WO2013182736A1 - Détermination de préférences utilisateur sensibles au contexte - Google Patents

Détermination de préférences utilisateur sensibles au contexte Download PDF

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Publication number
WO2013182736A1
WO2013182736A1 PCT/FI2013/050458 FI2013050458W WO2013182736A1 WO 2013182736 A1 WO2013182736 A1 WO 2013182736A1 FI 2013050458 W FI2013050458 W FI 2013050458W WO 2013182736 A1 WO2013182736 A1 WO 2013182736A1
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WO
WIPO (PCT)
Prior art keywords
context
sub
items
space
mobile device
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Application number
PCT/FI2013/050458
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English (en)
Inventor
Huanhuan Cao
Jilei Tian
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Nokia Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by Nokia Corporation filed Critical Nokia Corporation
Publication of WO2013182736A1 publication Critical patent/WO2013182736A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/50—Service provisioning or reconfiguring
    • 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
    • H04M1/72454—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to context-related or environment-related conditions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information

Definitions

  • a user of a mobile device may usually install many applications in his/her mobile device. If a user interface of a system can list the applications to be most likely used in a given context in the most eye-catching positions on a home screen, the user can avoid the cumbersome process for searching a desired application. Such a function is especially desirable for a mobile phone such as WindowsTM Phone platform because its applications are organized in a list. Thus, the applications frequently used in a particular context will show in the eye-catching positions on the home screen in the form of a list so that the user may make an intuitive selection.
  • embodiments of the present invention provide a method, apparatus and computer program product for determining context-aware user preferences.
  • a method comprising recording a context in which one or more items of a mobile device are used.
  • the method further comprises determining one or more sub-context spaces based on a distribution of the context.
  • the method further comprises forming a corresponding preference pattern for each sub-context space so as to display, when detecting that the mobile device is in one sub-context space, a respective item in accordance with the preference pattern corresponding to the one sub-context space.
  • merging the sub-context spaces having similar context distributions for different items further comprises: comparing context distributions for two items to determine a similarity metric between the context distributions; and merging the sub-context spaces for the two items when the similarity metric is greater than a first threshold.
  • a greedy algorithm is used to partition the overlapped contexts into one of the plurality of preference patterns.
  • forming the corresponding preference pattern for each sub-context space comprises: for each sub-context space, based on at least one property of respective historical use conditions of at least a portion of items in the one or more items, ranking the at least a portion of items to form the corresponding preference pattern.
  • a method comprising detecting one or more items being used in a mobile device and a corresponding context.
  • the method further comprises determining that the context is in a predetermined sub-context space.
  • the method further comprises displaying a respective item in accordance with a preference pattern corresponding to the predetermined sub-context space.
  • the predetermined sub-context space and the corresponding preference pattern are stored in the mobile device or in a remote server in the form of a context tree.
  • displaying the respective item in accordance with the preference rattern corresrjondine to the Dredetermined sub-context srace comrj rises: marjrjine the context to a sub-context space in the context tree; and determining a preference pattern related to the context by using the preference pattern corresponding to the sub-context space so as to display the respective item.
  • the context tree is updated dynamically or periodically based on a situation that the one or more items are used.
  • the items comprise contact persons in a phonebook or the used applications of the mobile device.
  • detecting the one or more items being used in the mobile device and the corresponding context comprises: detecting the one or more items being used in the mobile device and corresponding time and locations.
  • an apparatus comprising at least one processor and at least one memory containing computer program code.
  • the processor and the memory are configured to, with the processor, cause the apparatus to at least perform: recording a context in which one or more items of a mobile device are used; and determining one or more sub-context spaces based on a distribution of the context.
  • the processor and the memory are further configured to, with the processor, cause the apparatus to at least perform forming a corresponding preference pattern for each sub-context space so as to display, when detecting that the mobile device is in one sub-context space, a respective item in accordance with the preference pattern corresponding to the one sub-context space.
  • an apparatus comprising at least one processor and at least one memory containing computer program code.
  • the processor and the memory are configured to, with the processor, cause the apparatus to at least perform: detecting one or more items being used in a mobile device and a corresponding context; and determining that the context is in a predetermined sub-context space.
