CN105550312B - Context information processing method and device - Google Patents
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Abstract
The invention discloses a method and a device for processing context information. The method comprises the following steps: the following processing is respectively carried out on the context information of each dimension: dividing each vector into different vector groups according to the closeness degree among the vectors included in the dimension context information; taking the Cartesian product of vector groups divided by the context information of each dimension as an abstract vector to obtain the context information of the abstract dimension; and dividing the abstract vectors into different application scenes according to the tightness degree between the abstract vectors, and establishing a mapping relation between the abstract vectors and the application scenes. Based on the scheme of the invention, the method and the device are not only beneficial to realizing the objectivity and the accuracy of personalized recommendation, but also beneficial to simplifying the personalized recommendation process.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method and an apparatus for processing context information.
Background
With the continuous development of information technology, in order to better serve users and improve user experience, personalized recommendation technology is developed to provide personalized recommendation content meeting the needs of users. Generally, the personalized recommendation technology generates personalized recommendation content for a user based on user data such as user interest characteristics and action behaviors by combining a certain data analysis method.
When the personalized recommendation is performed, the user data can be combined, the recommendation algorithm is used for calculating the score value of the potential article by the user, the potential article with the higher score value is used as the recommendation content and is provided for the user, and the higher score value can be understood as exceeding the preset value. That is, the recommendation method mainly studies how to associate the user data with the potential item, and establish a binary relationship between the user and the item so as to perform personalized recommendation.
In the practical application process, people are aware of the context information corresponding to the user data, and the method is also very important for improving the accuracy of personalized recommendation. For example, when capturing the click behavior of the user, the context information corresponding to the click behavior, such as time, location, type of device used by the user, and device networking manner, may also be obtained at the same time. Each type of context information can be regarded as a dimension, and a specific value of each type of context information can be regarded as a vector in the dimension. For example, in the time dimension, 5 vectors in the morning, noon, afternoon, evening, and late night may be included.
How to reasonably process the multi-dimensional context information so as to perform objective and accurate personalized recommendation for the user based on the multi-dimensional context information becomes a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a method and a device for processing context information, which comprehensively consider the context information of each dimension, reduce the multi-dimension context information into an abstract dimension context of a single dimension, and establish a mapping relation between an abstract vector and an application scene.
The embodiment of the invention provides a context information processing method, which comprises the following steps:
the following processing is respectively carried out on the context information of each dimension: dividing each vector into different vector groups according to the closeness degree among the vectors included in the dimension context information;
taking the Cartesian product of vector groups divided by the context information of each dimension as an abstract vector to obtain the context information of the abstract dimension;
and dividing the abstract vectors into different application scenes according to the tightness degree between the abstract vectors, and establishing a mapping relation between the abstract vectors and the application scenes.
Optionally, for each dimension context information, a correspondence between an article, a vector, and a score value is pre-established, and then, according to a degree of closeness between each vector included in the dimension context information, the dividing of each vector into different vector groups includes:
carrying out Euclidean distance calculation by using the scoring values of the article under any two vectors to obtain the distance between any two vectors;
and dividing each vector into different vector groups by clustering analysis by using the distance between any two vectors.
Optionally, according to the article, determining a partition granularity of a vector in the dimension context information.
Optionally, the dividing the respective vectors into different vector groups by cluster analysis includes:
obtaining a vector group partition instruction, the vector group partition instruction comprising a specified number of vector groups;
and adjusting the number of the vector groups divided by the clustering analysis according to the number of the specified vector groups, and dividing each vector into different vector groups.
Optionally, a corresponding relationship between the application scenario and the recommended content is established, and the method further includes:
obtaining multidimensional context information corresponding to user data, searching an abstract vector matched with the multidimensional context information, and determining an application scene corresponding to the multidimensional context information;
and sending the recommended content corresponding to the determined application scene to the user.
