Recommenders.jl

This package aims to provide light-weight recommendation models, mainly for implicit feedback data. We want to provide

consistent interface for model training and inference
flexibility for input data with Tables.jl package, which offers simple, but powerful abstract interface for tabular data
robust baseline metrics for classic datasets. The comparison of advanced recommendation models to these baselines turns out to be challenge.

Implemented algorithms

Model	Ref.	Note
Most popular
ItemkNN	Item-based top-N recommendation algorithms
Matrix factorization	Neural Collaborative Filtering vs. Matrix Factorization Revisited	Implemented algorithm is only for implicit feedback.
BPR MF	BPR: Bayesian Personalized Ranking from Implicit Feedback	Simple SGD with negative samping is implemented instead of the original learnBPR algorithm.
SLIM ElasticNet	Efficient Top-N Recommendation by Linear Regression SLIM: Sparse Linear Methods for Top-N Recommender Systems
Random walk	Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time

Tuning and evaluation scripts are found in examples.

Random 80/20 split, treated as implicit feedback.

Model	Precision@10	Recall@10	nDCG@10	Best parameters
Most popular	0.1897	0.1182	0.2197
ItemkNN	0.3284	0.2180	0.3986	weighting: BM25 weighting_at_inference: false topk: 358 normalize: true normalize_similarity: true shrink: 0.00258278
Matrix factorization	0.3657	0.2416	0.4402	dimension: 32 l2_coeff: 0.01797358830471941 n_epochs: 128 n_negatives: 7 learning_rate: 0.017678089718746345
BPR MF	0.3463	0.2316	0.4083	dimension: 512 l2_coeff: 0.015587614364453028 n_epochs: 128 n_negatives: 12 learning_rate: 0.007785000886303088
SLIM ElasticNet	0.3626	0.2385	0.4359	k: 908 λminratio: 0.029052468222707274 l1_ratio: 0.003308685140740372
Random walk	0.2634	0.1828	0.3255	pixie_walk_length_scaling: false pixie_multi_hit_boosting: false terminate_prob: 0.9 total_walk_length: 51307 min_high_visited_candidates: 500 high_visited_count_threshold: 64

Random 80/20 split, treated as implicit feedback.

Model	Precision@10	Recall@10	nDCG@10	Best parameters
Most popular	0.1816	0.06661	0.2022
ItemkNN	0.3311	0.1489	0.3734	weighting: TF-IDF topk: 43 normalize: true normalize_similarity: true shrink: 0.453735
Matrix factorization	0.3752	0.1687	0.4203	dimension: 128 l2_coeff: 0.005720177108336541 n_epochs: 256 n_negatives: 18 learning_rate: 0.0012750705664730715
BPR MF	0.3358	0.1432	0.3715	dimension: 128 l2_coeff: 0.017941536727080445 n_epochs: 256 n_negatives: 6 learning_rate: 0.0014264826446678315
SLIM ElasticNet	0.3831	0.1792	0.4334	k: 444 λminratio: 3.968133599128073e-5 l1_ratio: 2.500068207067635e-5
Random walk	0.2314	0.09729	0.2665	pixie_walk_length_scaling: false pixie_multi_hit_boosting: false terminate_prob: 0.9 total_walk_length: 712047 min_high_visited_candidates: None high_visited_count_threshold: Inf