  • the processor and the memory are further configured to, with the processor, cause the apparatus to at least perform displaying a respective item in accordance with a preference pattern corresponding to the predetermined sub-context space.
  • an apparatus there is provided an apparatus.
  • the apparatus comprises detecting means configured to detect one or more items being used in a mobile device and a corresponding context; and second determining means configured to determine that the context is in a predetermined sub-context space.
  • the apparatus further comprises displaying means configured to display a respective item in accordance with a preference pattern corresponding to the predetermined sub-context space.
  • a computer program product comprising at least one computer-readable storage medium having a computer-readable program code portion stored therein, the computer-readable program code portion being configured to perform the method for determining context-aware user preferences according to one embodiment of the present invention.
  • a computer program product comprising at least one computer-readable storage medium having a computer-readable program code portion stored therein, the computer-readable program code portion being configured to perform the method for using context-aware user preferences according to another embodiment of the present invention.
  • a context tree is constructed by detecting preference patterns related to contexts and user preferences related to a given context are determined based on the context tree.
  • user preferences related to a context items frequently used by a user in this context are ranked so that the user can acquire the frequently used items easily and quickly.
  • FIG. 1 schematically illustrates a flowchart of a method 100 for determining context-aware user preferences according to one embodiment of the present invention
  • FIG. 2 schematically illustrates an example of context distribution for one contact in a two-dimensional context space according to one embodiment of the present invention
  • FIG. 3 schematically illustrates a flowchart of a method 300 for determining context-aware user preferences according to another embodiment of the present invention
  • FIG. 7 schematically illustrates a block diaeram of an arjrjaratus 700 for determining context-aware user preferences according to one embodiment of the present invention
  • FIG. 8 schematically illustrates a block diagram of an apparatus 800 for using context-aware user preferences according to one embodiment of the present invention
  • the user preferences are determined by modeling the context-aware user preferences to thereby display items frequently used by a user in this context.
  • the mobile device may comprise various types of mobile terminals, such as a mobile phone, a personal digital assistant (PDA), a tablet computer and a portable computer.
  • items may be contents displayed or recommended to a user via a mobile device.
  • the items may be contact persons in a phonebook of the mobile device or various specific applications used by the user of the mobile device. These applications, for example, comprise call, short-message service (SMS), multimedia messaging service (MMS) or browser applications (e.g. IE).
  • the items may further comprise corresponding links in the favorites of the browser.
  • a user may look for various interested items through a mobile device.
  • a context may be comprised of multiple factors.
  • the context comprises activities of the user of the mobile device at different time and in different locations. For example, in this context, the user may need to look for relevant contact persons (i.e. items) in a phonebook.
  • the context comprises the time, the location, the taken vehicle or the specific working state at the position of the user.
  • the context comprises the duration of each call of the user.
  • the context comprises social activities of the user, e.g. attending various meetings.
  • the context may further comprise the geographic position (obtained, for example, by Beidou satellite navigation, Galileo navigation or GPS measurement) where the mobile device is currently located, or historical sate information about the mobile device being connected to a network and record information on the access history.
  • the items and contexts in embodiments of the present invention are not limited to the above contents, and various types of data in which a user may use a mobile device to query or look for interested applications or which may be collected for recommending items to the user can be regarded as the contexts in the embodiments of the present invention.
  • a context space is constructed by the above contexts, and the context space may comprise one or more sub-context spaces.
  • the sub-context spaces may be two-dimensional and may also be multi-dimensional.
  • a user may perform operations on items for many times. For example, in a two-dimensional context space constructed by time and geographic areas, when a user relatively frequently contacts relevant contact persons during a particular time period and in a corresponding geographic area, the two-dimensional space constructed by this time period and the corresponding geographic area forms a sub-context space.
  • one way to set the granularity of context can be ⁇ Day period, District>.
  • a context-aware phonebook based on this model will only give a ranking list for a same contact person if the user stays in "Queen District" (i.e. "District") for the whole morning (i.e. "Day period”).
  • the granularity of context for such a context model is too big since the contact persons whom the user frequently contacts from AM9:00 to AM10:30 may be different from those from AM10:30 to PM12:00.
