Multiple location profiling for users and relationships from social network and content
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Multiple Location Profiling for Users and Relationships from Social Network and Content. Rui Li, Shengjie Wang, Kevin Chen- Chuan Chang University of Illinois at Urbana-Champaign. Users’ Locations are important for many information services. Local Content Recommendation. Content Provider.

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Multiple Location Profiling for Users and Relationships from Social Network and Content

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Multiple location profiling for users and relationships from social network and content

Multiple Location Profiling for Users and Relationshipsfrom Social Network and Content

Rui Li, Shengjie Wang, Kevin Chen-Chuan Chang

University of Illinois at Urbana-Champaign


Users locations are important for many information services

Users’ Locations are important for many information services

Local Content Recommendation

Content Provider

User

Social Network

Local Friends Recommendation

Carol

Lives in: Los Angeles

and many others.


Community has explored social network and content to profile users locations

Jean

Mike

Bob

Carol

San Diego

Community has explored social network and content to profile users’ locations.

?

LA

?

Lucy

Gaga

Terrible LA traffic!

Austin

Want to go to Honolulu for Spring vacation!

NY

See Gaga in Hollywood.

Good Morning!

Tweets

Social Network

Profiling a User’s Home Location

Location: Los Angeles


Problem 1 they only profile a single home location

Problem 1 They only profile a single home location.

  • Carol lives Los Angeles and studied at Uni. of Texas at Austin

Locations of a user’s friends

Tweeted Locational Words

  • incomplete

  • inaccurate


Problem 2 they totally miss profiling relationships

Problem 2 They totally miss profiling relationships.

both Carol and Bob work at Los Angeles

both Carol and Lucy studied at Austin

Carol lives Los Angeles

  • useful !


We focus on multiple location profiling for users and relationships

Jean

Mike

Bob

Carol

San Diego

We focus on multiple location profiling for users and relationships.

?

LA

?

Lucy

Gaga

Terrible LA traffic!

Austin

Want to go to Honolulu for Spring vacation!

NY

See Gaga in Hollywood.

Good Morning!

Carol in Real-world

Location: Los AngelesEducation: Uni. of Texas at Austin

Carol’s Location Profile:Los Angeles, Austin

Carol follows Lucy:Austin, Austin


Our approach is to build a model to connect known relationships with unknown locations

Our approach is to build a model to connect known relationships with unknown locations.

Known Relationships

Unknown Locations

MLP Model

Generation Model

Inference Algorithm


There are three challenges for building mlp

There are three challenges for building MLP.

  • Challenge 1How to connect users’ locations with relationships?

    • from users’ locations to following relationships

    • from users’ locations to tweeting relationships

  • Challenge 2 How to model that the relationships are mixed?

    • some relationships are not based on locations.

    • each relationship is based on a different location.

  • Challenge 3 How to utilize home locations from labeled users?


Challenge 1 a we need to connect following relationships with two users locations

Challenge 1.A We need to connect following relationships with two users’ locations.

Even a user has only one location follows others from different locations.

The following probability as the probability generating a following relationship from a user to another user based on their locations


Observation we explore following probability via investigating a corpus

Observation We explore following probability via investigating a corpus

  • It captures our intuition well.

  • It fits a power law distribution.


Solution we derive location based following model for following probability

Solution: We derive location-based following model for following probability.

The location-based following model


Challenge 1 b we need to connect tweeting relationships with a user s location

Challenge 1.B We need to connect tweeting relationships with a user’s location.

User at a location tweets different locations.

The tweeting probability as the probability generating a tweeting relationship from a user to a venue based on a location


Observation we explore tweeting probability via investigating a corpus

Observation We explore tweeting probability via investigating a corpus.

  • They capture our intuition well.

  • They can be modeled as a set of multinomial distributions.


Solution we derive location based tweeting model for tweeting probability

Solution: We derive location-based tweeting model for tweeting probability.

The location-based tweeting model


Challenge 2 a t here are both noisy and location based relationships

Challenge 2.A There are both noisy and location-based relationships.

Noisy relationships are not useful!


Solution we propose a mixture component for two types of relationships

Solution: We propose a mixture componentfor two types of relationships.

  • A relationship is generated based on either a location-based model or a random model.

  • A binary model selector μ indicates which model is used.

  • The selector is generated via a binomial distribution


Challenge 2 b l ocation based relationships are related to multiple locations

Challenge 2.B Location-based relationships are related to multiple locations.

both Carol and Lucy studied at Austin

Carol lives Los Angeles

Accurate!

Complete!


Solution we fundamentally model users multiple locations in generating relationships

Solution: We fundamentally model users multiple locations in generating relationships.

Location profile as a multinomial distribution over locations.

Carol

{Los Angels 0.1, Austin 0.1, … }

Each relationship is based on one particular location from his profile.


Challenge 3 we should utilize observed locations from some users profiles

Jean

Mike

Bob

Carol

San Diego

Challenge 3 We should utilize observed locations from some users’ profiles.

?

LA

?

Lucy

Gaga

Austin

NY

20% users provide their home locations in their profiles.

  • they are useful for profiling locations!

  • we cannot use them directly to generate relationships!


Solution we utilize observed locations from as priors to generate users profiles

Solution: We utilize observed locations from as priors to generate users’ profiles.

We assume users profiles are generated prior distributions.

Home locations of users are likely to be generated.

Bob

{San Diego 0.9, Los Angels 0.05, …}


Therefore we arrive a complete model

Therefore, we arrive a complete model.


We evaluate our model on a large twitter corpus

We evaluate our model on a large Twitter corpus.

  • We crawled a subset of Twitter.

  • There are 139K users, 50 million tweets and 2 million following relationships.


Task 1 profiling users home locations mlp performs accurately and improves baselines

Task 1 profiling users’ home locations, MLP performs accurately and improves baselines.


Task 2 profiling users multiple locations mlp proforms accurately and completely

Task 2 profiling users’ multiple locations, MLP proforms accurately and completely.

Accurately

Completely

Precision and Recall at Rank 2

Locations in a similar region

Locations in different areas

Case Studies


Task 3 profiling following relationships mlp achieves 57 accuracy

Task 3 profiling following relationships, MLP achieves 57% accuracy.


Thanks and questions

Thanks and Questions !


Backup for questions

Backup for Questions


Experiments 1

Experiments 1

  • We use the home location provided in users’ profiles as ground truth.

  • We compare two baseline methods proposed in literature.


Experiments 2

Experiments 2

  • We manually labeled multiple locations of 1000 users, and obtained 585 users, who clearly have multiple locations.

  • We compare the same baseline methods as in the previous task.

  • We measure the performance in terms of “precision” and “recall”.


Experiments 3

Experiments 3

  • We manually labeled location assignments of 585 users, whose multiple locations are known to us, and obtained 4426 relationships.

  • We design a meaningful baseline method, which profile a relationship based users home locations.


Multiple location profiling for users and relationships from social network and content

We infer users’ locations and location assignments for relationships as latent variable in the joint probability.

MLP defines the joint probability of observations, parameters, and latent variables.

We infer users’ locations and locations assignments with the observed relationships and the given parameters.

We develop our algorithm based on the Gibbs sampling method.


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