge graph to generate a knowledge chain group.
Optionally, the travel service calculation module is specifically configured to:
converting the knowledge chain group into a multi-dimensional feature matrix;
training a recommendation model matched with the user characteristic information by using a multi-dimensional characteristic matrix;
and inputting the user characteristic information into the recommendation model to obtain the output target travel service information.
Optionally, when the knowledge chain group is converted into the multidimensional feature matrix, the travel service calculation module is specifically configured to:
extracting user characteristic information and travel service information corresponding to each knowledge chain in the knowledge chain group;
generating a plurality of first characteristic rectangles according to the corresponding user characteristic information;
generating a plurality of second feature matrixes according to the corresponding travel service information;
correspondingly combining each first characteristic rectangle and each second characteristic rectangle into a plurality of characteristic matrixes;
and combining the plurality of feature matrixes into a multi-dimensional feature matrix.
Optionally, the travel service recommendation device further includes a knowledge graph construction module, configured to:
acquiring the prior knowledge of the travel service;
and constructing a text travel knowledge map according to the text travel service priori knowledge.
According to a third aspect of the embodiments of the present disclosure, the present invention provides an electronic apparatus including: a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor according to any one of the first aspect of the embodiments of the present disclosure.
According to a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having stored therein computer-executable instructions, which when executed by a processor, are configured to implement the method for recommending a travel service according to any one of the first aspect of the embodiments of the present disclosure.
According to the text travel service recommendation method, the text travel service recommendation device, the electronic equipment and the storage medium, the user characteristic information of the target user is obtained; extracting a knowledge chain group from a preset text travel knowledge graph according to the user characteristic information; calculating target travel service information matched with the user characteristic information according to the knowledge chain group; the target travel service information is recommended to the target user, and the knowledge chain group extracted from the preset travel knowledge graph according to the user characteristic information has smaller data amount and higher calculation efficiency compared with the original travel knowledge graph, so that the dynamic interaction behavior of the user in the using process can be analyzed in real time and fed back in time, and the accuracy of the result of recommending the travel service to the user is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is an application scenario diagram of a travel service recommendation method according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for recommending a travel service according to an embodiment of the present invention;
fig. 3 is a flowchart of a travel service recommendation method according to another embodiment of the present invention;
FIG. 4 is a flowchart of step S203 in the embodiment of FIG. 3;
FIG. 5 is a flowchart of step S204 in the embodiment shown in FIG. 3;
FIG. 6 is a diagram illustrating the contents of a knowledge chain set provided by an embodiment of the invention;
FIG. 7 is a flowchart of step S208 in the embodiment of FIG. 3;
fig. 8 is a schematic structural diagram of a travel service recommendation device according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a travel service recommendation device according to another embodiment of the present invention;
fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary 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 implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terms to which the present invention relates will be explained first:
knowledge graph: a knowledge graph is a structured semantic knowledge base that describes concepts in the physical world and their interrelationships in symbolic form. The basic composition unit is an entity-relation-entity triple, entities and related attribute value pairs thereof, and the entities are mutually connected through relations to form a network knowledge structure. The basic form of a triple mainly includes a first entity, a relationship, a second entity, and concepts, attributes, attribute values, and the like.
The entity is the most basic element in the knowledge graph, and different relationships exist among different entities. Concepts refer primarily to collections, categories, object types, categories of things, such as people, geographies, etc.; the attributes mainly refer to attributes, characteristics and parameters which the object may have, such as nationality, birthday, and the like; attribute values refer primarily to values of attributes specified by an object, such as china, 1988-09-08, and so on. Each entity may be identified by a globally unique ID, each attribute-Attribute Value Pair (AVP) may be used to characterize an entity's intrinsic properties, and a relationship may be used to connect two entities, characterizing an association between them.
The following describes the technical solutions of the present invention and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
The following explains an application scenario of the embodiment of the present invention:
fig. 1 is an application scenario diagram of a travel service recommendation method according to an embodiment of the present invention, and as shown in fig. 1, the travel service recommendation method according to the embodiment of the present invention is executed on an electronic device, specifically, the electronic device is a server of a website a. After a target user accesses the website A through the terminal device, the server of the website A acquires user characteristic information of the target user, determines target travel service information matched with the user characteristic information according to the user characteristic information, and pushes the target travel service information to the target user, so that the target user can quickly acquire travel service items suitable for the target user.
