![Page 1: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/1.jpg)
Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media
Charalampos Chelmis, Viktor K Prasanna [email protected]
MSM 2013, Paris, France
![Page 2: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/2.jpg)
• Introduction • Structure of Tripartite Graphs • Generative Models of Tripartite Graphs • Social Link Classification Schemes • Evaluation • Conclusion
Overview
2
![Page 3: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/3.jpg)
• Social Networking is used for Content organization Content sharing
• Multiple media types • Users' activities
Reveal interests and tastes Hidden structure
• Description of Resources Text Tags / Hashtags
• Social Annotation Collective characterization of resources Use of synonyms for similar recourses Same keywords for different recourses
Introduction
3
![Page 4: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/4.jpg)
• How to address issues of synonymy and polysemy? Deal with space size explosion
• How to discover emergent structure in online tagging systems? Hidden topics
• How to capture users’ latent interests? Which subjects a user is mostly interested in? Which users have similar interests?
• How to model the process of social generation of annotations? How to capture the semantics of collaboration
• Why is this useful? Recommend people Recommend Tags / resources Clustering …
Research Questions
4
![Page 5: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/5.jpg)
• Set of actors (e.g. users) A={a1, ...,ak} • Set of concepts (e.g. tags) C = {c1, ..., cl} • Set of resources (e.g. photos) R ={r1, ..., rm}
Structure of Tripartite Graphs
5
![Page 6: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/6.jpg)
• The User-Concept Model Users are modeled based on their tag usage φ denotes the matrix of topic distributions
− multinomial distribution over N concepts − T topics being drawn independently
θ: the matrix of user-specific mixture weights for these T topics
• Captures users’ latent interests • Ignores Resources • Ignores the social aspect of tagging
• The User-Resource Model Resources become vocabulary terms
• Tags are ignored • Ignores the social aspect of tagging
Reducing the Tripartite Graph to Bipartite Structures
6
![Page 7: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/7.jpg)
• Topic-based representation • Model both resources & users’ interests • Multiple users may annotate resource r
• For each tag a user is chosen uniformly at random • Each user is associated with a distribution over
latent topics ɵ • A topic is chosen from a distribution over topics
specific to that user • The tag is generated from the chosen topic
φt: probability distribution of tags for topic t
The User-Resource-Concept Model
7
![Page 8: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/8.jpg)
• Tag Recommendation Automatic annotation enhancement Search improvement
• Clustering Community detection Organization of resources/tags in categories
• Navigation and Visualization Social browsing
• Next we focus on recommending people
Recommendation
8
![Page 9: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/9.jpg)
• Classification Based on Latent Interests Measure “tastes” distance with respect to latent topics distribution Pointwise squared distance between feature vectors of users u and v Other measures to consider
− Kullback Leibler (KL) divergence − Cosine similarity
• Objective: Minimize the distance between linked users
• Focus on topical homophily Ignore network effects
• Prior work uses network proximity as indicator of link formation
Social Link Recommendation Using Latent Semantics & Network Structure
9
]v))(k,-u)(k,(,,v))(1,-u)(1,[( v)F(u, 22 ΘΘΘΘ=
F(u,v) = 0 => u,v have identical distributions
F(u,v) > 0 => distributions diverge
![Page 10: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/10.jpg)
• Latent Topics & Local Structure CN(u,v) = common neighbors between users u and v
− Simplicity and computational efficiency
Latent topics similarity
• Latent Topics & Global Structure SD(u,v) = shortest distance between users u and v
• Non separable training set => inefficient classifiers • Aggregation Strategy
Reduce the number of training samples Produce more efficient classifiers Average latent similarity of user pairs with k common
neighbors:
Social Link Recommendation Using Latent Semantics & Network Structure
10
v)]CN(u, v),(u,[ v)F(u, σ=
∑==
=k k : pp p
(p)|k k : p|
1 (k) avg σσ
v)]SD(u, v),(u,[ v)F(u, σ=
22 ),(),(
),(),(),(
∑∑∑
ΘΘ
ΘΘ=
tt
t
vtut
vtutvuσ
![Page 11: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/11.jpg)
• Objectives Ability to uncover subliminal collective knowledge Evaluate performance of “people” recommendation
• Setting 2.4 GHz Intel Core 2 Duo, 2 GB memory, Windows 7
• Real-world Dataset Last.fm online music system
− social relationships − tagging information − music artist listening information
Statistics − 1,892 users − 25,434 directed user friend relations
− 17,632 artists UR Model vocabulary size − 92,834 user-listened-artist relations
− 11,946 unique tags UC and URC vocabulary size − 186,479 annotations (tuples <user, tag, artist>)
Experimental Analysis
11
![Page 12: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/12.jpg)
Sample Topics
12
![Page 13: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/13.jpg)
• Evaluate ability to predict tags/resources on new users Perplexity
• Split dataset into two disjoint sets 90% for training
• Lower perplexity indicates better generalization
• URC better overall Exploits more information
• UC Organizes tags in “clusters”
• UR Inferior quality due to noise
Predictive Power
13
![Page 14: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/14.jpg)
• Split dataset into two disjoint sets 10%, 25%, 50%, 75% for training, rest for testing
• Evaluation process Randomly sample 12,716 pairs of users 50% true links, 50% negative samples Compute similarity of user pairs Sort users in decreasing order of similarity Add links between users with highest similarity
Recommendation of Social Ties
14
![Page 15: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/15.jpg)
• Latent Topics & Shortest Distance Aggregates all true links training similarity values in a single point Least effective
• Ensemble achieves best precision • Over fitting for training size > 50% • Recall drops as dataset size increases
Recommendation of Social Ties
15
[Latent Topics & Local Structure]
[Latent Topics]
[Ensemble]
![Page 16: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/16.jpg)
• In social media number of true links << absent links • High performance for both classes
True negatives easier to classify correctly Degradation in performance for true positives
• Reasonable results for practical purposes
How about High Class Imbalance?
16
[Latent Topics & Local Structure]
[Latent Topics]
[Ensemble]
![Page 17: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/17.jpg)
• Baselines Cosine Similarity (CS) Maximal Information Path (MIP)
• Evaluation Criterion Area under the receiver-operating characteristic curve (AUC)
• Baselines AUC Computed over the complete dataset Biases the evaluation in favor of the baselines CS AUC = 0.6087 MIP AUC = 0.6256
• Same evaluation process as before • Compute performance lift
% change over best performing baseline Positive % denotes improvement
Comparison to Tag-based similarity metrics
17
![Page 18: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/18.jpg)
• Not all schemes can beat the baseline For 10% training data ≤10% AUC loss But, significant speedup due to minimal training dataset
• Latent Topics & Local Structure Scheme consistently better
Comparison to Tag-based similarity metrics
18
Training dataset size
[Latent Topics & Local Structure]
[Latent Topics]
![Page 19: Exploring Generative Models of Tripartite Graphs for Recommendation in Social Media](https://reader033.vdocuments.net/reader033/viewer/2022060111/5562641ad8b42aed7d8b4dfb/html5/thumbnails/19.jpg)
• Three generative models of tripartite graphs in social tagging systems
• Modeling of users’ interests in a latent space over resources and metadata
• Limitations Ignore several aspects of real-world annotation process, such as topic
correlation and user interaction
• Achieve great performance in the recommendation task Accurate predictors of social ties in conjunction with structural
evidence Proposed aggregation strategy to reduce number of training samples
• Future work Incorporate other types of resources Automatically identify most discriminative latent topics and discard
uninformative resources and metadata
Concluding Remarks
19