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Personalized
Collaborating Filtering
Memory Based
User
Item
Model Based
Matrix Factorization Based
Funk Matrix Factorization
SVD/SVD++
PMF/Bayesian PMF/ Non Linear PMF
None Negative MF
ALS
FM
FFM
RankFM
LTFM
BMF
Slope One
RMF
NPCA
IMF/BPR
Content Based Filtering
Item-Centric
User-Centric
Hybrid Methods
Meta-level
Feature combination
Feature augmentation
Mixed hybridization
Cascade hybridization
Switching hybridization
Weighted hybridization
Deep Learning Based
LLM Empowerd
Non-personalized
Most Popular Items
Recommendation
LightFM
Hybrid matrix factorization
Outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios
Semantic features encoding
can be used for related recommendation tasks such as tag recommendations
Provide several evaluation metrics
auc_score
precision_at_k
recall_at_k
reciprocal_rank
Implicit feedback recommender
Implements WARP (Weighted Approximate-Rank Pairwise) loss
Paralellized via HOGWILD SGD.
Battle tested by many developers and is very very fast
Different loss functions
logistic: useful when both positive (1) and negative (-1) interactions are present.
BPR: Bayesian Personalised Ranking 1 pairwise loss. Maximises the prediction difference between a positive example and a randomly chosen negative example.
WARP: Weighted Approximate-Rank Pairwise 2 loss. Maximises the rank of positive examples by repeatedly sampling negative examples until rank violating one is found. Useful when only positive interactions are present and optimising the top of the recommendation list (precision@k) is desired.
k-OS WARP: k-th order statistic loss 3. A modification of WARP that uses the k-th positive example for any given user as a basis for pairwise updates.
Deep Learning
Multi-Layer Perceptron Based Recommendation
NCF
DeepFM
Autoencoder Based Recommendation
AutoRec
Multi-VAE and Multi-DAE
CNN Based Recommendation
RNN Based Recommendation
GRU4Rec
Restricted Boltzmann Machines Based Recommendation
Neural Attention Models Based Recommendation
Attentive Collaborative Filtering
Hashtag Recommendation with
Topical Attention-Based LSTM
Neural AutoRegressive Based Recommendation
Deep Reinforcement Learning Based Recommendation
DRN
DEERS