Fig. 2 is a flowchart of a travel service recommendation method according to an embodiment of the present invention, and as shown in fig. 2, the travel service recommendation method according to the embodiment includes the following steps:
step S101, obtaining user characteristic information of a target user.
The user feature information is information for describing characteristics of the user, and different users can be classified and distinguished by using the user feature information. The user characteristic information may be simple single factor information, such as gender characteristics, and the user characteristic information of the target user is obtained, that is, the gender of the target user is obtained. The user feature information may also be complex multi-factor information, for example, the interest feature, the age feature, and the crowd feature of the user are integrated as the user feature information, and the user feature information of the target user is obtained, that is, a plurality of feature factors such as the interest feature, the age feature, and the crowd feature of the target user are obtained as the user feature information.
Further, the user characteristic information may include objective characteristics of the user itself, for example, a sex characteristic and an age characteristic of the user. Subjective features of the user, such as high-consumption features and young group features, may also be included after the user is classified as required. Therefore, the content of the user feature information can be adjusted according to specific scenes and needs, and the content of the user feature information is not specifically limited here.
And S102, extracting a knowledge chain group from a preset text travel knowledge graph according to the user characteristic information.
The language travel knowledge graph is used for describing the users with different user characteristic information and the knowledge graph of the specific language travel activity content in the language travel activity, and the mapping relation between the users with different user characteristic information and the language travel activity content can be determined according to the language travel index graph.
However, the preset knowledge graph for the travel document contains a huge amount of content, the mapping relationship is extremely complex, and the problem of low calculation efficiency is caused by directly processing or calculating the preset knowledge graph for the travel document.
For the user characteristic information, only a small amount of information is contained, and according to the user characteristic information, the content matched with the user characteristic information of the target user is found from the preset text travel knowledge map to form a plurality of groups of knowledge chains, so that useless information in the preset text travel knowledge map can be filtered, the dimension reduction is performed equivalently, the data volume of subsequent data processing can be effectively reduced, and the data processing efficiency is improved.
For example, if the user characteristic information includes two characteristic elements, namely "interest characteristic" and "gender characteristic", the knowledge chain related to the "interest characteristic" and the "gender characteristic" in the preset knowledge map of the travel is extracted according to the user characteristic information, and the rest knowledge chains not related to the "interest characteristic" and the "gender characteristic" are omitted, so that the data processing efficiency is improved without processing in subsequent calculation.
And step S103, calculating the target travel service information matched with the user characteristic information according to the knowledge chain group.
After the knowledge chain group is determined, the knowledge chain group comprises a plurality of knowledge chains, a set of strategies for recommending the travel service for the target user according to different user characteristic information of the target user can be formed according to the relation elements in each knowledge chain, and at the moment, the target travel service information matched with the user characteristic information is obtained by calculation according to the specific user characteristic information of the user and the strategies for recommending the travel service.
And step S104, recommending the target text travel service information to the target user.
After the target travel service information is obtained, the information is recommended to the target user through different channels, so that the target user can obtain travel service recommendation information matched with the user characteristic information and capable of meeting user requirements, and the target user can quickly obtain travel service items suitable for the target user.
In this embodiment, the user characteristic information of the target user is obtained. And extracting a knowledge chain group from a preset text travel knowledge graph according to the user characteristic information. And calculating the target text travel service information matched with the user characteristic information according to the knowledge chain group. The target travel service information is recommended to the target user, and compared with the original travel knowledge map, the knowledge chain group extracted from the preset travel knowledge map according to the user characteristic information has smaller data volume and higher calculation efficiency, so that the dynamic interaction behavior in the use process of the user can be analyzed in real time and fed back in time, and the accuracy of the result recommended by the travel service to the user is improved.
