Interaction segment at a daily routine-based place pair Classification based on interaction segment Short-period Long-period duration Work-Leisure Leisur Classification based on Home-Leisure -Leisure Work Work types of daily routine- based place pair Classification based on the duration of Strangers Strangers Strangers Level-4 closeness Team Customers Relatives Friends Collaborators Colleagues members in same building Family Fig.7.Decision tree of closeness-based social relationships classification. people's static and active status.We empirically set a threshold Our approach is based on the intuition that different types to the activeness score of each significant AP and further of social relationships show different temporal patterns for determine the activeness (i.e.,active or static)of a staying various levels of physical closeness in the overlapped daily segment based on the majority vote over all significant APs. routine-based place,which reveal different degrees of interac- VI.SOCIAL RELATIONSHIPS AND DEMOGRAPHICS tions between two people.Figure 6 illustrates this intuition INFERENCE by comparing the interaction segment characteristics for two In this section,we present how our system utilizes the pairs of social relationships (i.e.,neighbor and family,and activity features provided by staying segments to derive the team member and collaborator).which can be differentiated user's fine-grained social relationships and demographics. from spacial closeness degree or temporal pattern difference. A.Closeness-based Social Relationships Derivation We design a triple-layer decision tree for relationships The social relationship is about how two people interact with classification based on examining the characteristics of the each other in their daily lives,including both face-to-face inter- interaction segments between two people (i.e.,the temporal action and event the hidden interaction without encountering. and spatial patterns of their physical closeness).Figure 7 Therefore,to infer social relationships,we need to understand illustrates the flow of the decision tree.In the first layer, not only a person's activities at a place,but also how the person the decision tree takes the detected interaction segment of interacts with other people at different places.Towards this two people in one day as input,and classifies it into two end,we define the interaction segment based on the staying classes (i.e.,Short-period and long-period interaction segment) segments between two people to capture the temporal and by examining the duration of the interaction time slot in spatial characteristics of their interactions.The basic idea is the interaction segment.The intuition behind this layer is that,we first extract and characterize the interaction segments that people usually spend most time at several places (e.g., between a target user and other people based on their staying homes,offices,or schools)and shorter time at other places segments and corresponding activity features.Then we utilize (e.g.,diners,grocery stores,and post office)and so as their the temporal and spatial patterns of the closenesses of the interactions at these places.In the second layer,we make interaction segments as well as the individual daily place finer decisions from the result of the first layer.In particular, contexts to derive fine-grained relationships. we examine the daily routine-based place pair of the interac- 1)Interaction Segment Characterization:We generate inter- tion segment to further classify the interaction based on the action segments based on the staying segments of two people people's individual daily place contexts.Because the short- in the same day.Specifically,we first find the temporally period interaction should happen at least at one person's leisure overlapped segments between the daily staying segments from place in logic,the short-period interaction segment leads to the two people.Then we estimate the physical closeness be- three possible branches:workplace-leisure,home-leisure and tween every two overlapped segments by using the Equation 1.leisure-leisure.And the long-period interaction segment leads Only long overlapped segments(i.e.,time duration is longer to the pairs of workplace-workplace and home-home.In the than 10min)with at least level-1 closeness are considered last layer,we further detail the classification of the interaction as valid interaction segments.Each overlapped segment is by analyzing the physical closeness of the interaction segment described by three characteristics:1)interaction time slot,2) to infer fine-grained relationships.Specifically,we examine daily routine-based place pair based on the two users'same whether the level-4 closeness of the interaction segment is or different personal daily place contexts at the interaction non-zero or not,which suggest the two people have or not place (e.g.,Home-Home or Work-Leisure),and 3)physical have the face-to-face interaction in the place.The duration closeness,which correspond to when,where and how closely of the face-to-face interaction allows the decision tree to the two people interact,respectively.Finally,the characterized further distinguish social interaction into 8 categories of fine- interaction segments represent users'interaction at the place. grained relationships:Customers,Relatives,Friends,Team 2)Closeness-based Social Relationships Classification: members,Collaborators,Same-building Colleagues,Family After determining the interaction segments,we classify the and Neighbors,as well as excluding strangers user's social relationships leveraging the temporal and spatial The decision tree infers the possible relationships