  • the context model will be very complex since it will contain 3600 * N context patterns to deal with, where N indicates a number of locations where the user used to visit and the number is usually bigger than several thousands. If the above model considers more context information, such as battery level, the activated profile (general, silent, pager, etc), inactive time, weather, user's activities recognized by 3D accelerometers, etc, the situation to deal with will become much more worse since it is hard for a mobile device with limited storage capacity and computing power to store tens of millions of context-aware preferences.
  • an appropriate granularity of context needs to be selected to model user preferences to thereby accurately reflect the preferences of a user in a particular context.
  • FIG. 1 schematically illustrates a flowchart of a method 100 for determining context-aware user preferences according to one embodiment of the present invention. It should be understood that steps of the method 100 as illustrated in FIG. 1 are only used for illustration purpose and the method may comprise additional and/or alternative steps.
  • step S101 starts at step S101 and then proceeds to step SI 02.
  • step SI 02 there is recorded a context in which one or more items of a mobile device are used.
  • the method 100 may record items frequently used by a user and corresponding context distributions based on the historical device log of the mobile device.
  • the method 100 may record contact persons whom the user frequently contacts and the corresponding context distributions based on groups of the contact persons in a phonebook.
  • the method 100 may record applications frequently used by the user and corresponding context distributions based on the popularity of the applications, e.g. the number of web pages searched by a search engine.
  • step SI 03 one or more sub-context spaces are determined based on a distribution of the context.
  • Table 1 schematically illustrates context information of a device log:
  • step SI 04 a corresponding preference pattern is formed for each sub-context space so as to display, when detecting that the mobile device is in one sub-context space, a respective item in accordance with the preference pattern corresponding to the one sub-context space.
  • step SI 05 the method 100 ends at step SI 05.
  • the context cluster indicates a group of contexts in which the user has an obvious rjreference. which should be considered when determining the context-aware preference patterns for the user.
  • corresponding sub-context spaces are determined by using the call records frequently used and the corresponding context distributions to thereby form context-aware preference patterns.
  • the illustrated context distribution has two clusters of contexts A and B, which respectively indicate that calls with the contact person generally concentrate in a specific time period and in a specific geographic area as indicated by A and B. That is, two sub-context spaces may be determined based on context clusters A and B, and the two sub-context spaces reflect a preference pattern for calls made between the user and the contact person.
  • FIG. 3 schematically illustrates a flowchart of a method 300 for determining context-aware user preferences according to another embodiment of the present invention. It should be understood that steps of the method 300 as illustrated in FIG. 3 are only used for illustration purpose and the method 300 may comprise additional and/or alternative steps.
  • step S302 there is recorded a context in which one or more items of a mobile device are used.
  • step S302 is the same as that of step SI 02 in method 100 as described with reference to FIG. 1, and thus the detailed description of step SI 02 are also applicable to step S302.
  • step S303 one or more sub-context spaces are determined based on a distribution of the context.
  • a sub-context space corresponding to the item can be determined.
  • the specific implementation to determine whether the context distribution of a certain item form some context clusters is to perform a density based clustering algorithm (e.g., DBSCAN) on them.
  • DBSCAN is a typical density based clustering algorithm, and it defines the cluster as a maximum collection of density connected points, can partition an area with an enough high density into clusters, and can find clustering of arbitrary shapes in a space database. Clusters generated according to the clustering algorithm can reach a certain density requirement.
  • step S304 the method 300 merges sub-context spaces having similar context distributions for different items.
  • sub-context spaces having similar context distributions for different items are merged to make the context space more compact, thereby reducing the complexity of the preference patterns.
  • each context-aware preference pattern will correspond to one sub-context space.
  • some similarity metric criterions may be firstly defined, and then context distributions for two items are compared to obtain the similarity metric between the context distributions.
  • the similarity metric is greater than a predetermined first threshold, the sub-context spaces for the two items are merged into one sub-context space.
  • step S305 the method 300 forms a corresponding preference pattern for each sub-context space so as to display, when detecting that the mobile device is in one sub-context space, a respective item in accordance with the preference pattern corresponding to the one sub-context space.
  • the at least a portion of items are ranked to form corresponding preference patterns.