Fig. 3 is a flowchart of a travel service recommendation method according to another embodiment of the present invention, as shown in fig. 3, the travel service recommendation method according to this embodiment further refines steps S101 to S103 on the basis of the travel service recommendation method according to the embodiment shown in fig. 2, and adds a step of constructing a travel knowledge graph before step S101, so that the travel service recommendation method according to this embodiment includes the following steps:
step S201, acquiring the prior knowledge of the travel service.
Travel service a priori knowledge refers to data that can be used to describe the relationship between different users and travel services. This data can be used as a learning sample to study the relationships between different users and travel services. The form and content of the prior knowledge of the travel service are various, for example, the prior knowledge of the travel service is recorded according to the active or passive acceptance of the travel service by different users. For another example, the data such as the evaluation and click rate of different travel services by different users on the website is used as the prior knowledge of the travel services. The acquisition mode of the prior knowledge of the travel service is various, and the form, the content and the acquisition mode of the prior knowledge of the travel service are not specifically limited.
And S202, constructing a text travel knowledge graph according to the text travel service priori knowledge.
According to the acquired prior knowledge of the travel service, the acquired prior knowledge of the travel service can be used as sample data to analyze and extract characteristics, and a travel knowledge graph is constructed. The construction of the knowledge graph is divided into a bottom-up mode and a top-down mode, the bottom-up mode is a process of extracting a certain rule from the prior knowledge of the existing travel service, finding information with higher confidence coefficient according to the rule and adding the information into the knowledge graph, the top-down mode is a process of finding entities and relationship information from a high-quality website and adding the entities and the relationship information into the knowledge graph, or a combined mode is adopted, for example, the entities are extracted and induced from behavior and comment data of a user in a natural language processing mode, and then verification expansion is carried out on information such as user figures and the like in the top-down mode. And finally, expressing the processed data through uniform resource descriptors and storing the data into graph data to form a travel knowledge graph.
In step S203, image information of the target user is acquired.
The portrait information is information for describing characteristics and needs of users, and users having different characteristics can be classified into different types according to the portrait information. Such as young populations, high consumption populations, price sensitive populations, and the like.
Optionally, as shown in fig. 4, the step S203 of acquiring the image information of the target user includes three specific implementation steps S2031, S2032, and S2033:
step S2031, identity information of the target user is acquired.
Specifically, the identity information is information related to the real identity of the user, and the manner of acquiring the identity information is generally obtained through registration information of the user. For example, sex information, age information, and the like registered by the user. Of course, the identity information of the user may also be obtained by means of third party authorization, for example, the user is a member of the a website, and the identity information is registered in the a website. And the B website obtains the identity information of the user through the authorization of the A website. Here, the specific manner of obtaining the identity information of the target user is not limited.
Step S2032, according to the identity information of the target users, the target users are classified, and the group category of the target users is determined.
The identity information of the user is different, which generally causes different behavior habits and consumption habits of the user. Therefore, after the target users are classified according to the identity information of the target users, the users with different user identities are in the corresponding group categories, and the recommendation of the travel service information matched with the users is facilitated according to the different group categories.
Optionally, the relationship information between the target user and the other users may be determined according to the identity information of the target user, for example, the target user a and the user B are in a friend relationship. The target user and other users with a specific relationship to the target user, such as friends, classmates, and relatives, may be determined to be a group category. Due to the fact that the behavior habits and characteristics of the user are often influenced by relatives and friends in the social network of the user, the target user and other users having specific relations with the target user are determined to be a group category, the actual requirements of the user can be better determined, and the accuracy of determining the characteristic information of the user is improved.
Step S2033, image information of the target user is determined according to the group type.
As the specific group categories have relatively obvious differences and characteristics in behavior habits and consumption habits, the portrait information corresponding to the users of different group categories can be determined according to the differences and the characteristics. For example, in the case of younger age groups, when participating in a travel event, there is a tendency to use a few popular cities and highly popular sights as destinations of the travel event. While some older middle-aged and elderly people tend to take scenic spots of scenic spots, historic sites and natural scenery as destinations of the travel activities. For another example, young people consume more when participating in travel events, and middle-aged and elderly people consume less when participating in travel events. Therefore, the portrait information of young group users and the portrait information of middle-aged and old group users can be determined accordingly.
