Presentation from ECML-PKDD 2017 of joint work with my supervisors Helge Langseth and Heri Ramampiaro, about a Poisson factorization model for recommendations that integrates topic models in the items set and social connections between users.
code: https://github.com/zehsilva/poissonmf_cs
preprint version: https://inajourney.files.wordpress.com/2012/11/poissonmf_cs.pdf
Content-Based Social Recommendation with Poisson Matrix Factorization (ECML-PKDD 2017)
1. Content-Based Social
Recommendation with Poisson
Matrix Factorization
Eliezer de Souza da Silva
PhD student
Department of Computer Science, NTNU
Joint work with Helge Langseth and Heri Ramampiaro
ECML-PKDD 2017
2. 2
Introduction
● Basic Problem: Recommendation of items to users given user interaction
with some items
User 1 User 2 User 3
Item 1 Item 2 Item 3 Item 4
4. 4
Challenges and opportunities in RS
Research
User 1 User 2 User 3
Item 1 Item 2 Item 3 Item 4 Item 5
Topics
User 4
User
Social
Network
5. 5
Challenges and opportunities in RS
Research
● Incorporate
○ Social network analysis tools and methods
○ Content analysis (topic models,
sentiment/intent/mood)
○ New rich contextual information
■ location, activity, user intent/goal, etc.
6. 6
Joint modelling of user social
network and item topic content
● User social network
○ Homophily
○ Item exposure positively influenced by peers
(positive “peer-pressure”)
● Item content analysis
○ Enrich items latent factors with topic model
○ Cold start items
○ Preferences can be influenced by topics
12. 12
Inference
• Batch variational inference:
• Conjugate model with auxiliary variable “tricky” for each
Poisson likelihood term:
• Running time for each iteration depends on the sparse
observations:
– O(K(obs_W + obs_R + obs_S + U + D + W ))
13. 13
Item Recommendations
• Top-M items for each user:
– Approximate expected value of user-item matrix for each
unseen item for ranking
Rud User preferences
Shared item topic
intensity
Item topic offset
Weighted sum of social
network neighbors
interactions with item
18. 18
Conclusion
• Model including social and topic information in Poisson
matrix factorization using coupled latent factors
• Inference is computationally efficient with variational
inference
• Future work:
– Non-negative relational learning
– Non-parametric extensions
– Scalable inference (SVI)