An embodiment of the present invention further provides a context information processing apparatus, where the apparatus includes:
the vector group dividing unit is used for respectively processing the context information of each dimension as follows: dividing each vector into different vector groups according to the closeness degree among the vectors included in the dimension context information;
an abstract dimension obtaining unit, configured to obtain context information of an abstract dimension by taking a cartesian product of a vector group divided by the context information of each dimension as an abstract vector;
and the mapping relation establishing unit is used for dividing the abstract vectors into different application scenes according to the tightness degree between the abstract vectors and establishing the mapping relation between the abstract vectors and the application scenes.
Optionally, for each dimension context information, a correspondence between an article, a vector, and a score value is pre-established, and the vector group dividing unit includes:
the distance calculation unit is used for performing Euclidean distance calculation by using the scoring values of the article under any two vectors to obtain the distance between any two vectors;
and the clustering analysis unit is used for dividing each vector into different vector groups through clustering analysis by using the distance between any two vectors.
Optionally, the vector group dividing unit further includes:
and the granularity determining unit is used for determining the division granularity of the vector in the dimension context information according to the article.
Optionally, the cluster analysis unit is specifically configured to obtain a vector group partitioning instruction, where the vector group partitioning instruction includes a specified vector group number; and adjusting the number of the vector groups divided by the clustering analysis according to the number of the specified vector groups, and dividing each vector into different vector groups.
Optionally, the apparatus further includes:
the searching unit is used for acquiring multi-dimensional context information corresponding to user data, searching an abstract vector matched with the multi-dimensional context information and determining an application scene corresponding to the multi-dimensional context information;
and the sending unit is used for sending the recommended content corresponding to the determined application scene to the user.
According to the technical scheme, the multidimensional context information can be reduced into the single-dimensional abstract dimension context information aiming at the multidimensional context information acquired in the user data capturing process, and then the abstract dimension context information is subjected to clustering analysis to obtain the mapping relation between the abstract vector and the application scene included by the abstract dimension context information. The scheme of the invention comprehensively considers the context information of each dimension to finally obtain the mapping relation, and the processing process is not influenced by human factors, so that the method is favorable for realizing the objectivity and the accuracy of personalized recommendation when the personalized recommendation is carried out based on the mapping relation. In addition, based on the mapping relationship, the personalized recommendation is realized mainly in a table look-up mode, and the personalized recommendation process is facilitated to be simplified.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a context information processing method of the present invention;
fig. 2 is a schematic structural diagram of the context information processing apparatus according to the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
Referring to fig. 1, a flow chart illustrating a context information processing method of the present invention may include:
s101, respectively processing the context information of each dimension as follows: and dividing each vector into different vector groups according to the closeness degree among the vectors included in the dimension context information.
In order to improve the accuracy of personalized recommendation, the context information corresponding to the user data can be captured while the user data is captured. For example, for a user a clicking to view a video a on a webpage, the user data may be embodied as a viewing video a, and the context information corresponding to the user data may be embodied as: the time is 20:30, the place is Beijing, the equipment type is pad, the video playing source is Youkou, and the networking mode is wifi connection. The above examples include time, place, device type, video playing source, networking mode and 5-dimensional context information.
It should be noted that, in addition to the user data and the context information, the score value of the video a by the user a can be captured. For example, the score value may be a score given by the user a after watching the video a, and if the user a clicks 3 points in 1-5 points, the score value of the user for the video a is 3 points. Or, the score value may also be a favorite degree of the user a for the video a, such as a favorite representing 1 score and a dislike representing 0 score, and if the user a clicks the favorite, the score value of the user for the video a is 1 score. The embodiment of the invention does not specifically limit the specific setting mode of the score value, the mode of capturing the score value and the like, and can be determined according to the actual application condition.
In summary, for the user a clicking on the video a on the viewing webpage, the following set of information is obtained: user A-video A-20: 30-Beijing-pad-you Ku-wifi connection-credit value is 3, and for this group of information, it can be understood that: user-item-multi-dimensional context information contextn-a score value, where n represents the number of dimensions of the context information.