In the step of the embodiment, different portrait information is generated for the user through the identity information of the target user of the user, the purpose of quickly classifying the user is achieved, the identity information is obtained in a simple mode, and the content is accurate, so that the portrait information processing efficiency and accuracy of the user can be improved.
And step S204, obtaining interest information of the target user.
Specifically, the interest information refers to the interest point information of the user, and in the travel activities, the user often easily receives travel services matched with the interest points. Such as country, region of interest. The interesting activity contents such as food and sports belong to the category of the interesting information.
Optionally, the browsing information and the comment information of the user on the website may represent the content of interest of the user, and therefore, the interest information of the target user may be determined according to the browsing information and the comment information of the target user.
Optionally, as shown in fig. 5, the obtaining of the interest information of the target user in step S204 includes three specific implementation steps of steps S2041, S2042, and S2043:
step S2041, browsing information and comment information of the target user are acquired.
In particular, browsing information and comment information of a user on a website are often direct representations of the user's interest in their content. The browsing information includes the content and duration of the web pages browsed by the user in a period of time.
The comment information is comment content which is published by a user aiming at specific content within a period of time, and comprises non-real-time messages and real-time messages, such as video barrage and the like.
Step S2042, according to the browsing information of the target user, determining the interest field of the target user.
Alternatively, if the target user browses the content of the specific field for a long time or for multiple times, it may be determined that the user is interested in the field, that is, it may be determined that the specific field is an interest field of the target user. For example, the target user can browse the content and information related to the Xinjiang tour for multiple times on the website, and the target user can be determined to be interested in the Xinjiang tour, namely the Xinjiang tour can be used as the field of interest of the target user. For another example, the target user browses the content and information related to the "self-driving tour" on the website for multiple times, and it can be determined that the target user is interested in the "self-driving tour", that is, the "self-driving tour" can be used as the field of interest of the target user.
Step S2043, according to the comment information of the target user in the interest field, the interest information of the target user is determined.
While the user is very interested in a certain area, the user's emotion to the area of interest may be positive or negative, for example, the user searches for "Xinjiang tourism" many times, actually because he has already participated in the literary tourism activities related to "Xinjiang tourism", but has a poor experience, and browses the content many times for complaints and expression of literary tourism activities not satisfying "Xinjiang tourism".
Therefore, on the basis of determining the interest field, the emotional tendency of the user is determined according to the comment information of the target user in the interest field, and the interest information of the target user is further accurately determined.
In the step of the embodiment, the interest information of the user is determined from the two aspects of the interest field and the emotional tendency of the user by acquiring the browsing information and the comment information of the user, so that misjudgment on the real interest intention of the user is avoided, and the accuracy of evaluating the interest information of the user is improved.
Optionally, when the target user does not comment in the interest field, the emotional tendency of the target user to the interest field can be judged through the specific content in the interest field browsed by the target user, and then the interest information of the target user is accurately determined.
Optionally, the interest information includes browsing information and comment information itself.
Optionally, the method for determining the emotional tendency of the user from the comment information of the target user, and the method for judging the emotional tendency of the target user to the interest field from the specific content in the interest field browsed by the target user may adopt a manner of performing natural language processing on the specific content and the comment content and acquiring semantic information and emotional information to realize the judgment of the emotional tendency of the target user, and a specific implementation method thereof is the prior art in the field and is not described herein again.
In step S205, user characteristic information of the target user is determined according to the portrait information and the interest information.
The portrait information of the user and the interest information of the user describe the characteristic information of the user from two dimensions. The portrait information is an approximate classification of user groups, and the interest information is used for screening out user groups with specific interest characteristics on the basis of specific user groups. Therefore, the user characteristic information of the target user is determined according to the two dimensions of the portrait information and the interest information, the effect of accurately evaluating the user characteristic information is achieved, and the accuracy of user characteristic evaluation is improved. Specifically, one optional implementation is to determine portrait information and interest information as user characteristic information.
Optionally, the knowledge graph includes a plurality of knowledge chains, and the knowledge chains are used to map relationships between users corresponding to different user feature information and travel service information.
