项亮 推荐系统实践 从入门到精通

• When new user comes, his feedback on what items can help us better understand his interest?
– Not very popular – Can represent a group of items – Users who like this item have different preference with users who dislike this item
• Users’ behaviors on items can be represented by bi-part graph.
A B C 1 2 3 4 A B C 1 2 3 4 A B C 1 2 3 4 D D D
Graph-based
A D B C
1 2 3 4
• Two nodes will have high relevance if
The Blair Witch Project Pacific Heights Stir of Echoes Dead Calm Phantasm
4 (Horror)
The Terminator
Sleepy Hollow The Faculty
• Advantage
• Disadvantage
– High accuracy in rating prediction – Auto group items – Scalability is good – Learning-based – Incremental updating – Real-time – Explanation
User-based
pui =
User-based CF
v∈S ( u , K ) ∩ N ( i )
∑
wuv rvi
w uv =
N (u ) ∩ N (v) N (u ) ∪ N (v)
• Algorithm
– For user u, get items set N(u) this user like before. – Recommend items which are similar to many items in N(u) to user u.
References
• Users and items are connect by latent features.
A B C D b c a 1 2 3 4
Latent Factor Model
Latent Factor Model
ˆ rui = ∑ puk qik
0.5 0.9 0.8 0.8 0.3 0.0 Science Fiction Universe Physical
Graph-based
• Advantage
• Disadvantage
– Heterogeneous data
– Statistical-based – High cost for long path
• Multiple user behaviors • Social Network • Context (Time, Location)
1 (Sci-fi)
Latent Factor Model
2 (Crime) Jaws Lethal Weapon Reservoir Dogs La shou Shen tan Donnie Brasco The Fugitive Total Recall 101 Dalmatians Groundhog Day The Jungle Book 2 Antz The Aristocats Tarzan Back to the Future 3 (Family)
• Timestamp • Location • Mood
Data
Sheldon watch Star Trek with his friends at home
Algorithms
Recommender System Method Collaborative Filtering Content Filtering Social Filtering …… Graph-based Latent Factor Model ……
Data
Page view Behavior Favorite Vote Share
User
Add to playlist Facebook like Review
Watch video
All user All user
Size
Register user Register user Register user Register user
• Example: Gender and TV shows
User Cold Start
Data comes from IMDB : /title/tt0412142/ratings
User Cold Start
Male Age : 20-30 Theoretical physicist Doctor American Irreligious
Neighborhood -based
……
User-based
Item-based
……
• User-based • Item-based
– Digg
Neighborhood-based
– Amazon, Netflix, YouTube, Hulu, …
• Algorithm
– For user u, find a set of users S(u) have similar preference as u. – Recommend popular items among users in S(u) to user u.
Item-based
pui =
Item-based CF
j∈S ( i , K ) ∩ N ( u )
∑
w ji ruj
w ij =
N (i ) ∩ N ( j ) N (i ) ∪ N ( j )
Item-based CF
Why not use w ij =
N (i ) ∩ N ( j ) N (i )
Problem
• User behaviors data
Watch video Favorite Vote Share Page view Behavior All user All user User Add to playlist Facebook like Review Register user Register user Register user Register user Register user Register user
Bad when item size is large
Coverage
Cold start
Performance
Bad for new users Need to get many users history
Bad for new items
Only need to get current user’s history
Data
Very Large Large Small Small Small Small Size Middle Middle
• Which data is most important
– Main behavior in the website – All user can have such behavior – Cost – Reflect user interests on items
k
Science Fiction Universe Physical
0.9 0.5 0.7 0.1
0.9
Space Travel Animation Romance
Space Travel Animation Romance
0.0
• How to get p, q? puk qik ) + λ ( pu
Graph-based
A B C D
1 2 3 4
• A Graph-based Recommender System for Digital Library. • Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation.
Cold Start
• How to recommend items to new users?
– Non-personalization recommendation
• Most popular items • Highly Rated items
User Cold Start
– Using user register profile (Age, Gender, …)
Latent Factor Model
• rmatik.unitrier.de/~ley/db/indices/atree/k/Koren:Yehuda.html
References
• Problems
– User cold start : new users – Item cold start : new items – System cold start : new systems
• Implicit feedback data: only have positive samples and missing values, how to select negative samples?