ddoeunn / weighted-regularized-matrix-factorization

Recommender system weighted regularized matrix factorization in python

Weighted-Regularized-Matrix-Factorization

Weighted Regularized Matrix Factorization for Implicit Feedback in Recommender System

• [1] Hu, Yifan, Yehuda Koren, and Chris Volinsky. "Collaborative filtering for implicit feedback datasets." 2008.
• [2] Pan, Rong, et al. "One-class collaborative filtering." 2008.
• [3] He, Xiangnan, et al. "Fast matrix factorization for online recommendation with implicit feedback." 2016.

I implemented WRMF methods in python in reference to Cornac. Only uniform weighting strategy on positive or negative instances is available in WMF model in Cornac. By modifying the WMF code of Cornac, I implemented user-oriented, item-oriented weighting strategy of "One-class collaborative filtering (Pan, Rong, et al.)" and item-popularity weighting strategy of "Fast matrix factorization for online recommendation with implicit feedback (He, Xiangnan, et al)".

See the example comparing weighting strategies.

Data	Strategy	k	Train time (s)	Precision@k	Recall@k	NDCG@k
movielens100k	uniform_pos	10	6.2740	0.292365	0.184272	0.343978
movielens100k	uniform_neg	10	9.2785	0.327253	0.215326	0.383740
movielens100k	user_oriented	10	10.6478	0.366172	0.230124	0.431030
movielens100k	item_oriented	10	9.8481	0.361082	0.229981	0.426998
movielens100k	item_popularity	10	11.0064	0.360551	0.231452	0.423511

from Datasets import Movielens
from Evaluation.data_split import split_data
from Evaluation.ranking_metrics import *
from WRMF.wrmf import *
from WRMF import wrmf_rec

df_movielens = Movielens.load_data()                # load dataset
train, test = split_data(df_movielens,
                         split_strategy="random_by_user",
                         random_state=0)            # split data

wrmf = WRMF(train, weight_strategy="uniform_pos")   # wrmf model
model =  train_cornac(wrmf, train)                  # train model

k = 10
top_k = wrmf_rec.recommend_top_k(model, train, k)   # recommendation
ranking_metrics(top_k, test)                        # evaluation

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Weighted Regularized Matrix Factorization (WRMF)

Basic idea of Weighted Regularized Matrix Factorization (WRMF) is to assign smaller weights to the unobserved instances than the observed. The weights are related to the concept of confidence. As not interacting with an item can result from other reasons than not liking it, negative instances have low confidence. For example, a user might be unaware of the existence of the item, or unable to consume it due to its price or limited availability. Unobserved instances are a mixture of negative and unknown feedback.

Also, interacting with an item can be caused by a variety of reasons that differ from liking it. For example, a user may buy an item as gift for someone else, despite the user does not like the item. Thus it can be thought that there are also different confidence levels among the items that the user interacted with.

Several weighting strategies have been proposed. Read more here! for more details.

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Questions for Implicit Matrix Factorization ("liked" or "hearted" only, no "viewed"):

1. What are the major challenges in making every non-liked item as "0" and liked item as "1"?
2. How should test and evaluation be scored if false positives (spam-like) are worse than false negatives (censorship-like)?
3. How should evaluation sampling be done? subset election of users, or subset selection of user-item pairs?
4. Is Truncated SVD more interpretable than MF?
5. Are PPMI based cosine similarity better or worse? https://github.com/nitsourish/hybrid_recommendation_engine/blob/master/MF_cosine-similarity_CF.ipynb