Step S206, determining a target knowledge chain related to the user characteristic information in the knowledge graph.
The user characteristic information is less than the knowledge graph information amount, a large number of redundant knowledge chains in the knowledge graph are irrelevant to the user characteristic information, the relevant knowledge chains in the knowledge graph are determined according to the user characteristic information, and the subsequent data processing efficiency can be effectively improved.
For example, if the user characteristic information includes the age characteristic and the interest characteristic of the user a, the knowledge chain of the attribute related to the age characteristic and the interest characteristic in the knowledge graph is determined as the target knowledge chain. Other attributes in the knowledge graph are not associated with the knowledge chain, such as the knowledge chain representing the relationship between the record of the travel activity the user last participated in and the recommended travel service, and are not determined as the target knowledge chain.
Step S207, extracting a preset number of target knowledge chains from the knowledge graph, and generating a knowledge chain group.
Optionally, the knowledge chain group includes a plurality of knowledge chains, the number of the knowledge chains is a preset number, and the number may be set according to the number of the target knowledge chains and the specific requirement, which is not specifically limited herein.
Fig. 6 is a content diagram of a knowledge chain set provided by an embodiment of the present invention. As shown in fig. 6, the portrait information in the user feature information includes several feature elements, namely "professional information" and "friend information" of the user. The interest information in the user characteristic information comprises several characteristic elements of 'browsing information' and 'comment information', ten knowledge chains related to the user characteristic information are extracted from the knowledge graph to form a knowledge chain group, wherein the first entity is a user 1, namely a target user. The second entity is service 2, i.e. the target travel service, and the relationships in the knowledge chains one to ten are respectively:
and the first knowledge chain determines a service 2 through the evaluation content of the user 1.
And a second knowledge chain, namely, a friend user 2 is determined according to the portrait information of the user 1, and a service 2 is determined according to the browsing content of the user 2.
And a third knowledge chain, namely determining the service 1 through the browsing content of the user 1. Service 2 is determined by co-browsing users 2 of service 1.
And fourthly, determining the service 1 through browsing of the user 1, determining the company 1 through attribution of the service 1, and determining the travel service belonging to the company 2 as the service 2.
And a fifth knowledge chain, determining the service 1 through browsing of the user 1, determining the location 1 according to the position of the service 1, and determining the travel service located at the same location as the service 2.
And a sixth knowledge chain, namely determining the service 1 through browsing of the user 1, determining the consumption 1 according to the consumption condition of the service 1, and determining the travel service with similar consumption condition as the service 2.
And a seventh knowledge chain, determining that the occupation of the user is occupation 1 according to the portrait information of the user 1, determining a user 2 with the same occupation according to the occupation 1, and determining a service 2 according to the browsing content of the user 2.
And eighthly, determining the service 1 through the forward emotion in the evaluation 1 written by the user 1, determining the user 2 writing the evaluation 2 according to the evaluation 2 with the forward emotion, and determining the service 2 according to the browsing content of the user 2.
And a ninth knowledge chain, namely determining the mentioned product 1 through the content in the evaluation 1 written by the user 1, determining the user 2 writing the evaluation 2 according to the evaluation 2 also referring to the product 1, and determining the service 2 according to the browsing content of the user 2.
And a knowledge chain ten for determining the service 1 and the product through the evaluation and the mentioned content in the evaluation 1 written by the user 1. The user 2 who drafts the rating 2 is determined from the rating 2 having similar rating and content reference to the service 1 and the product, and the service 2 is determined from the browsing content of the user 2.
And step S208, converting the knowledge chain group into a multi-dimensional feature matrix.
And determining the target recommended travel service according to the knowledge chain group, wherein the knowledge chain group needs to be subjected to matrixing and then is calculated.
Optionally, as shown in fig. 7, the step S208 of converting the knowledge chain set into a multidimensional feature matrix includes five specific implementation steps S2081, S2082, S2083, S2084, and S2085:
step S2081, extracting user characteristic information and travel service information corresponding to each knowledge chain in the knowledge chain group.