According to the process, the information that the user A watches the video in other time periods all day and the information that other users watch the video all day can be obtained. For example, in addition to the above-mentioned set of information captured by the user a watching the video a, a set of information captured by the user B watching the video a is captured: user B-video a-20: 10-shenyang-pad-you ku-wifi connection-score value 4, user C watches a set of information of video a: user C-video a-20: 15-beijing-phone-you cool-wifi connection-rating value 5, etc., and will not be further illustrated here.
In the above example, by capturing information of a large number of users watching a video, context information of 5 dimensions can be obtained, and the context information of each dimension includes a plurality of vectors. For example, the time dimension may be defined as C1 and includes nC1A time vector; the place dimension may be defined as C2 and includes nC2A location vector; by analogy, the device type, the video playing source and the networking mode can be sequentially defined as C3, C4 and C5, and the number of vectors correspondingly included in each dimension is nC3、nC4、nC5。
For example, the granularity of partitioning the vectors in each dimension context information may be a minimum granularity; or, the division granularity of the vector in the context information of each dimension can be determined according to the article. For example, if the item is of a type that changes slowly over the time dimension, such as for purchase behavior analysis of apparel items, the vector granularity for the time dimension may be divided into 1 day; if the item is of a type that changes faster over the time dimension, such as for the viewing behavior of a video item, the vector granularity for the time dimension may be divided into 30min, and so on. Generally, the finer the vector granularity is, the more the number of vectors is, the more the objective grouping of the vectors can be found by the clustering analysis, but the calculation amount is increased, and for this, the practical application can be combined, and the dividing method of the vector granularity in the embodiment of the present invention is not particularly limited.
The scheme of the invention aims to reduce the multi-dimensional context information into the single-dimensional context information through processing, and is helpful for simplifying the process of personalized recommendation based on the scheme of the invention.
Based on the method, the context information of each dimension can be grouped first, and reasonable grouping of the vectors under each dimension can be found. Specifically, the degree of closeness between every two vectors of the context information of each dimension may be calculated, and vector grouping may be implemented according to the degree of closeness. For example, the degree of closeness between every two vectors may be represented as the similarity between the vectors, and generally, the larger the value of the similarity is, the closer the two vectors are; or, the closeness between every two vectors can also be represented as the distance between the vectors, and generally, a smaller distance value indicates that the two vectors are closer.
If the degree of closeness is expressed by the similarity between vectors, the cluster analysis can be performed by linear regression to obtain a vector group.
If the closeness is expressed in terms of the distance between vectors, the grouping of vectors can be done in the following way: carrying out Euclidean distance calculation by using the scoring values of the article under any two vectors to obtain the distance between any two vectors; and dividing each vector included by the dimension context information into different vector groups through clustering analysis by using the distance between any two vectors. For example, the clustering analysis may be implemented by means of K-means, K-medoids, etc., which is not specifically limited in the embodiments of the present invention.
Taking the time dimension as an example, the scoring values 1 of different users for the article 1 under the time vector 1 and the scoring values 2 of different users for the article 1 under the time vector 2 can be obtained, and then the euclidean distance calculation is performed on the scoring values under the two time vectors to obtain the distance between the time vector 1 and the time vector 2. By analogy, the distance between any two time vectors under the time dimension can be obtained, and then the vectors with the shorter distance are divided into a vector group through cluster analysis. As an example, the pearson correlation coefficient may be used to calculate the distance r between vectors, which may be represented by the following formula, taking a time vector as an example.
Wherein N represents the number of score values; x is the number ofiRepresents the value of credit under time vector 1;
represents the average score value under time vector 1; y isiRepresents the value of credit under time vector 2;
represents the average score value under time vector 2.
