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Recommender Systems - Coggle Diagram
Recommender
Systems
Collaborative Filtering
Neighborhood-Based
User-Based
(1.3 p15-24)
Item-Based
(1.3 p26-29)
Model-Based
Matrix Factorization
(2.1 p3-10)
Algorithms
ALS
(2.1 p12-16)
SVD
(2.1 p22-24)
Gradient Descent (SGD or Adam)
(2.1 p11)
Pros and Cons
(2.1 p25)
Classification
Clustering
Advantages
(1.3 p3)
Evaluation
(1.3 p32-35)
Mean Percentage Ranking (MPR)
Estimating Lift over Random
Content-based
Advantages
(1.3 p3)
(2.2 p19)
Components
(2.2 p5-18)
Content Analyzer
Profile Learner
Filtering
Disadvantages
(2.2 p20)
Serendipity issue
(2.2 p28-30)
Association Mining
Market Basket Analysis
(1.2 p3-6)
Metrics
(1.2 p7-8)
Support
Confidence
Lift
Algorithms
Apriori
(1.2 p9)
FP-Growth
Applications
(1.2 p10)
Issues (1.2 p11)
Level of details for the items to recommend
(1.2 p15-18)
Challenges and Issues
(3.3 p25-30)
Implicit Ratings
(3.1 p2-4)
Often treated as Binary
(3.1 p5-7)
Use Jaccard Similarity
Methods
ALS with implicit feedback
(3.1 p8)
Bayesian Personalised Ranking
(3.1 p9)
Neural Network
(3.2 p3-14)
Graph
(3.2 p15-16)
Hybrid
(3.3 p2-14)