The knowledge chain group comprises a plurality of groups of knowledge chains, each knowledge chain is equivalent to a group of user-service pairs, and the first entity and the second entity at two ends of the knowledge chain are user characteristic information and travel service information respectively. And extracting the first entity and the second entity at two ends of the knowledge chain to obtain the user characteristic information and the travel service information.
Step S2082, a plurality of first characteristic rectangles are generated according to the corresponding user characteristic information.
Step S2083, a plurality of second feature matrixes are generated according to the corresponding travel service information.
Step S2084, the first feature rectangles and the second feature rectangles are correspondingly combined into a plurality of feature matrices.
Step S2085, combining the plurality of feature matrices into a multi-dimensional feature matrix.
Specifically, the user characteristic information and the travel service information are respectively matrixed, wherein the user characteristic information is represented by u, the travel service information is represented by b, and the ith user characteristic information is uiService information b of Chinese traveli. Integrating user characteristic information and travel service informationThen, L group characteristics can be obtained, where L is the number of knowledge chains in the preset knowledge chain group, as shown in formula 1:
Figure BDA0002348222100000141
where F is the rank of the matrix, ui (l)And bj (l)Respectively representing the user characteristic information and the travel service information extracted from the ith knowledge chain. The different feature matrices have different ranks, and the feature matrices are fixed to the same value in this embodiment. x is the number ofnRepresenting the feature vector of the n-th sample after combination. Each user-service pair may be represented by a feature matrix of dimension L x F.
And step S209, training a recommendation model matched with the user characteristic information by using the multi-dimensional characteristic matrix.
After a multi-dimensional feature matrix is generated, modeling is performed on a second-order relation between features, and low-order decomposition is performed on second-order parameters in the method as shown in a formula (2):
Figure BDA0002348222100000144
wherein, w0Is the bias, d 2LF denotes all the features of the matrix consisting of L and the knowledge chain, w ∈ RdDenotes a first order parameter, V ═ Vi]∈Rd×kSecond order parameter, v, representing cross-point multiplication of different parametersiThe ith row, x, of the matrix Vi nIs xnThe ith feature of (1). The parameters are learned by minimizing the mean square loss function, as shown in equation (3):
Figure BDA0002348222100000145
in the formula ynObtained from the nth sample, N being the total number of samples.
Optionally, some implicit features between the user and the service are constructed in a knowledge chain manner, in order to prevent overfitting of the training result, the embodiment may solve the problem of feature combination under the sparse matrix in a manner of factoring factors, eliminate useless knowledge chains, abandon feature groups useless for similarity calculation, and optimize the objective function to be a non-convex and non-smooth problem, where the regularized second-order relationship is shown in formula (4):
Figure BDA0002348222100000151
in the formula phivAnd phiwRespectively carrying out regularized first-order and second-order parameters, and finally carrying out iterative computation by a near-end gradient method. And obtaining first-order and second-order parameters through iterative training, when a new user is input, bringing a knowledge chain formed by the user and the commodity into a model to obtain user-service similarity, and finally outputting the association probability of the user and the service, wherein the greater the probability is, the tighter the relationship is, sorting is performed according to the probability, and recommendation is performed before ranking.
And step S210, inputting the user characteristic information into a recommendation model to obtain the output target travel service information.
And after the recommendation model is obtained, inputting specific user characteristic information into the recommendation model to obtain the target travel service matched with the requirements of the target user.
In the embodiment, the portrait information and the user operation of the user are extracted in the interaction process, the group characteristics and the interest information of the user are collected, and a knowledge chain is constructed by combining a knowledge graph algorithm, so that data are collected, processed and analyzed in real time to infer the action characteristics of the user in the continuous interaction process of the system and the user, recommended content is fed back in time according to the deviation of the user, the content quality is optimized, system content which is more matched with the cognitive requirements of the user is provided, the man-machine interaction process is more intelligent, the user satisfaction is improved, and the user experience is optimized.
Fig. 8 is a schematic structural diagram of a travel service recommendation device according to an embodiment of the present invention, and as shown in fig. 8, a travel service recommendation device 3 according to this embodiment includes:
the user characteristic obtaining module 31 is configured to obtain user characteristic information of the target user.
