CN110704741B - Interest point prediction method based on space-time point process - Google Patents

Interest point prediction method based on space-time point process Download PDF

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CN110704741B
CN110704741B CN201910940088.8A CN201910940088A CN110704741B CN 110704741 B CN110704741 B CN 110704741B CN 201910940088 A CN201910940088 A CN 201910940088A CN 110704741 B CN110704741 B CN 110704741B
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CN110704741A (en
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王东京
张新
俞东进
张剑清
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Hangzhou Dianzi University
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Abstract

The invention discloses an interest point prediction method based on a space-time point process, which comprises the following steps: s1 modeling with user sign-in sequence based on spatio-temporal context information integration of point process; s2 prediction of user interest based on a spatiotemporal process; s3 prediction of spatio-temporal context and sequence awareness. The invention extracts the behavior pattern and the interest of the user from the check-in sequence of the user by utilizing the process of the time-space point, predicts the context interest of the user by combining the time-space context, and finally comprehensively considers the general interest and the context interest of the user, thereby improving the prediction effect and improving the accuracy.

Description

Interest point prediction method based on space-time point process
Technical Field
The invention belongs to the technical field of data mining and recommendation, and particularly relates to an interest point prediction method based on a space-time point process.
Background
With the development of information technology, users have information overload problems while enjoying convenient information and services, and it is difficult to find related or interested contents from massive online data. The recommendation system can actively mine the potential interests of the user according to the historical records of the user and help the user to find related contents from massive online data to meet the requirements of the user, the information acquisition cost is reduced, and the prediction of the behavior of the user is one of the keys for realizing the personalized recommendation system.
However, in the field of point of interest prediction, the conventional method generally cannot fully utilize the check-in sequence of the user and the temporal context and spatial context information, and it is difficult to further improve the accuracy and meet the real-time requirements of the user. Therefore, how to fully utilize rich context information sequence information, accurately extract the long-term interest and the context dynamic interest of the user from the information and perform modeling is one of the keys for meeting the real-time requirements of the user and improving the prediction recommendation effect.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides the interest point prediction method based on the space-time point process, which can improve the prediction and recommendation effects and performances.
The invention comprises the following steps:
(1) collecting check-in data for all users
Figure BDA0002222638880000011
The check-in data of each user is a check-in sequence of the user to a Point of Interest (POI)
Figure BDA0002222638880000012
Wherein p isi、tiAnd ciPOI, check-in time and context, respectively, ciIncluding temporal context vectors
Figure BDA0002222638880000013
And spatial context vector
Figure BDA0002222638880000014
The temporal context vector is a 6-dimensional access time period vector of the POI(s) ((s))<Morning, noon, afternoon, evening, workday, holiday>) The spatial context vector is a 2-dimensional geographic location vector for the corresponding POI(s) ((<Longitude and latitude>) The user set, POI set, and context set are denoted as U, P and C, respectively.
(2) According to user uiCheck-in sequence to POI
Figure BDA0002222638880000021
User uiHistory check-in sequence { (p)1,t1,c1),(p2,t2,c2),…,(pm-1,tm-1,cm-1) } and target POI sign-in record (p)m,tm,cm) The conditional density function of (a) is modeled as:
Figure BDA0002222638880000022
wherein:
Figure BDA0002222638880000023
is user uiIn the general interest of (a) in (b),
Figure BDA0002222638880000024
is an exponential function for representing the time decay,
Figure BDA0002222638880000025
is a function for representing spatial context similarity,
Figure BDA0002222638880000026
is a function for representing the similarity of temporal contexts, and f (x) 1(1+ exp (-x)) is a Logistic function for ensuring the similarity of temporal contexts
Figure BDA0002222638880000027
Is not negative.
The above exponential function
Figure BDA0002222638880000028
Is defined as:
Figure BDA0002222638880000029
wherein: alpha is alphauIs a parameter related to the user and is used for representing the historical sign-in behavior h to the target POI p for different usersmThe degree of influence of (c) is different.