  • the at least one property comprises at least one of a historical use number, a historical use frequency, a historical use location, historical use time and historical use duration.
  • step S306 the method 300 partitions overlapped contexts in a plurality of preference patterns into one of the plurality of preference patterns.
  • some contexts appear in more than one sub-context space so that these contexts belong to a plurality of preference patterns and are regarded as overlapped contexts.
  • sub-context space 1 the time period is 5:00 pm to 7:00 pm and the geographic area is "office.”
  • sub-context space 2 the time period is 7:00 pm to 9:00 pm and the geographic area is "home.”
  • this call will be partitioned into the sub-context space 2.
  • this call being partitioned to the sub-context space 1 can more accurately reflect the preference of the user.
  • a greedy algorithm can be used to partition the overlapped context into an appropriate preference pattern. Specifically, for each overlapped context, it is checked which context cluster's preference pattern will be impacted least when absorbing the context, and then assigning the overlapped context to that context cluster. The greedy algorithm is repeatedly used until all overlapped contexts are assigned to corresponding context clusters.
  • a certain context belongs to two sub-context spaces
  • it is firstly removed from the two sub-context spaces, and the two sub-context spaces with the context being removed respectively form two different preference patterns C and D.
  • the context is respectively added to the two sub-context spaces, and the two sub-context spaces containing the context respectively form two different preference patterns C and D'.
  • a change between the ranking of items in the sub-context spaces C and C, and the change between the ranking of items in the sub-context spaces D and D' are compared.
  • the sub-context spaces and corresponding preference patterns are stored in the mobile device or in a remote server in the form of a context tree.
  • the context tree is updated dynamically or periodically.
  • the modeling process for a context-aware preference model is executed periodically to achieve incremental training for the context distribution to thereby update a context-aware preference pattern.
  • FIG. 4 schematically illustrates a flowchart of a method 400 for using context-aware user preferences according to one embodiment of the present invention. It should be understood that steps of the method 400 as illustrated in FIG. 4 are only used for illustration purpose and the method 400 may comprise additional and/or alternative steps.
  • the method 400 starts at sterj S401 and then rjroceeds to sterj S402.
  • items beine used in a mobile device and a corresponding context are detected.
  • one or more sub-context spaces are determined based on a distribution of the context so that each context is associated with one or more sub-context spaces.
  • the method 400 proceeds to step S404.
  • a respective item is displayed in accordance with a preference pattern corresponding to the predetermined sub-context space.
  • a preference pattern corresponding to the predetermined sub-context space.
  • Items corresponding to a given context are comprised in these preference patterns, e.g. contact persons in a call at specific time and in a specific location.
  • the predetermined sub-context space and the corresponding preference pattern are stored in the mobile device or in a remote server in the form of a context tree.
  • the context tree is updated dynamically or periodically based on a situation that the one or more items are used.
  • the method 400 indexes the sub-context spaces through a context tree so that a user can quickly find a sub-context space matching a given particular context to thereby display a respective item.
  • FIG. 5 schematically illustrates an example of preference patterns for different items having similar context distributions according to one embodiment of the present invention.
  • the top 5A in FIG. 5 illustrates a sub-context space for one contact person
  • the bottom 5B illustrates a sub-context space for another contact person.
  • the two contact persons have similar attributes for a user (for example, both belong to relatives, friends or colleagues of the user), both of them have similar sub-context spaces, and the two sub-context spaces can be merged based on the step S304 as illustrated in the method 300.
  • FIG. 6 schematically illustrates an example of a context tree according to one embodiment of the present invention. As illustrated in FIG.
  • Context 1 ⁇ (Monday), (6:00 am - 6:01 am), (Location 1) ⁇ , Context 2 ⁇ (Tuesday), (6:00 am - 6:01 am), (Location 2) ⁇ and Context 3 ⁇ (Tuesday), (6:02 am - 6:03 am), (Location 1) ⁇ can be mapped to a same sub-context space, while the sub-context space is associated with one preference pattern.
  • Each element in the table as illustrated at the bottom of FIG. 6 indicates a preference pattern for one sub-context space.
