Recommendations in LBSN Social Networks (Notes)

Recommendations in LBSN Social Networks

Section 2
Concepts of LBSN Social Networks:
New social structure made up of individuals connected by the interdependency derived from their locations in the physical World
As well as location-tagged media content
Here

The physical locations mean the instant location of a individual at a given timestamp and the
That's individual have accumulated in a certain preriod
　　　　

The interdependency mean includes not only the superficial message, and the people show up at the time of the same, but AL So some other message
like the common interests and behaviors

LBSN consists of three Graphs:location-locat Ion graph, User-user graph, user-location graph

-location-location graph:relations:physical distances or similarities or some users consecively visited
-user-user graph:1.physical Distances 2.friendship relationships 3. Relationships derived from their check in data
-user-location graph:starting from a user and end at a location, with the weight of the rating or the Times of visits

Unique Properties of Locations
1.hierachical:rank of the place, country->city->venue ... The hierachical level influences the connection of both users sharing same check in data
2.Measuable distances:three kinds of distances locations, User-location,user-user, location-location
3.Sequential Odering:not quite understand ...

Existing Challenges:
1.Location Context Awareness:

A) Current location,

{

1.different recommendations need different granularity,

2.distance influence user ' s dicision

3.current location Influence Next decision

}
b) The historical Locations of the user,

{

Data cannot is full and complete,

Constantly changing

}

c) Location History of others:social opinion

{

1.how to weigh different uesers ' data according to their Knowlede and experience }

2.Heterogeneous Domain
3.Rate of growth:constant changing and evolve Fast
4.cold-start problem and data sparsity

Section 3
Location recommendations:
A) Stanalone location recommendation:
a) User-profi Le based (content based):
Match user profile with Loaction meta data, does not suffer cold-start problem but poor R Ecommendation quality
B) user-loaction history:collabrative Filtering, steps
1.calculating similarity BETW Eeen users
2.selecting candidate Location Usig user;s current loaction (!!! This was differen from product rating)
3.scoring prediction

Some papers suggest solely using friends ' s data'll be is more efficiant
Some find that geograohical distance impact the most
some add a persona Lized travel distance model, which are TE biggest ompact, extending by considering general popularity
form a cate Gory-regularized matrix constructed from the user location History,thus considering both user preferences and category Sim Ilarity

Recommendations in LBSN Social Networks (Notes)