deep recommender system: fundamentals and advances · 2021. 4. 20. · spider man toy iron man...
TRANSCRIPT
Deep Recommender System:Fundamentals and Advances
Xiangyu Zhao1, Wenqi Fan2, Dawei Yin3, Jiliang Tang1
1Michigan State University, 2The Hong Kong Polytechnic University, 3Baidu Inc. Tutorial website: https://deeprs-tutorial.github.io
1Data Science and Engineering Lab
2
Recommender Systems
Recommender Systems
Informationoverload
Age of Information Explosion
Recommend item X to user
Items can be: Products, News, Movies, Videos, Friends, etc.
3
Recommender Systems
A B C
Recommendation has been widely applied in online services:- E-commerce, Content Sharing, Social Networking ...
Product Recommendation
4
Recommender SystemsRecommendation has been widely applied in online services:- E-commerce, Content Sharing, Social Networking ...
News/Video/Image Recommendation
5
Recommender SystemsRecommendation has been widely applied in online services:- E-commerce, Content Sharing, Social Networking ...
Friend Recommendation
Problem Formulation
Historical user-item interactions or additional side information (e.g., social relations, item’s knowledge, etc.)
INPUTPredict how likely a user would interact with a target Item (e.g., click, view, or purchase)
OUTPUT
6
Item set
User set social relations, age, gender, occupation, etc.
year, genre, actor,reviews, etc.
Side information
Side information
! users
! items (movies) …
…
User-item Interaction History
55
5 5
3
5
14
Spider Man
Iron ManToy Story
MinionsCaptainAmerica
Lily LalaPeter David
7
Recommender Systems
• Collaborative Filtering (CF) is the most well-known technique for recommendation.- Similar users (with respect to their historical interactions) have similar preferences.- Modelling users’ preference on items based on their past interactions (e.g., ratings and clicks).
• Learning representations of users and items is the key of CF.
items …
users …! users
! items (movies) …
…
User-item Interaction History
55
5 5
3
5
14
Spider Man
Iron ManToy Story
MinionsCaptainAmerica
Lily LalaPeter David
Task: predicting missing movie ratings in Netflix.
5 4…
5…
5…
5 12 5
! users
" items (movies)
Lily
…
?…
? ?? ? ? ?
…? ?
…? ? ?
Spider Man
Iron ManToy Story
MinionsCaptainAmerica
…
User-item Rating Matrix𝐑
8
Matrix Factorization
≈𝒒!𝒑"#
𝑑
𝑑 Predicted rating of item 𝒋 for user 𝒊 :
�̂�"! ≈ 𝒑"#𝒒! = *$%&
'
𝑝"$ 𝑞!$
User representations 𝐏 ∈ ℝ!×#
Items representationsQT ∈ ℝ#×$
𝐑 ≈ 𝐏 × 𝐐T
× ≪ 𝒎𝒊𝒏(𝒏,𝒎)
User-item Rating Matrix𝐑
5 4…
5
…5
…5 1
2 5
𝑛us
ers
𝑚 items (movies)
Lily
Lala
Peter
David
…
Spider Man Iron Man
Toy Story Minions
CaptainAmerica
…
Lily
Lala
Peter
David
…
…
Spider Man Iron Man
Toy Story Minions
CaptainAmerica
?…
? ?
? ? ? ?
…? ?
…? ? ?
… …
4𝒓𝒊𝒋
Ø Learn representations to describe users and items based on user-item rating matrix 𝐑.
…
…
…
Objective with rating reconstruction error:
𝑚𝑖𝑛 𝐏, 𝐐 *",!∈.
(𝑟"! − �̂�"!)/ = *",!∈.
(𝑟"! − 𝒑"#𝒒!)/
Given 𝑛×𝑚 matrix 𝐑, the goal is to learn: Users/Items representations: 𝐏 ∈ ℝ0×', 𝐐 ∈ ℝ2×'
Observed user-item interactions (known): 𝑺
observed rating scorepredicted rating score
≈𝒒!𝒑"#
𝒅
𝒅
User representations 𝐏 ∈ ℝ!×#
Item representationsQT ∈ ℝ#×$
×
≪ 𝒎𝒊𝒏(𝒏,𝒎)
User-item Rating Matrix𝐑
5 4…
5
…5
…5 1
2 5
𝑛us
ers
𝒎 items (movies)
…
…
Task: rating prediction in Netflix
9
Matrix Factorization
10
Deep Learning is Changing Our Lives
11
Reinforcement Learning (RL)
Graph Neural Networks (GNNs)
Automated Machine Learning(AutoML)
Deep Recommender Systems
Fundamentals of Deep Recommender Systems
00 1
Field1 Fieldm FieldM
01 0 10 0
Manually Deisgned Architectures§ Expert knowledge§ Time and engineering efforts
Graph-structured Data§ Information Isolated Island
Issue: ignore implicit/explicit relationships among instances
ItemsUsers!! "!!! ""!! "#!" "!
… …
Recommendation Policies§ Offline optimization§ Short-term reward
12
Agenda
• 9:00 – 9:10 Introduction to Recommender Systems (Jiliang Tang)• 9:10 – 9: 35 Fundamentals of Deep Recommender Systems (Wenqi Fan)• 9: 35 – 10:15 Reinforcement Learning for Recommendations (Xiangyu Zhao)• 10:15 – 10:25 Coffee Break (10 mins)• 10:25 – 11:00 Graph Neural Network for Recommendations (Wenqi Fan)• 11:05 – 11:35 AutoML for recommendations (Xiangyu Zhao)• 11:35 – 11:45 Conclusion and QA session