  • the context tree is a context space with a three-dimensional structure, the three dimensions comprising week, time and location.
  • the context tree may further comprise more dimensions, e.g. vehicle, call duration, working state or social activity dimensions.
  • FIG. 7 schematically illustrates a block diagram of an apparatus 700 for determining context-aware user preferences according to one embodiment of the present invention.
  • the apparatus 700 comprises recording means 701 configured to record a context in which one or more items of a mobile device are used; first determining means 702 configured to determine one or more sub-context spaces based on a distribution of the context; and forming means 703 configured to form a corresponding preference pattern for each sub-context space so as to display, when detecting that the mobile device is in one sub-context space, a respective item in accordance with the preference pattern corresponding to the one sub-context space.
  • the first determining means 702 is further configured to identify one or more context clusters in the distribution of the context, and to partition at least one of the context clusters into one sub-context space.
  • the first determining means 702 is further configured to merge the sub-context spaces having similar context distributions for different items.
  • the first determining means 702 is further configured to compare context distributions for two items to determine a similarity metric between the context distributions, and to merge the sub-context spaces for the two items when the similarity metric is greater than a first threshold.
  • the forming means 703 is further configured to rartition overlarjrjed contexts in a rjluralitv of rjreference ratterns into one of the plurality of preference patterns.
  • a greedy algorithm is used to partition the overlapped contexts into one of the plurality of preference patterns.
  • the forming means 703 is further configured to check changes in ranking among items in the plurality of preference patterns when absorbing the overlapped contexts, and to partition the overlapped contexts into a preference pattern in which the change in ranking among the items is small.
  • the sub-context spaces and corresponding preference patterns are stored in the mobile device or in a remote server in the form of a context tree.
  • the context tree is updated dynamically or periodically.
  • the forming means 703 is configured to, for each sub-context space, based on at least one property of respective historical use conditions of at least a portion of items in the one or more items, rank the at least a portion of items to form the corresponding preference pattern.
  • the at least one property comprises at least one of a historical use number, a historical use frequency, a historical use location, historical use time and historical use duration.
  • FIG. 8 schematically illustrates a block diagram of an apparatus 800 for using context-aware user preferences according to one embodiment of the present invention.
  • the apparatus 800 comprises detecting means 801 configured to detect one or more items being used in a mobile device and a corresponding context; second determining means 802 configured to determine that the context is in a predetermined sub-context space; and displaying means 803 configured to display a respective item in accordance with a preference pattern corresponding to the predetermined sub-context space.
  • the predetermined sub-context space and the corresponding preference pattern are stored in the mobile device or in a remote server in the form of a context tree.
  • the displaying means 803 is configured to map the context to a sub-context space in the context tree, and to determine a preference pattern related to the context by using a preference pattern corresponding to the sub-context space so as to display a respective item.
  • the context tree is undated dvnamicallv or periodically based on a situation that the one or more items are used.
  • FIG. 9 schematically illustrates a block diagram of an apparatus 900 for determining context-aware user preferences according to another embodiment of the present invention.
  • the apparatus 900 comprises a data processor (DP) 901 and memory (MEM) 903 coupled to the data processor 901.
  • the memory 903 stores a program (PROG) 902.
  • PROG program
  • Embodiments of the present invention can be implemented by software executed by the data processor 901, or implemented by hardware, or implemented by the combination of software and hardware.
  • the data processor 901 and the memory 903 are configured to, with the data processor 901, cause the apparatus 900 to at least perform: recording a context in which one or more items of a mobile device are used; determining one or more sub-context spaces based on a distribution of the context; and forming a corresponding preference pattern for each sub-context space so as to display, when detecting that the mobile device is in one sub-context space, a respective item in accordance with the preference pattern corresponding to the one sub-context space.
  • the data processor 901 and the memory 903 are configured to, with the data processor 901, cause the apparatus 900 to at least perform: identifying one or more context clusters in the distribution of the context; and partitioning at least one of the context clusters into one sub-context space.
  • the data processor 901 and the memory 903 are configured to, with the data processor 901, cause the apparatus 900 to at least perform merging the sub-context spaces having similar context distributions for different items.
