CN108521522B - User personality identification method based on multidimensional perception data - Google Patents
User personality identification method based on multidimensional perception data Download PDFInfo
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- CN108521522B CN108521522B CN201810360217.1A CN201810360217A CN108521522B CN 108521522 B CN108521522 B CN 108521522B CN 201810360217 A CN201810360217 A CN 201810360217A CN 108521522 B CN108521522 B CN 108521522B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
- H04M1/72451—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to schedules, e.g. using calendar applications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/08—Testing, supervising or monitoring using real traffic
Abstract
The invention discloses a user personality identification method based on multidimensional sensing data, which relates to the technical field of user personality identification. Compared with the existing user personality identification method, the method utilizes simpler mobile phone functions, involves less user privacy data, and can utilize less data and obtain more accurate user personality characteristics by collecting experimental data of two weeks or more, so that application research related to the user personality characteristics can be better supported.
Description
Technical Field
The invention relates to the technical field of user personality identification, in particular to a user personality identification method based on multi-dimensional perception data.
Background
With the high popularization of smart devices, people's daily life, activities and activities are more and more closely related to smart phones. Users with different personality traits may have different dependencies on the handset and thus the habits and preferences in using the handset may vary. For example, an insider prefers to use the internet, a conscientious user may charge a mobile phone immediately when the power is exhausted, a person with a high openness score may charge the mobile phone for a long time or even keep the mobile phone at a full level, and the insider may spend more time contacting friends on social software. A user with a camber may prefer a travel application due to the characteristic of having an adventure spirit. Because users with different personality characteristics may have different habits of using mobile phones, personality identification is very important.
Patent CN103440864A introduces a personality characteristic prediction method based on voice, which first performs personality evaluation and determination on multiple participants, collects voice segments of the participants, extracts acoustic characteristics and statistical characteristics, and establishes a personality prediction model; and extracting corresponding features from the newly acquired voice information, and inputting the features into a prediction model to obtain the score values of the corresponding personality features.
Patent CN103902566A provides a personality prediction method based on microblog user behavior, which first obtains id lists of microblog active users and personality questionnaires of the users; then extracting corresponding static and dynamic behavior characteristics from the microblog data of the users according to the established microblog network behavior system; carrying out numerical processing on the dynamic behavior characteristics by utilizing time sequence analysis to form a complete microblog characteristic set; extracting a maximum significant feature set from the microblog feature set by using a stepwise regression algorithm to complete feature selection; and predicting the personality psychological indexes of the user by utilizing the established personality prediction regression model for the selected features.
Patent CN106649267A provides a method for conjecturing the five personality of a user by text topic mining, which first collects text data and five personality scores, and preprocesses them; and then obtaining a personality 62 theme distribution matrix according to the preprocessed text, and analyzing the relationship between the personality and the theme according to the distribution matrix so as to obtain the five-personality score associated with different themes. The research on the five-personality of the user provides a certain reference for the problem to be solved in the research thinking.
The two papers start from smart phones, collect short messages, telephones, APP usage data, mode changes and Bluetooth information, extract corresponding characteristics according to the situations of using the mobile phones by users, and further realize the identification of Personality characteristics of the users. The research of the users can collect the information of the length of the short message, the call duration and the like of the users, and the information can invade the privacy of the users to a certain extent. Meanwhile, with the rapid development of WeChat and microblog, people use less and less mobile phone functions such as traditional telephone and short message functions. The invention only utilizes the information of the change condition of the electric quantity of the mobile phone, the access condition of the earphone, the current contextual model of the mobile phone, the networking condition of the mobile phone, the screen state of the mobile phone, the APP name currently used by the mobile phone and the like to identify the personality characteristics of the user. In the identification process, the required time is shorter, the collected data is less, the privacy data is avoided, and a better effect is achieved on efficiency and accuracy.
Disclosure of Invention
The embodiment of the invention provides a user personality identification method based on multi-dimensional perception data, which is used for solving the problems in the prior art.
The personality research of users who start from smart phones generally collects the time and duration of incoming calls and outgoing calls of the users and the length of short messages sent by the users, and the data can invade the privacy of the users to a certain degree. In addition, due to the rapid development of WeChat and microblog in recent years, people use less and less mobile phone functions such as traditional telephone and short message functions. The method collects the data of the change condition of the electric quantity of the mobile phone, whether the earphone is accessed, the current contextual model of the mobile phone, the networking condition of the mobile phone, the information of a switch screen, the APP name currently used by the mobile phone and the like, respectively calculates the correlation between the use habit of the mobile phone and the five types of personality (openness, accountability, camber, humanity and neural quality), selects the characteristic capable of identifying the personality traits of the user, finally obtains the regression equation of the five types of personality and the use habit of the mobile phone, and can better support the related application research.