And the knowledge chain group extraction module 32 is used for extracting the knowledge chain group from the preset text travel knowledge graph according to the user characteristic information.
And the travel service calculation module 33 is configured to calculate target travel service information matched with the user feature information according to the knowledge chain group.
And the text travel service recommending module 34 recommends the target text travel service information to the target user.
The user characteristic obtaining module 31, the knowledge chain group extracting module 32, the travel service calculating module 33 and the travel service recommending module 34 are connected in sequence. The travel service recommendation apparatus 3 provided in this embodiment may execute the technical solution of the method embodiment shown in fig. 2, and the implementation principle and the technical effect are similar, which are not described herein again.
Fig. 9 is a schematic structural diagram of a travel service recommendation apparatus according to another embodiment of the present invention, and as shown in fig. 9, the travel service recommendation apparatus 4 according to this embodiment further includes a knowledge graph construction module 41, on the basis of the travel service recommendation apparatus shown in fig. 8, for:
and acquiring the prior knowledge of the travel service.
And constructing a text travel knowledge map according to the text travel service priori knowledge.
Optionally, the user characteristic obtaining module 31 is specifically configured to:
and acquiring portrait information and interest information of a target user.
And determining user characteristic information of the target user according to the portrait information and the interest information.
Optionally, the user characteristic obtaining module 31 is specifically configured to, when obtaining the portrait information of the target user:
and acquiring the identity information of the target user.
And classifying the target users according to the identity information of the target users, and determining the group category of the target users.
And determining the portrait information of the target user according to the group category.
Optionally, when the user characteristic obtaining module 31 obtains interest information of the target user, it is specifically configured to:
and acquiring browsing information and comment information of the target user.
And determining interest information of the target user according to the browsing information and the comment information of the target user.
Optionally, when determining interest information of the target user according to the browsing information and the comment information of the target user, the user characteristic obtaining module 31 is specifically configured to:
and determining the interest field of the target user according to the browsing information of the target user.
And determining interest information of the target user according to the comment information of the target user in the interest field.
Correspondingly, when determining the user feature information of the target user according to the portrait information and the interest information, the user feature obtaining module 31 is specifically configured to:
and determining the portrait information and the interest information as the user characteristic information.
Optionally, the knowledge graph includes a plurality of knowledge chains, the knowledge chains are used to map relationships between users corresponding to different user feature information and travel service information, and the knowledge chain group extraction module 32 is specifically configured to:
and determining a target knowledge chain related to the user characteristic information in the knowledge graph.
And extracting a preset number of target knowledge chains from the knowledge graph to generate a knowledge chain group.
Optionally, the travel service calculation module 33 is specifically configured to:
and converting the knowledge chain group into a multi-dimensional feature matrix.
And training a recommendation model matched with the user characteristic information by using the multi-dimensional characteristic matrix.
And inputting the user characteristic information into a recommendation model to obtain the output target text travel service information.
Optionally, the travel service calculation module 33 is specifically configured to, when converting the knowledge chain group into the multidimensional feature matrix:
and extracting user characteristic information and travel service information corresponding to each knowledge chain in the knowledge chain group.
And generating a plurality of first characteristic rectangles according to the corresponding user characteristic information.
And generating a plurality of second feature matrixes according to the corresponding travel service information.
And correspondingly combining each first characteristic rectangle and each second characteristic rectangle into a plurality of characteristic matrixes.
And combining the plurality of feature matrices into a multi-dimensional feature matrix.
The knowledge graph building module 41, the user characteristic obtaining module 31, the knowledge chain group extracting module 32, the travel service calculating module 33, and the travel service recommending module 34 are connected in sequence. The travel service recommendation device 4 provided in this embodiment may execute the technical solutions of the method embodiments shown in fig. 3 to fig. 7, and the implementation principles and technical effects thereof are similar and will not be described herein again.
Fig. 10 is a schematic view of an electronic device according to an embodiment of the present invention, and as shown in fig. 10, the electronic device according to the embodiment includes: a memory 51, a processor 52 and a computer program.
