Recommended
More Related Content
What's hot
What's hot (20)
Similar to Introduction to Recommender System
Similar to Introduction to Recommender System (20)
Recently uploaded
Recently uploaded (20)
Introduction to Recommender System
- 1. Deep Learning for Recommendations: Fundamentals and Advances Wenqi Fan1, Xiangyu Zhao2, Dawei Yin3, Jiliang Tang4 1The Hong Kong Polytechnic University, 2City University of Hong Kong, 3Baidu Inc., 4Michigan State University 1 Data Science and Engineering Lab Tutorial website: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/
- 2. 2 Recommender Systems Recommender Systems Information overload Age of Information Explosion Recommend item X to user Items can be: Products, News, Movies, Videos, Friends, etc.
- 3. 3 Recommender Systems A B C Recommendation has been widely applied in online services: - E-commerce, Content Sharing, Social Networking ... Product Recommendation
- 4. 4 Recommender Systems Recommendation has been widely applied in online services: - E-commerce, Content Sharing, Social Networking ... News/Video/Image Recommendation
- 5. 5 Recommender Systems Recommendation has been widely applied in online services: - E-commerce, Content Sharing, Social Networking ... Friend Recommendation
- 6. Problem Formulation Historical user-item interactions or additional side information (e.g., social relations, item’s knowledge, etc.) INPUT Predict 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 5 5 5 5 3 5 1 4 Spider Man Iron Man Toy Story Minions Captain America Lily Lala Peter David
- 7. 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 5 5 5 5 3 5 1 4 Spider Man Iron Man Toy Story Minions Captain America Lily Lala Peter David Task: predicting missing movie ratings in Netflix. 5 4 … 5 … 5 … 5 1 2 5 ! users " items (movies) Lily … ? … ? ? ? ? ? ? … ? ? … ? ? ? Spider Man Iron Man Toy Story Minions Captain America … User-item Rating Matrix 𝐑
- 8. 8 Matrix Factorization ≈ 𝒒! 𝒑" # 𝑑 𝑑 Predicted rating of item 𝒋 for user 𝒊 : ̂ 𝑟"! ≈ 𝒑" # 𝒒! = * $%& ' 𝑝"$ 𝑞!$ User representations 𝐏 ∈ ℝ!×# Items representations QT ∈ ℝ#×$ 𝐑 ≈ 𝐏 × 𝐐T × ≪ 𝒎𝒊𝒏(𝒏, 𝒎) User-item Rating Matrix 𝐑 5 4 … 5 … 5 … 5 1 2 5 𝑛 users 𝑚 items (movies) Lily Lala Peter David … Spider Man Iron Man Toy Story Minions Captain America … Lily Lala Peter David … … Spider Man Iron Man Toy Story Minions Captain America ? … ? ? ? ? ? ? … ? ? … ? ? ? … … 4 𝒓𝒊𝒋 Ø Learn representations to describe users and items based on user-item rating matrix 𝐑.
- 9. … … … Objective with rating reconstruction error: 𝑚𝑖𝑛 𝐏, 𝐐 * ",!∈. (𝑟"! − ̂ 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 score predicted rating score ≈ 𝒒! 𝒑" # 𝒅 𝒅 User representations 𝐏 ∈ ℝ!×# Item representations QT ∈ ℝ#×$ × ≪ 𝒎𝒊𝒏(𝒏, 𝒎) User-item Rating Matrix 𝐑 5 4 … 5 … 5 … 5 1 2 5 𝑛 users 𝒎 items (movies) … … Task: rating prediction in Netflix 9 Matrix Factorization
- 10. 10 Deep Learning is Changing Our Lives
- 11. 11 Reinforcement Learning (RL) Graph Neural Networks (GNNs) Automated Machine Learning (AutoML) Deep Recommender Systems Fundamentals of Deep Recommender Systems Graph-structured Data § Information isolated island Issue: ignore implicit/explicit relationships among instances Items Users !! "! !! "" !! "# !" "! … … 0 0 1 Field 1 Field m Field M 0 1 0 1 0 0 Manually Deisgned Architectures § Expert knowledge § Time and engineering efforts Recommendation Policies § Offline optimization § Short-term reward Adversarial Attacks ... ... ... ... ... ... ... ... ... ... Normal Data Fake User Profiles Generating Fake User Profiles Attacker promote/demote Poisoning attacks: § Promote/demote items § White/grey/black-box attacks
- 12. 12 Agenda • Introduction to Recommender Systems (Jiliang Tang) • Fundamentals of Deep Recommender Systems (Wenqi Fan) • Reinforcement Learning for Recommendations (Xiangyu Zhao) • Coffee Break (10 mins) • Graph Neural Network for Recommendations (Wenqi Fan) • AutoML for Recommendations (Xiangyu Zhao) • Adversarial Attacks for Recommendations (Wenqi Fan) • Future