between patterns of the physical closeness in the interaction segments. two people based on their one-day social interactions.But(  &%'  )      ' " " *  +      '     ' '   (           '        '   "    ' " "  '  %    +,  %  Fig. 7. Decision tree of closeness-based social relationships classification. people’s static and active status. We empirically set a threshold to the activeness score of each significant AP and further determine the activeness (i.e., active or static) of a staying segment based on the majority vote over all significant APs. VI. SOCIAL RELATIONSHIPS AND DEMOGRAPHICS INFERENCE In this section, we present how our system utilizes the activity features provided by staying segments to derive the user’s fine-grained social relationships and demographics. A. Closeness-based Social Relationships Derivation The social relationship is about how two people interact with each other in their daily lives, including both face-to-face inter￾action and event the hidden interaction without encountering. Therefore, to infer social relationships, we need to understand not only a person’s activities at a place, but also how the person interacts with other people at different places. Towards this end, we define the interaction segment based on the staying segments between two people to capture the temporal and spatial characteristics of their interactions. The basic idea is that, we first extract and characterize the interaction segments between a target user and other people based on their staying segments and corresponding activity features. Then we utilize the temporal and spatial patterns of the closenesses of the interaction segments as well as the individual daily place contexts to derive fine-grained relationships. 1) Interaction Segment Characterization: We generate inter￾action segments based on the staying segments of two people in the same day. Specifically, we first find the temporally overlapped segments between the daily staying segments from the two people. Then we estimate the physical closeness be￾tween every two overlapped segments by using the Equation 1. Only long overlapped segments (i.e., time duration is longer than 10min) with at least level-1 closeness are considered as valid interaction segments. Each overlapped segment is described by three characteristics: 1) interaction time slot, 2) daily routine-based place pair based on the two users’ same or different personal daily place contexts at the interaction place (e.g., Home-Home or Work-Leisure), and 3) physical closeness, which correspond to when, where and how closely the two people interact, respectively. Finally, the characterized interaction segments represent users’ interaction at the place. 2) Closeness-based Social Relationships Classification: After determining the interaction segments, we classify the user’s social relationships leveraging the temporal and spatial patterns of the physical closeness in the interaction segments. Our approach is based on the intuition that different types of social relationships show different temporal patterns for various levels of physical closeness in the overlapped daily routine-based place, which reveal different degrees of interac￾tions between two people. Figure 6 illustrates this intuition by comparing the interaction segment characteristics for two pairs of social relationships (i.e., neighbor and family, and team member and collaborator), which can be differentiated from spacial closeness degree or temporal pattern difference. We design a triple-layer decision tree for relationships classification based on examining the characteristics of the interaction segments between two people (i.e., the temporal and spatial patterns of their physical closeness). Figure 7 illustrates the flow of the decision tree. In the first layer, the decision tree takes the detected interaction segment of two people in one day as input, and classifies it into two classes (i.e., Short-period and long-period interaction segment) by examining the duration of the interaction time slot in the interaction segment. The intuition behind this layer is that people usually spend most time at several places (e.g., homes, offices, or schools) and shorter time at other places (e.g., diners, grocery stores, and post office) and so as their interactions at these places. In the second layer, we make finer decisions from the result of the first layer. In particular, we examine the daily routine-based place pair of the interac￾tion segment to further classify the interaction based on the people’s individual daily place contexts. Because the short￾period interaction should happen at least at one person’s leisure place in logic, the short-period interaction segment leads to three possible branches: workplace-leisure, home-leisure and leisure-leisure. And the long-period interaction segment leads to the pairs of workplace-workplace and home-home. In the last layer, we further detail the classification of the interaction by analyzing the physical closeness of the interaction segment to infer fine-grained relationships. Specifically, we examine whether the level-4 closeness of the interaction segment is non-zero or not, which suggest the two people have or not have the face-to-face interaction in the place. The duration of the face-to-face interaction allows the decision tree to further distinguish social interaction into 8 categories of fine￾grained relationships: Customers, Relatives, Friends, Team members, Collaborators, Same-building Colleagues, Family and Neighbors, as well as excluding strangers. The decision tree infers the possible relationships between two people based on their one-day social interactions. But