In the embodiment of the present invention, the value of credit 1 of the item 1 under the time vector 1 can be understood as the user-item-context capturednIn value, the value is used as the value of the time dimension, namely user-item-context, by ignoring context information of other dimensionstime—value。
For example, the time dimension takes 1h as the vector granularity, and the data shown in the following table can be obtained for the information when the user captured at 20: 00-21: 00 watches the video A and the information when the user captured at 21: 00-22: 00 watches the video A.
Degree of closeness r between time vectors T1 and T2:
in conclusion, the closeness r between T1 and T2 is 0.866.
The calculation process is explained by taking the compactness of the time vector as an example, and in combination with practical applications, the compactness of the vector in other dimensions can be calculated and obtained by referring to the above manner, which is not illustrated here.
In addition, in the embodiment of the present invention, the closer distance may be understood as that the distance between the two vectors does not exceed a preset distance value. Optionally, the preset distance value may be set in combination with an application situation, and in the clustering analysis process, vector grouping is performed according to the preset distance value; or, the number of the designated vector groups may be set in combination with the application, and a suitable preset distance value may be determined according to the number of the designated vector groups and the calculated distance between every two vectors in the clustering analysis process, so as to implement vector grouping.
For example, if the vector partition granularity of the time dimension is 1h, after the above clustering process, the time vectors 8: 00-9: 00, 11: 00-13: 00, and 17: 00-18: 00 may be partitioned into a vector group, which indicates that the behavior habits of the users in these time periods are relatively similar, and clustering and merging may be performed. In this respect, it can be understood that the above time periods belong to non-working time, and the user behaviors tend to be consistent. Compared with a grouping mode of manually dividing time dimension into morning, noon, afternoon and evening, the grouping obtained by the scheme of the invention can objectively and accurately reflect the behavior habits of the user, and the accuracy of personalized recommendation based on the scheme of the invention is improved based on accurate grasp of the behavior habits of the user.
Thus, the method can also be used for the places, the types of equipment, the video playing sources,The networking method is to perform vector grouping on the context information of the 4 dimensions, which can be specifically referred to the above description and is not described in detail here. For example, grouping the time dimension clusters to obtain KC14 dimensionality clustering groups of the vector group, the place, the equipment type, the video playing source and the networking mode are grouped to obtain KC2、KC3、KC4、KC5A set of vectors. Typically, the number of vector groups obtained by cluster analysis is less than the number of vectors included in the dimension context information.
It should be noted that, with respect to the specific process of dividing the vectors into different vector groups by clustering analysis, reference is made to the following description, and details thereof will not be provided here.
And S102, taking the Cartesian product of the vector groups divided by the context information of each dimension as an abstract vector, and obtaining the context information of the abstract dimension.
S103, dividing the abstract vectors into different application scenes according to the tightness degree between the abstract vectors, and establishing the mapping relation between the abstract vectors and the application scenes.
S101, after clustering grouping of the context information of each dimension is obtained, the obtained vector group can be used for establishing context information of a single dimension, namely the context information of the abstract dimension in the embodiment of the invention. Specifically, in order to obtain any combination between vector groups of multi-dimensional context information, a cartesian product of the vector groups divided by the context information of each dimension may be used as an abstract vector of the context information of an abstract dimension. Typically, the abstract vector includes at least one vector in each dimension context information.
For example, the following set of relationships may exist for abstract vector 1: article-time vector group 1-location vector group 1-device type vector group 1-video playing source vector group 1-networking mode vector group 1-score value 1, for which we can simply understand that: item-abstract vector 1-score value 1.
The abstract dimension context information is used as single dimension context information, and the introduction at S101 can be referred to calculate the degree of closeness between every two abstract vectors, and then abstract vector grouping is performed according to the degree of closeness, and each abstract vector group corresponds to one application scene, so that the mapping relationship between the abstract vectors and the application scenes can be established. For example, the mapping relationship may be embodied as: abstract vector set 1-application scenario 1, typically, each abstract vector set includes at least one abstract vector.