In order to achieve the purpose, the invention adopts the technical scheme that:
a user personality identification method based on multi-dimensional perception data comprises the following steps:
acquiring data of a change condition of a battery capacity of a mobile phone, an earphone access condition, a current contextual model of the mobile phone, a networking condition of the mobile phone, a screen state of the mobile phone and an application condition of the mobile phone APP by using an event monitor provided by Android, and selecting 1 minute, 5 minutes, 10 minutes, 30 minutes and 60 minutes as different time windows;
acquiring scene data of the electric quantity change condition of the mobile phone battery, and acquiring the battery charging state, the full electricity times, the most frequently used charging mode and the electric quantity consumption condition of each time window;
thirdly, acquiring scene data of the earphone access condition, and acquiring the times of connecting the earphone, the times of not connecting the earphone and the most common state of the earphone under each time window;
acquiring current contextual model scene data of the mobile phone, and acquiring the use times of each type of contextual model and the most frequently used contextual model;
acquiring scene data of the networking condition of the mobile phone, and acquiring the times of connecting 2G, 3G, 4G, Wi-Fi and non-networking and the most frequently used networking mode in different time windows;
collecting mobile phone screen state scene data, and collecting the screen lightening times and screen unlocking times in each time window;
step seven, collecting the scene data of the use condition of the APP of the mobile phone, and collecting the number and the name of each type of APP, wherein the use duration and the opening frequency of a certain type of APP are obtained in each time window;
and step eight, assigning all the characteristic values obtained in the step two, the step three, the step four, the step five, the step six and the step seven to corresponding personality attributes, taking the perception data of the user when using the mobile phone as input information, judging the personality attributes of the characteristics, and further determining the personality of the user, wherein the personality is divided into five types, namely openness, accountability, camber, humanity and nerve quality.
Preferably, the battery Charging state in the second step includes a Discharging-Charging, a Charging-Charging, an uncharged-Charging, a Full-charge-Full and an Unknown-Unknown state.
Preferably, the charging modes in the second step include a charger charging-AC, a USB interface charging-USB, and an uncharged-NULL state.
Preferably, the mobile phone profile in step four is divided into three types, namely Normal-Normal, mute-silence, and Vibrate-Vibrate.
The invention has the beneficial effects that: compared with the existing user personality identification method, the method utilizes simpler mobile phone functions, involves less user privacy data, and can utilize less data and obtain more accurate user personality characteristics by collecting experimental data of two weeks or more, so that application research related to the user personality characteristics can be better supported.
Drawings
Fig. 1 is a schematic flowchart of a method for recognizing a personality of a user based on multidimensional sensing data according to an embodiment of the present invention;
fig. 2 is a detailed schematic diagram of a usage scenario of a mobile phone APP.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, but it should be understood that the scope of the present invention is not limited by the specific embodiments.
The invention provides a user personality identification method based on multidimensional perception data, which comprises the following steps:
acquiring data of a change condition of a battery capacity of a mobile phone, an earphone access condition, a current contextual model of the mobile phone, a networking condition of the mobile phone, a screen state of the mobile phone and an application condition of the mobile phone APP by using an event monitor provided by Android, and selecting 1 minute, 5 minutes, 10 minutes, 30 minutes and 60 minutes as different time windows;
acquiring scene data of the electric quantity change condition of the mobile phone battery, and acquiring the battery charging state, the full electricity times, the most frequently used charging mode and the electric quantity consumption condition of each time window; battery state of charge includes discharge-Charging, charge-Charging, uncharged-Charging, fully charged-Full and Unknown-Unknown states; the charging modes comprise charger charging-AC, USB interface charging-USB and uncharged-NULL state;
thirdly, acquiring scene data of the earphone access condition, and acquiring the times of connecting the earphone, the times of not connecting the earphone and the most common state of the earphone under each time window;
acquiring current contextual model scene data of the mobile phone, and acquiring the use times of each type of contextual model and the most frequently used contextual model; the mobile phone scene modes are divided into Normal-Normal, mute-Silent and vibration-Vibrate;
acquiring scene data of the networking condition of the mobile phone, and acquiring the times of connecting 2G, 3G, 4G, Wi-Fi and non-networking and the most frequently used networking mode in different time windows;
collecting mobile phone screen state scene data, and collecting the screen lightening times and screen unlocking times in each time window;
step seven, collecting the scene data of the use condition of the mobile phone APP, and collecting the number of each type of APP, the use duration and the opening frequency of a certain type of APP under each time window;
and step eight, assigning all the characteristic values obtained in the step two, the step three, the step four, the step five, the step six and the step seven to corresponding personality attributes, taking the perception data of the user when using the mobile phone as input information, judging the personality attributes of the characteristics, and further determining the personality of the user, wherein the personality is divided into five types, namely openness, accountability, camber, humanity and nerve quality.