The computer program is stored in the memory 51 and configured to be executed by the processor 52 to implement the travel service recommendation method provided in any one of the embodiments corresponding to fig. 2-7 of the present invention.
The memory 51 and the processor 52 are connected by a bus 53.
The relevant description may be understood by referring to the relevant description and effect corresponding to the steps in fig. 2 to fig. 7, and redundant description is not repeated here.
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement a method for recommending a travel service according to any embodiment of the present invention corresponding to fig. 2-7.
The computer readable storage medium may be, among others, ROM, Random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of modules is merely a division of logical functions, and an actual implementation may have another division, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims (12)

1. A method for recommending travel services, the method comprising:
acquiring user characteristic information of a target user;
extracting a knowledge chain group from a preset text travel knowledge graph according to the user characteristic information;
calculating target travel service information matched with the user characteristic information according to the knowledge chain group;
recommending the target travel service information to the target user.
2. The method of claim 1, wherein the obtaining user characteristic information of the target user comprises:
acquiring portrait information and interest information of the target user;
and determining the user characteristic information of the target user according to the portrait information and the interest information.
3. The method of claim 2, wherein obtaining the portrait information of the target user comprises:
acquiring identity information of a target user;
classifying the target users according to the identity information of the target users, and determining the group category of the target users;
and determining the portrait information of the target user according to the group category.
4. The method of claim 3, wherein the obtaining interest information of the target user comprises:
acquiring browsing information and comment information of the target user;
and determining interest information of the target user according to the browsing information and the comment information of the target user.
5. The method of claim 4, wherein the determining interest information of the target user according to browsing information and comment information of the target user comprises:
determining the interest field of the target user according to the browsing information of the target user;
determining interest information of the target user according to the comment information of the target user in the interest field;
correspondingly, the determining the user characteristic information of the target user according to the portrait information and the interest information includes:
and determining the portrait information and the interest information as the user characteristic information.
6. The method of claim 1, wherein the knowledge graph includes a plurality of knowledge chains, the knowledge chains are used for mapping relationships between users corresponding to different user characteristic information and travel service information, and the extracting a knowledge chain group from a preset travel knowledge graph according to the user characteristic information includes:
determining a target knowledge chain related to the user characteristic information in a knowledge graph;
and extracting a preset number of target knowledge chains from the knowledge graph to generate a knowledge chain group.
7. The method of claim 1, wherein calculating the target travel service information matching the user characteristic information according to the knowledge chain set comprises:
converting the knowledge chain group into a multi-dimensional feature matrix;
training a recommendation model matched with the user characteristic information by using a multi-dimensional characteristic matrix;
and inputting the user characteristic information into the recommendation model to obtain the output target travel service information.
8. The method of claim 7, wherein transforming the set of knowledge chains into a multi-dimensional feature matrix comprises:
extracting user characteristic information and travel service information corresponding to each knowledge chain in the knowledge chain group;
generating a plurality of first characteristic rectangles according to the corresponding user characteristic information;
generating a plurality of second feature matrixes according to the corresponding travel service information;
correspondingly combining each first characteristic rectangle and each second characteristic rectangle into a plurality of characteristic matrixes;
and combining the plurality of feature matrixes into a multi-dimensional feature matrix.
9. The method according to claim 1, further comprising, before said obtaining the user characteristic information of the target user:
acquiring the prior knowledge of the travel service;
and constructing a text travel knowledge map according to the text travel service priori knowledge.
10. A travel service recommendation device, the device comprising:
the user characteristic acquisition module is used for acquiring user characteristic information of a target user;
the knowledge chain group extraction module is used for extracting a knowledge chain group from a preset text travel knowledge map according to the user characteristic information;
the travel service calculation module is used for calculating target travel service information matched with the user characteristic information according to the knowledge chain group;
and the text travel service recommending module is used for recommending the target text travel service information to the target user.
11. An electronic device, comprising: a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the travel service recommendation method of any of claims 1-9.
12. A computer-readable storage medium having stored thereon computer-executable instructions for implementing the method of travel service recommendation of any one of claims 1-9 when executed by a processor.
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