Similar to the description of S101, the closeness between every two abstract vectors in the present invention may be represented as a similarity between the abstract vectors, or may also be represented as a distance between the abstract vectors, which is not specifically limited in the embodiment of the present invention.
In addition, in the embodiment of the present invention, the application scene may be embodied as an actual scene, such as a current affairs scene, an entertainment scene, and the like; or, the application scenario may also be embodied as a scenario number, such as application scenario 1, application scenario 2, and the like, and the concrete representation form of the application scenario may not be limited in the embodiment of the present invention.
In conclusion, the processing process of the multi-dimensional context information is realized. Aiming at the multi-dimensional context information acquired in the process of capturing the user data, the multi-dimensional context information can be reduced to be single-dimensional context information, then the single-dimensional context information is subjected to clustering analysis, the mapping relation between the context information and the application scene is acquired, and the method is beneficial to simplifying the process of carrying out personalized recommendation based on the scheme of the invention.
Furthermore, the scheme of the invention realizes grouping of the vectors included in the context information of each dimension based on the vector compactness and cluster analysis, the grouping does not depend on any human factor, can truly reflect the behavior habit of the user, has objectivity, is also helpful for discovering the implicit grouping possibility, and is helpful for improving the rationality of the grouping.
Furthermore, the scheme of the invention realizes grouping of the abstract vectors included in the abstract dimension context information based on the compactness of the abstract vectors and cluster analysis, and is also favorable for realizing a reusable application scene. That is to say, under the condition that the behavior habits of most users do not change greatly, the mapping relationship obtained by the scheme of the invention can be maintained to be effective in a period of time, and the mapping relationship does not need to be adjusted frequently.
In the embodiment of the present invention, the vectors may be divided into different vector groups in various ways, which can be specifically described as follows.
In the first mode, vectors included in the context information of each dimension are grouped according to a preset distance value. For example, the same preset distance value may be set for each dimension context information, or different preset distance values may be set for different dimension context information, which may be determined by combining with practical applications, and the embodiment of the present invention may not be specifically limited. In this way, as long as the distance between two vectors does not exceed the preset distance value, the two vectors can be divided into one vector group, based on this principle, grouping of vectors included in context information of each dimension can be realized, and the number of vector groups obtained by this way is indefinite.
And in the second mode, the vectors included in the context information of each dimension are grouped according to the number of the specified vector groups. For example, the same number of specified vector groups may be set for each dimension context information, or different numbers of specified vector groups may be set for different dimension context information, which may be determined according to practical applications, and the embodiment of the present invention may not be particularly limited. In addition, the number of the specified vector groups may be obtained through an externally input vector group dividing instruction, or the number of the specified vector groups may be stored locally in a preconfigured manner and read locally when needed. In this way, a suitable preset distance value can be determined according to the calculated distance between every two vectors and the designated vector group number, and then vector grouping is performed based on the preset distance value, so that the vector group number after context information grouping can be controlled.
And the third mode and the second mode are matched with each other to group the vectors included by the context information of each dimension. Specifically, vectors included in the context information of each dimension can be grouped through the process shown in the first mode, if the obtained vector groups are unreasonable, if the number of the vector groups is too large, the vector groups can be further clustered, and the number of the component groups can be properly reduced through the second mode, so that the vector groups are more reasonable; alternatively, if the number of vector groups is too small, which may mask some of the groups during clustering, then the number of component groups can be increased appropriately by way of two.
In a specific application process, a suitable manner may be selected to perform vector grouping in combination with an actual situation, which is not specifically limited in the embodiment of the present invention. In addition, the grouping process for the abstract vectors can also be implemented by referring to the above-mentioned manner, and is not described herein again.