  • the data processor 901 and the memory 903 are configured to, with the data processor 901, cause the apparatus 900 to at least perform: comparing context distributions for two items to determine a similarity metric between the context distributions; and merging the sub-context spaces for the two items when the similarity metric is greater than a first threshold.
  • the data processor 901 and the memory 903 are configured to, with the data processor 901, cause the apparatus 900 to at least perform partitioning overlapped contexts in a plurality of preference patterns into one of the plurality of preference patterns.
  • a greedy algorithm is used to partition the overlapped contexts into one of the plurality of preference patterns.
  • the data processor 901 and the memory 903 are configured to, with the data processor 901, cause the apparatus 900 to at least perform: checking changes in ranking among items in the plurality of preference patterns when absorbing the overlapped contexts; and partitioning the overlapped contexts into a preference pattern in which the change in ranking among the items is small.
  • the sub-context spaces and corresponding preference patterns are stored in the mobile device or in a remote server in the form of a context tree.
  • the context tree is updated dynamically or periodically.
  • the data processor 901 and the memory 903 are configured to, with the data processor 901, cause the apparatus 900 to at least perform: for each sub-context space, based on at least one property of respective historical use conditions of at least a portion of items in the one or more items, ranking the at least a portion of items to form the corresponding preference pattern.
  • the data processor 1001 and the memory 1003 are configured to, with the data processor 1001, cause the apparatus 1000 to at least perform: detecting one or more items being used in a mobile device and a corresponding context; determining that the context is in a predetermined sub-context space. Furthermore, the data processor 1001 and the memory 1003 are configured to, with the data processor 1001, cause the apparatus 1000 to at least perform displaying a respective item in accordance with a preference pattern corresponding to the predetermined sub-context space.
  • the predetermined sub-context space and the corresponding preference pattern are stored in the mobile device or in a remote server in the form of a context tree.
  • the context tree is updated dynamically or periodically based on a situation that the one or more items are used.
  • embodiments of the present invention are described above with reference to the flowcharts and block diagrams as illustrated in the accompanying drawings. It needs to be explained that the embodiments of the present invention may further employ the form of a computer program product accessed by a computer-usable or computer-readable medium, and these media provide program code for use by a computer or any instruction executing system or for use in combination with them.
  • computer-usable or computer-readable mechanism may be any tangible means, which may comprise, store, communicate, broadcast or transmit programs for use by an instruction executing system, apparatus or device, or for use in combination with them.

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  • Computer Networks & Wireless Communication (AREA)
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  • Environmental & Geological Engineering (AREA)
  • Human Computer Interaction (AREA)
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  • Mobile Radio Communication Systems (AREA)

Abstract

Selon ses modes de réalisation, la présente invention concerne un procédé, un appareil et un produit programme d'ordinateur qui permettent de déterminer des préférences utilisateur sensibles au contexte. Ledit procédé comprend l'enregistrement d'un contexte dans lequel un ou plusieurs éléments d'un dispositif mobile sont utilisés. Ce procédé comprend en outre la détermination d'un ou plusieurs sous-espaces de contexte basés sur la distribution du contexte. De plus, le procédé comprend également la création d'un profil de préférences correspondant pour chaque sous-espace de contexte de façon à afficher un élément respectif, lorsqu'une détection indique que le dispositif mobile est dans un sous-espace de contexte, en fonction du profil de préférences correspondant à ce sous-espace de contexte. Selon certains modes de réalisation de la présente invention, dans les préférences utilisateur liées à un contexte, des éléments fréquemment utilisés par un utilisateur dans le contexte sont classés de manière à ce que l'utilisateur puisse les obtenir facilement et rapidement.
PCT/FI2013/050458 2012-06-08 2013-04-24 Détermination de préférences utilisateur sensibles au contexte WO2013182736A1 (fr)

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CN103729281B (zh) * 2014-01-17 2016-05-25 中国联合网络通信集团有限公司 应用程序使用信息采集方法及移动终端
CN105589898A (zh) * 2014-11-17 2016-05-18 中兴通讯股份有限公司 数据存储方法及装置
CN108196663B (zh) * 2018-01-16 2020-04-17 维沃移动通信有限公司 一种人脸识别方法、移动终端

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