In this work we address the development of a smart personal assistant that is capable of anticipating a user's information needs based on a novel type of context: the person's activity inferred from her check-in records on a location-based social network. Our main contribution is a method that translates a check-in activity into an information need, which is in turn addressed with an appropriate information card. This task is challenging because of the large number of possible activities and related information needs, which need to be addressed in a mobile dashboard that is limited in size. Our approach considers each possible activity that might follow after the last (and already finished) activity, and selects the top information cards such that they maximize the likelihood of satisfying the user's information needs for all possible future scenarios. The proposed models also incorporate knowledge about the temporal dynamics of information needs. Using a combination of historical check-in data and manual assessments collected via crowdsourcing, we show experimentally the effectiveness of our approach.
On this page we provide additional resources related
to the above mentioned paper. The resources include
experiment details, datasets to download, further
analyses and algorithms.
Currently, the paper is in the reviewing process and
from obvious reasons we provide only previews
of the files at the moment.
Experiment #2 (textual mode)
Experiment #3 (card-based mode)
Experiment #5 (top category)
Experiment #6 (second category)
|Experiment||#Tasks||Workers/task||Payment/task||Worker satisfection||Payment total||Download dataset|
|#1||9||30||10.0 ¢||86.0%||\$ 27||download|
|#2||1125||5||0.60 ¢||68.0%||\$ 34||download|
|#3||1125||5||0.60 ¢||66.9%||\$ 34||download|
|#4||335||9||2.00 ¢||84.0%||\$ 60||download|
|#5||1148||5||0.75 ¢||60.0%||\$ 43||download|
|#6||1240||3||0.75 ¢||72.0%||\$ 28||download|
|Total||\$ 226||download all|
We retrieved query suggestions for a sample of Foursquare POIs (see Section 3.2.1). Here we provide this data after cleansing steps described in the paper.