As an example, after obtaining the mapping relationship between the abstract vector and the application scene according to the scheme shown in fig. 1, personalized recommendation may be implemented by using the mapping relationship. For example, the corresponding relationship between the application scene and the recommended content may be pre-established, so that a set of relationships may be obtained: abstract vector-application scenario-recommended content. As an example, the correspondence between the application scenario and the recommended content may be established through a collaborative filtering technique: user-item-context based on capturenAnd value, calculating an estimated score value of the potential article in the application scene by using a collaborative filtering algorithm, and if the estimated score value is higher, using the potential article as the recommended content of the application scene.
Specifically, when personalized recommendation is required, multidimensional context information corresponding to user data can be obtained, an abstract vector matched with the multidimensional context information is searched, and an application scene corresponding to the multidimensional context information is determined; and sending the recommended content corresponding to the determined application scene to the user.
At present, when a traditional scheme carries out an individualized recommendation scheme based on a collaborative filtering algorithm, after multi-dimensional context information of a user U is obtained, a score value of a potential article in each dimension context information of the user U can be predicted by using an analysis result of an active user, then an estimated score value of the potential article for the user U is calculated by combining preset weights of each dimension context information, and if the estimated score value is higher, the potential article can be sent to the user U as a recommendation content.
Compared with the traditional scheme, the personalized recommendation scheme provided by the invention can determine the recommendation content matched with the multi-dimensional context information only by table lookup, has a simple implementation process and a small calculation amount, and is beneficial to improving the efficiency of personalized recommendation. In addition, in the practical application process, limited by the capture technology or the user privacy setting, the context information of the user in some dimensions may not be obtained, so that a null value appears in the dimension context information, that is, the problem of sparsity of the context information occurs, and if the user privacy setting is set to prohibit obtaining the geographical location information, the context information of the place dimension may not be captured. For this reason, the traditional personalized recommendation scheme can only discard the part of null values, which inevitably affects the prediction of the score value of the potential item in the dimension context information by using the analysis result of the active user, thereby affecting the calculation accuracy of the estimated score value, and also can perform personalized recommendation which is not suitable for the user requirement under severe conditions, thereby affecting the user experience. In contrast, when the multi-dimensional context information is processed, each dimension in the multi-dimensional context information, namely, the mapping relation between the abstract vector and the application scene, is obtained after the condition of each dimension context information is integrated, even if the sparsity problem occurs when the personalized recommendation is performed for a specific user U, the accuracy of the recommended content obtained based on the mapping relation is improved more than that of the traditional scheme by considering the context information of all dimensions involved in the mapping relation, and the problem of the sparsity of the context information is solved. In addition, if the context information of the new dimension is obtained when the user data is captured, the context information of the new dimension can be classified under the existing application scene through the processing of the scheme of the invention, a new application scene does not need to be established for the context information of the new dimension, and the problem of sparsity of the context information is also solved.
Corresponding to the method shown in fig. 1, an embodiment of the present invention further provides a context information processing apparatus 200, which, referring to the schematic diagram shown in fig. 2, may include:
a vector group dividing unit 201, configured to perform the following processing on the context information of each dimension: dividing each vector into different vector groups according to the closeness degree among the vectors included in the dimension context information;
an abstract dimension obtaining unit 202, configured to obtain context information of an abstract dimension by taking a cartesian product of a vector group obtained by dividing the context information of each dimension as an abstract vector;
a mapping relationship establishing unit 203, configured to divide the abstract vectors into different application scenes according to the closeness degree between the abstract vectors, and establish a mapping relationship between the abstract vectors and the application scenes.
Optionally, for each dimension context information, a correspondence between an article, a vector, and a score value is pre-established, and the vector group dividing unit includes:
the distance calculation unit is used for performing Euclidean distance calculation by using the scoring values of the article under any two vectors to obtain the distance between any two vectors;
and the clustering analysis unit is used for dividing each vector into different vector groups through clustering analysis by using the distance between any two vectors.
Optionally, the vector group dividing unit further includes: and the granularity determining unit is used for determining the division granularity of the vector in the dimension context information according to the article.