The above spatial context distance function
Figure BDA00022226388800000210
Is defined as:
Figure BDA00022226388800000211
wherein: beta is auIs a user-related parameter, the way in which the computation representing the degree of similarity between spatial contexts is personalized,
Figure BDA00022226388800000212
representing historical check-in POI phLocation context vector of
Figure BDA0002222638880000031
And target POIpmLocation context vector of
Figure BDA0002222638880000032
The euclidean distance between.
The above time context similarity function
Figure BDA0002222638880000033
Is defined as:
Figure BDA0002222638880000034
wherein: gamma rayuIs a user-related parameter that indicates that, for different users, the degree of influence of the temporal context is different,
Figure BDA0002222638880000035
representing historical check-in POI phTemporal context vector of
Figure BDA0002222638880000036
With a target POI pmTemporal context vector of
Figure BDA0002222638880000037
The euclidean distance between.
(3) Given POI check-in sequence data for all users
Figure BDA0002222638880000038
Logarithmic form of the objective functionCan be defined as:
Figure BDA0002222638880000039
wherein:
Figure BDA00022226388800000310
is given user uiPOI check-in interaction sequence before time t
Figure BDA00022226388800000311
User uiFor POI pjThe probability of interest, defined as:
Figure BDA00022226388800000312
(4) and (4) carrying out maximization solution on the objective function O to obtain all parameters.
(5) And calculating the interest value of the user to each POI in the P according to the historical check-in record of the user. Given user uiHistorical interaction records and spatiotemporal context information csAnd ctUser uiFor POI pjThe interest of (2) is defined as:
Figure BDA00022226388800000313
wherein: (x) log (1+ exp (x)) is a Logistic function for guaranteeing probability values
Figure BDA00022226388800000314
Is not negative in the sense of (1),
Figure BDA00022226388800000315
is user uiIn the general interest of (a) in (b),
Figure BDA0002222638880000041
representing the contextual interest of the user, t, csAnd ctCurrent temporal, temporal context and spatial context, respectively.
(6) And sequencing all POIs in the database from top to bottom according to the interest values of the user, and extracting a plurality of POIs with the highest interest values to recommend to the user. The ordering formula is as follows:
Figure BDA0002222638880000042
wherein: u represents a target user; p is a radical ofiE.g. P and Pi′E P is the POI in the database.
The invention integrates time and space context information by combining a point process model for the first time, and provides a reliable method for solving the behavior modeling and prediction of context sensing; the general interest and the contextual interest of the user are modeled and predicted according to the spatio-temporal information in the check-in sequence of the user, and an accurate method is provided for extracting the interest preference of the user and difficulty in modeling; the invention can improve the prediction and recommendation effects by integrating the spatio-temporal context and the sequence information by using the point process model.
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FIG. 1 is a system architecture diagram of the present invention.
FIG. 2 is a schematic diagram of a user preference prediction process according to the present invention.
Detailed Description
In order to more specifically describe the present invention, the following detailed description is provided for the technical solution of the present invention with reference to the accompanying drawings and the specific embodiments.
The invention relates to an interest point prediction algorithm based on a space-time point process, which comprises the following steps:
(1) collecting check-in data for all users
Figure BDA0002222638880000043
The check-in data of each user is a check-in sequence of the user to a Point of Interest (POI)
Figure BDA0002222638880000044
Wherein p isi、tiAnd ciPOI, check-in time and context, respectively, ciIncluding temporal context vectors
Figure BDA0002222638880000045
And spatial context vector
Figure BDA0002222638880000046
The temporal context vector is a 6-dimensional access time period vector of the POI(s) ((s))<Morning, noon, afternoon, evening, workday, holiday>) The spatial context vector is a 2-dimensional geographic location vector for the corresponding POI(s) ((<Longitude and latitude>) The user set, POI set, and context set are denoted as U, P and C, respectively.
(2) According to user uiCheck-in sequence to POI
Figure BDA0002222638880000051
User uiHistory check-in sequence { (p)1,t1,c1),(p2,t2,c2),…,(pm-1,tm-1,cm-1) } and target POI sign-in record (p)m,tm,cm) The conditional density function of (a) is modeled as:
Figure BDA0002222638880000052
wherein:
Figure BDA0002222638880000053
is user uiIn the general interest of (a) in (b),
Figure BDA0002222638880000054
