hi everyone i'm Zhou Qin a phd student at Rutgers university computer science department
today i'm going to present our work called MIMU mobile wi-fi usage influenced by many diverse
user behaviors. This is a joint work between Rutgers university and Chinese academy of sciences
i will follow this outline to introduce our work let's start with the introduction first our work
focuses on mobile wi-fi systems which is essentially wi-fi on public transport it
is a beneficial service for multiple parties for the riders they can have convenient access
to the internet for the bus company they can provide a better user experience to
attract more users and for the wi-fi service provider they can cast ads to make profits and
there are two reasons why the prediction is important the first is based on the bus wi-fi
log data we can do user profiling which can then be used for ads promotion and the recommendation
and the second reason is that we can do data traffic estimation and essentially we then can
do data caching to save cost on the data traffic consumption for the bus wi-fi service provider
our contributions are summarized here we are the first work
on usage prediction of large-scale mobile wi-fi system considering unbalanced usage distribution
also considering access irregularity and the mobility regularity we then propose a
two-dimensional usage interference framework from user dimension and device dimension we then have
a spatial and temporal attention modeling for access regularity and the mobility regularity
in the end we did the evaluation on large-scale data sets with more than 110 000
users and more than 4 000 buses we also discussed generalization application and the privacy issues
for the model design part in the beginning we have the pre-processing module basically we need to
format the raw wi-fi log data into session-based data to feed into the prediction model
there are two sub-models here the first is user dimensions sub-model
and the second is device dimension sub-model then we combine the two predictions here as the final
content prediction then based on that we can have content caching location service and wi-fi sharing
our problem is defined here given a device d which is a bus wifi device and the container
c the goal is to predict the boss wi-fi usage of content c on this device d denoted by ydc
and the ydc can be approached by two parts the first is the f function denoted by the user
dimension modeling and the second is g function represented by the device dimension those two
models share similar structures but with the different inputs and different potential modules
so those two attention modules are actually dedicated versions of self-attention for the
temporal one we consider high similarity for close timing and considering users taking bus at the
regular time and for the spatial one we consider high similarity for closed location and for the
same bus line we also consider that certain locations can only be visited by certain buses
and the third part is about the evaluation result we did our evaluation on the data sets with two
month long wi-fi log data and we have more than 6 000 devices and more than 150 000 active data
users from three cities and we show our results quantified about one metric which is accuracy here
and we also show some results on factors like a day or week data proportion
multiple city comparison and we also have one caching application to show cost saving rate hit
and miss ratio so this is the overall performance we consider two significant baselines one is
neural collaborative filtering one's deep spatial temporal residual network and across all cases our
MIMU achieve the best performance compiled to the baselines and we also show the dimension ablation
and show the performance in terms of day or week and then we have the contextual investigation
basically we show the performance of MIMU in multiple cities and we can see MIMU achieves
the best performance in the largest city shenzhen here in terms of accuracy
and we have the discussion and lessons learned here
so our work considered unbalanced usage dynamics which are from two levels the user level and the
device level and we also consider diverse usage participation which are mobility regularity and
access regularity we also have application and the implication in terms of personalized
remodeling and profiling based promotion for more details please check our papers thank you
and you can also check more about data and more related work on those two websites thank you
you