Referring to fig. 2, the invention provides a user personality identification method based on multidimensional sensing data, wherein the usage scenarios of mobile phone APP are as follows:
(1) classifying the APP into 27 classes according to Google play store and pea pod, and selecting 1/5/10/30/60 minutes as a time window;
(2) calculating the number of each type of APP, and the use duration and use frequency of certain type of APP under each time window;
(3) and determining the characteristic value of the mobile phone used by the user in the scene, if the characteristic value is not in the range of the characteristic value, continuously acquiring data, and if the characteristic value is in the range of the characteristic value, outputting a regression equation of the personality characteristics.
In summary, the invention collects data such as the change condition of the electric quantity of the mobile phone, the access condition of the earphone, the current contextual model of the mobile phone, the networking condition of the mobile phone, the screen state of the mobile phone, the use condition of the APP of the mobile phone and the like, extracts corresponding features from an experimental scene, and further identifies the personality features of the user, thereby determining the personality of the user. Compared with the existing user personality identification method, the method utilizes simpler mobile phone functions, involves less user privacy data, and can utilize less data and obtain more accurate user personality characteristics by collecting experimental data of two weeks or more, so that application research related to the user personality characteristics can be better supported.
The above disclosure is only one specific embodiment of the present invention, however, the present invention is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (4)
1. A user personality identification method based on multi-dimensional perception data is characterized by comprising the following steps:
acquiring data of a change condition of a battery capacity of a mobile phone, an earphone access condition, a current contextual model of the mobile phone, a networking condition of the mobile phone, a screen state of the mobile phone and an application condition of the mobile phone APP by using an event monitor provided by Android, and selecting 1 minute, 5 minutes, 10 minutes, 30 minutes and 60 minutes as different time windows;
acquiring scene data of the electric quantity change condition of the mobile phone battery, and acquiring the battery charging state, the full electricity times, the most frequently used charging mode and the electric quantity consumption condition of each time window;
thirdly, acquiring scene data of the earphone access condition, and acquiring the times of connecting the earphone, the times of not connecting the earphone and the most common state of the earphone under each time window;
acquiring current contextual model scene data of the mobile phone, and acquiring the use times of each type of contextual model and the most frequently used contextual model;
acquiring scene data of the networking condition of the mobile phone, and acquiring the times of connecting 2G, 3G, 4G, Wi-Fi and non-networking and the most frequently used networking mode in different time windows;
collecting mobile phone screen state scene data, and collecting the screen lightening times and screen unlocking times in each time window;
step seven, collecting the scene data of the use condition of the APP of the mobile phone, and collecting the number and the name of each type of APP, wherein the use duration and the opening frequency of a certain type of APP are obtained in each time window;
and step eight, assigning all the characteristic values obtained in the step two, the step three, the step four, the step five, the step six and the step seven to corresponding personality attributes, taking the perception data of the user when using the mobile phone as input information, judging the personality attributes of the characteristics, and further determining the personality of the user, wherein the personality is divided into five types, namely openness, accountability, camber, humanity and nerve quality.
2. The method of claim 1, wherein the battery Charging state in step two comprises a discharge-Charging, a charge-Charging, an uncharged-Charging, a Full-charge-Full, and an Unknown-Unknown state.
3. The method as claimed in claim 1, wherein the charging mode in step two includes charger charging-AC, USB interface charging-USB and non-charging-NULL state.
4. The method as claimed in claim 1, wherein the mobile phone profile in step four is divided into three types of Normal-Normal, mute-Silent and Vibrate-Vibrate.
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| US10268682B2 (en) * | 2014-04-02 | 2019-04-23 | International Business Machines Corporation | Adjusting text in message in light of recipients interests and/or personality traits to sustain recipient's interest in message |
| FR3021485A1 (en) * | 2014-05-26 | 2015-11-27 | Lg Electronics Inc | MOBILE DEVICE AND METHOD OF CONTROLLING THE SAME |
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