is an exponential function for representing the time decay,
Figure BDA0002222638880000055
is a similarity function for representing the spatial context,
Figure BDA0002222638880000056
is a function for representing the similarity of temporal contexts, and f (x) 1/(1+ exp (-x)) is a Logistic function for ensuring the similarity of temporal contexts
Figure BDA0002222638880000057
Is not negative.
The above exponential function
Figure BDA0002222638880000058
Is defined as:
Figure BDA0002222638880000059
wherein: alpha is alphauIs a parameter related to the user and is used for representing the historical sign-in behavior h to the target POI p for different usersmThe degree of influence of (c) is different.
The above spatial context distance function
Figure BDA00022226388800000510
Is defined as:
Figure BDA00022226388800000511
wherein: beta is auIs a user-related parameter, the way in which the computation representing the degree of similarity between spatial contexts is personalized,
Figure BDA00022226388800000512
representing historical check-in POI phLocation context vector of
Figure BDA00022226388800000513
And target POIpmLocation context vector of
Figure BDA00022226388800000514
The euclidean distance between.
Above and below the timeText similarity function
Figure BDA0002222638880000061
Is defined as:
Figure BDA0002222638880000062
wherein: gamma rayuIs a user-related parameter that indicates that, for different users, the degree of influence of the temporal context is different,
Figure BDA0002222638880000063
representing historical check-in POI phTemporal context vector of
Figure BDA0002222638880000064
With a target POI pmTemporal context vector of
Figure BDA0002222638880000065
The euclidean distance between.
(3) Given POI check-in sequence data for all users
Figure BDA0002222638880000066
The objective function in logarithmic form can be defined as:
Figure BDA0002222638880000067
wherein:
Figure BDA0002222638880000068
is given user uiPOI check-in interaction sequence before time t
Figure BDA0002222638880000069
User uiFor POI pjThe probability of interest, defined as:
Figure BDA00022226388800000610
(4) and (4) carrying out maximization solution on the objective function O to obtain all parameters.
(5) And calculating the interest value of the user to each POI in the P according to the historical check-in record of the user. Given user uiHistorical interaction records and spatiotemporal context information csAnd ctUser uiFor POI pjThe interest of (2) is defined as:
Figure BDA00022226388800000611
wherein: (x) log (1+ exp (x)) is a Logistic function for guaranteeing probability values
Figure BDA00022226388800000612
Is not negative in the sense of (1),
Figure BDA00022226388800000613
is user uiIn the general interest of (a) in (b),
Figure BDA00022226388800000614
representing the contextual interest of the user, t, csAnd ctCurrent temporal, temporal context and spatial context, respectively.
(6) And sequencing all POIs in the database from top to bottom according to the interest values of the user, and extracting a plurality of POIs with the highest interest values to recommend to the user. The ordering formula is as follows:
Figure BDA0002222638880000071
wherein: u represents a target user; p is a radical ofiE.g. P and Pi′E P is the POI in the database.
Fig. 1 shows an architecture of a point of interest prediction method based on a space-time point process according to the present embodiment. The method is divided into two main modules: a preprocessing module and a prediction module. In the preprocessing module, firstly, check-in recording sequences and space-time context information of all users are obtained; and integrating the spatiotemporal context information by using a point process model and modeling the sign-in sequence of the user to obtain an interest model based on the spatiotemporal process. In a prediction module, firstly, acquiring a check-in sequence and context information from POI check-in data of a target user; and then, the interest model based on the space-time point process is used for deducing the interest of the user and predicting the subsequent check-in behavior of the user. FIG. 2 shows the detailed steps of user preference prediction, which first obtains the historical check-in data and context information of the user, and calculates the preference of the target user u for POI in combination with the interest model based on the spatio-temporal point process.
The embodiments described above are intended to facilitate one of ordinary skill in the art in understanding and using the invention. It will be readily apparent to those skilled in the art that various modifications to the above-described implementations may be made, and the generic principles described herein may be applied to other implementations without the use of inventive faculty. Therefore, the present invention is not limited to the above embodiments, and those skilled in the art should make improvements and modifications to the present invention based on the disclosure of the present invention within the protection scope of the present invention.

