Collaborative Filtering for Implicit Feedback Datasets

By Yifan Hu, Yehuda Koren, C. Volinsky. 2008

3,563 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Marketing, Recommender Systems

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Citations in the corpus

Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016, background): “• WR-MF: A state-of-the-art point-wise MF model for implicit feedback proposed by [13].”
Collaborative Deep Learning for Recommender Systems (2014, methodology): “,RiJ) T is a column vector containing all the ratings of user i, and Cij reflects the confidence controlled by a and b as discussed in [13].”
Variational Autoencoders for Collaborative Filtering (2018, methodology): “ed approaches in recommender systems. Collaborative filtering predicts what items a user will prefer by discovering and exploiting the similarity patterns across users and items. Latent factor models [13, 19, 38] still largely dominate the collaborative filtering research literature due to their sim”
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015, methodology): “Finally, we found that pairwise methods indeed out-perform point-wise methods (WRMF in our case) on our datasets, consistent with our analysis in Related Work.”
Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems (2012, methodology): “For u i , v j , s m , maximization follows similar to matrix factorization (Hu et al., 2008).”
On the challenges of learning with inference networks on sparse, high-dimensional data (2017, methodology): “With them, the same generative model , outperforms CDAE and WMF on both datasets, and marginally outperforms SLIM on ML-20M while achieving nearly state-of-the-art re-sults on Netﬂix.”
Neural Collaborative Filtering (2017, methodology): “Since eALS shows superior performance over the uniform-weighting method WMF [19], we do not further report WMF’s performance.”
A Neural Autoregressive Approach to Collaborative Filtering (2016, background): “however, explicit feedback is not always available or as common as implicit feedback (watch, search, browse behaviors) in real-world recommender systems (Hu et al., 2008).”
On the Effectiveness of Linear Models for One-Class Collaborative Filtering (2016, methodology): “As an intermediate between the two extreme weighting schemes above, the WRMF method (Pan et al. 2008) sets J ui to be (5) where α assigns an importance weight to the observed ratings.”
Gaussian Ranking by Matrix Factorization (2015, background): “There has been a large body of work on training collaborative ﬁltering approaches as to maximize various ranking metrics, see eg [5, 8, 17, 2, 18, 10, 20, 21, 14, 13, 12].”
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “between user preference and our condence in an observation [26].”
Recommendations as Treatments: Debiasing Learning and Evaluation (2016, methodology): “Related but different from the problem we consider is recommendation from positive feedback alone (Hu et al., 2008; Liang et al., 2016).”
Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016, methodology): “We consider the implicit feedback [26, 27] setting, where we only observe whether a person has viewed or liked an item and do not observe explicit ratings. r ij = 1 if user i liked item j and 0 otherwise.”
BPR: Bayesian Personalized Ranking from Implicit Feedback (2009, methodology): “Machine learning approaches for item recommenders [5, 10] typically create the training data from S by giving pairs (u, i) ∈ S a positive class label and all other combinations in (U × I) \ S a negative one (see Figure 1).”
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System (2019, methodology): “Specifically, we derive a federated version of the widely used CF method for implicit feedback datasets [6].”
A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems (2017, methodology): “Similar to (Wang, Wang, and Yeung 2015; Wang and Blei 2011), we use recall as another evaluation metric since the rating information is in the form of implicit feedback (Hu, Koren, and Volinsky 2008; Rendle et al. 2009).”
Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis (2018, background): “The explanation can further enable practitioners to pinpoint the cause of unexpected model behaviors and develop effective strategies for enhancing model performance [20].”
Self-Attentive Sequential Recommendation (2018, methodology): “To address the problem, point-wise [4] and pairwise [5] methods are proposed to solve such challenges.”