Optionally, the cluster analysis unit is specifically configured to obtain a vector group partitioning instruction, where the vector group partitioning instruction includes a specified vector group number; and adjusting the number of the vector groups divided by the clustering analysis according to the number of the specified vector groups, and dividing each vector into different vector groups.
Optionally, the apparatus further includes:
the searching unit is used for acquiring multi-dimensional context information corresponding to user data, searching an abstract vector matched with the multi-dimensional context information and determining an application scene corresponding to the multi-dimensional context information;
and the sending unit is used for sending the recommended content corresponding to the determined application scene to the user.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (8)
1. A method for processing context information, the method comprising:
the following processing is respectively carried out on the context information of each dimension: dividing each vector into different vector groups according to the closeness degree among the vectors included in the dimension context information;
taking the Cartesian product of vector groups divided by the context information of each dimension as an abstract vector to obtain the context information of the abstract dimension;
dividing the abstract vectors into different application scenes according to the tightness degree between the abstract vectors, and establishing a mapping relation between the abstract vectors and the application scenes;
obtaining multidimensional context information corresponding to user data, searching an abstract vector matched with the multidimensional context information, and determining an application scene corresponding to the multidimensional context information;
and sending the recommended content corresponding to the determined application scene to the user, wherein the corresponding relation between the application scene and the recommended content is pre-established.
2. The method according to claim 1, wherein, for each dimension context information, a corresponding relationship among an article, a vector and a score value is pre-established, and then, the dividing each vector into different vector groups according to a closeness degree among each vector included in the dimension context information comprises:
carrying out Euclidean distance calculation by using the scoring values of the article under any two vectors to obtain the distance between any two vectors;
and dividing each vector into different vector groups by clustering analysis by using the distance between any two vectors.
3. The method of claim 2, wherein the granularity of partitioning the vectors in the dimension context information is determined based on the item.
4. The method of claim 2, wherein said partitioning the individual vectors into different vector groups by cluster analysis comprises:
obtaining a vector group partition instruction, the vector group partition instruction comprising a specified number of vector groups;
and adjusting the number of the vector groups divided by the clustering analysis according to the number of the specified vector groups, and dividing each vector into different vector groups.
5. An apparatus for processing context information, the apparatus comprising:
the vector group dividing unit is used for respectively processing the context information of each dimension as follows: dividing each vector into different vector groups according to the closeness degree among the vectors included in the dimension context information;
an abstract dimension obtaining unit, configured to obtain context information of an abstract dimension by taking a cartesian product of a vector group divided by the context information of each dimension as an abstract vector;
the mapping relation establishing unit is used for dividing the abstract vectors into different application scenes according to the tightness degree between the abstract vectors and establishing the mapping relation between the abstract vectors and the application scenes;
the searching unit is used for acquiring multi-dimensional context information corresponding to user data, searching an abstract vector matched with the multi-dimensional context information and determining an application scene corresponding to the multi-dimensional context information;
and the sending unit is used for sending the recommended content corresponding to the determined application scene to the user, wherein the corresponding relation between the application scene and the recommended content is established in advance.
6. The apparatus according to claim 5, wherein, for each dimension context information, a corresponding relationship among an item, a vector and a score value is pre-established, and the vector group dividing unit comprises:
the distance calculation unit is used for performing Euclidean distance calculation by using the scoring values of the article under any two vectors to obtain the distance between any two vectors;
and the clustering analysis unit is used for dividing each vector into different vector groups through clustering analysis by using the distance between any two vectors.
7. The apparatus of claim 6, wherein the vector group partition unit further comprises:
and the granularity determining unit is used for determining the division granularity of the vector in the dimension context information according to the article.
8. The apparatus of claim 6,
the cluster analysis unit is specifically configured to obtain a vector group partitioning instruction, where the vector group partitioning instruction includes a specified vector group number; and adjusting the number of the vector groups divided by the clustering analysis according to the number of the specified vector groups, and dividing each vector into different vector groups.
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