Claims (7)

1. The interest point prediction method based on the space-time point process is characterized by comprising the following steps:
step (1) collecting check-in data of all users
Figure FDA0003135105570000011
Check-in data of each user is check-in sequence of the user to POI (point of interest)
Figure FDA0003135105570000012
Wherein p isi、tiAnd ciPOI, time of sign-in and context, respectively, ciIncluding temporal context vectors
Figure FDA0003135105570000013
And spatial context vector
Figure FDA0003135105570000014
The user set, POI set, and context set are denoted U, P and C, respectively;
step (2) according to the user uiCheck-in sequence for point of interest POI
Figure FDA0003135105570000015
User uiHistory check-in sequence { (p)1,t1,c1),(p2,t2,c2),…,(pm-1,tm-1,cm-1) } and target Point of interest POI sign-in record (p)m,tm,cm) The conditional density function of (a) is modeled as:
Figure FDA0003135105570000016
wherein:
Figure FDA0003135105570000017
is user uiIn the general interest of (a) in (b),
Figure FDA0003135105570000018
is an exponential function for representing the time decay,
Figure FDA0003135105570000019
is a similarity function for representing the spatial context,
Figure FDA00031351055700000110
is a function for representing the similarity of temporal contexts, and f (x) 1/(1+ exp (-x)) is a Logistic function for ensuring the similarity of temporal contexts
Figure FDA00031351055700000111
Is non-negative;
step (3) giving POI (Point of interest) check-in data of all users
Figure FDA00031351055700000112
The objective function in logarithmic form is defined as:
Figure FDA0003135105570000021
wherein:
Figure FDA0003135105570000022
is given user uiPoint of interest POI check-in interaction sequence before time t
Figure FDA0003135105570000023
User uiFor point of interest POI pjA probability of interest;
step (4), carrying out maximum solution on the objective function O to obtain all parameters;
step (5), calculating the interest value of the user for each POI in the P according to the historical sign-in record of the user;
and (6) sequencing all the POIs in the database from top to bottom according to the interest values of the user, and extracting a plurality of POIs with the highest predicted interest values to recommend to the user.
2. The method of predicting points of interest based on space-time point process of claim 1, wherein: the exponential function of step (2)
Figure FDA0003135105570000024
Is defined as:
Figure FDA0003135105570000025
wherein: alpha is alphauIs a parameter related to the user and is used to indicate the relation to notWith the same user, the historical sign-in behavior h is towards the target point of interest POI pmThe degree of influence of (c) is different.
3. The method of predicting points of interest based on space-time point process of claim 1, wherein: the spatial context similarity function in the step (2)
Figure FDA0003135105570000026
Is defined as:
Figure FDA0003135105570000027
wherein: beta is auIs a user-related parameter, the way in which the computation representing the degree of similarity between spatial contexts is personalized,
Figure FDA0003135105570000028
representing historical check-in points of interest POI phLocation context vector of
Figure FDA0003135105570000029
And a target point of interest (POIp)mLocation context vector of
Figure FDA00031351055700000210
The euclidean distance between.
4. The method of predicting points of interest based on space-time point process of claim 1, wherein: the time context similarity function of step (2)
Figure FDA00031351055700000211
Is defined as:
Figure FDA0003135105570000031
wherein: gamma rayuIs a user-related parameter that indicates that, for different users, the degree of influence of the temporal context is different,
Figure FDA0003135105570000032
representing historical check-in points of interest POI phTemporal context vector of
Figure FDA0003135105570000033
With a target point of interest POI pmTemporal context vector of
Figure FDA0003135105570000034
The euclidean distance between.
5. The method of predicting points of interest based on space-time point process of claim 1, wherein: step (3) the given user uiPoint of interest POI check-in interaction sequence before time t
Figure FDA0003135105570000035
User uiFor point of interest POI pjProbability of interest
Figure FDA0003135105570000036
Is defined as:
Figure FDA0003135105570000037
6. the method of predicting points of interest based on space-time point process of claim 1, wherein: giving user u in step (5)iHistorical interaction records and spatiotemporal context information csAnd ctUser uiFor point of interest POI pjThe interest of (2) is defined as:
Figure FDA0003135105570000038
wherein: (x) log (1+ exp (x)) is a Logistic function for guaranteeing probability values
Figure FDA0003135105570000039
Is not negative in the sense of (1),
Figure FDA00031351055700000310
is user uiIn the general interest of (a) in (b),
Figure FDA00031351055700000311
representing the contextual interest of the user, t, csAnd ctCurrent temporal, temporal context and spatial context, respectively.
7. The method of predicting points of interest based on space-time point process of claim 1, wherein: the sequence in the step (6) is calculated by adopting the following formula:
Figure FDA0003135105570000041
wherein: u. ofiRepresenting a target user; p is a radical ofiE.g. P and Pi′E P is the point of interest POI in the database.
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Granted publication date: 20211015

License type: Common License

Record date: 20221124

Application publication date: 20200117

Assignee: ZHEJIANG ANDA SYSTEM ENGINEERING Co.,Ltd.

Assignor: HANGZHOU DIANZI University

Contract record no.: X2022980022900

Denomination of invention: Prediction method of interest points based on spatio-temporal point process

Granted publication date: 20211015

License type: Common License

Record date: 20221124