Hello everyone my name is Yali Fan,
I'm from Sun Yat-Sen university, China.
The topic of my presentation is personalized context-
aware
collaborative online activity
prediction.
My presentation will be divided into these five parts.
First,
I'd like to introduce the background of our work.
With the increasing prevalence of mobile devices.
Nowadays,
users can connect to the Internet anytime and anywhere by various apps in their smartphones,
thus generating a large amount of online
activity data. Naturally, individual online activity contains temporal and spacial information,
and is highly influenced by these two contexts.
For example,
in this picture, the user uses different apps at different time periods. In the early morning,
the user may prefer to use news apps while in the evening he may like to use video apps.
As for location context,
the user is likely to use social apps and video apps at home while using book apps on the subway.
Since users have different
online activities in different locations and different time periods,
context-aware
online activity prediction becomes very
necessary.
Well, solving this problem is actually very difficult,
there are mainly two challenges.
First,
the relationship among users, activities, time and location is pretty complex, because for a user, location and time contexts are jointly influence
the user's online
activity behavior and for different users,
they may use different apps,
even when they are in the same location and at the same time.
So first of all,
we should know how to model the complex relationship among users,
activities and time, locations.
The second challenge is the data sparsity issue,
which is often a problem for prediction and recommendation tasks.
On the one hand,
for each user,
we may only observe a small part of his
historical online activity,
On the other hand,
for each user,
the scenarios that can be observed are also very limited.
Here, the scenario means time and location pair.
This causes the data much sparser.
Now I will show our solutions. For challenge 1, we propose to build a user-location-time-activity tensor to model such complex relationship.
Tensor is a higher-order matrix. It is regarded as an efficient way to store context information and can represent complex relationship among multiple dimensions.
so it can be utilized to solve context-aware
problem. Like the first picture,
the online activity data is stored by a 4-D tensor X.
For the second challenge, to make up for sparsity issues, we find offline
check-in data is very suitable to serve as auxiliary data.
Look at the second picture,
the check-in data records
the users' activity in the real physical world.
Specifically, it records the users' visiting behavior toward different POIs
in specific locations and time slots.
So it contains rich information about time and location
contextual knowledge, so we can consider to transfer such knowledge from it to better
learn the semantic meanings of time and location
contexts of online activity,
then further to help the accurate prediction.
Next is our model framework. It includes three parts: data and input, generative model, features and output.
First,
data and input.
We consider two types of data,
online app data and offline check-in data.
The app data is regarded as the targeted domain,
while the check-in data represents the auxiliary domain. Then we construct a 4-D tensor X
and a 3-D tensor Y, respectively.
Then
we propose a generative model called Collaborative Tensor Factorization,
which is a transfer learning based method to handle both tensor X and tensor Y and transfer contextual knowledge among them. Then we
obtain all the latent matrices of users, locations,
time slots, app activities and POIs. Then we are able to reconstruct the tensor X
to realize the online activity prediction.
Okay,
now the evaluation results. We use two datasets in our study Telecom
dataset and Check-in
dataset. The Telecom dataset records the users' app usage activity during 7 days and it covers about
2000 apps. The check-in data records users' visiting
behavior towards POIs. It lasts for 3 months and its POIs
can be categorized into 16 categories.
The three figures below show the performance of our model and other methods under different
data sparsity levels, and we use 3 metrics: top 3 accuracy, top 5 accuracy and nDCG5.
Here,
the higher accuracy or higher nDCG represents that the prediction is more accurate.
We can see that the performance of our model outperforms other baselines,
especially at the higher sparsity levels.
For example,
on top 3 accuracy,
our model can achieve
45% improvement with 60% sparsity level. On top 5 accuracy,
42% improvement and on nDCG5, 10% improvement.
Now we'll conclude our presentation. First, to achieve personalized
context-aware
online activity prediction, we introduce offline
check-in data as auxiliary data to learn the semantic information of location and time contexts.
Second,
we propose a transfer learning based collaborative tensor factorization
method to jointly model the relationship of different entities and learn their latent feature matrices.
Third, our method outperforms the other state-of-the-arts,
especially in high sparsity levels.
That's all for my presentation.
If you have any question,
Welcome to
Welcome to contact me.
Besides, the Telecom Dataset is available by this link.
That's all thank you.