Towards a Better Understanding of Linear Models for Recommendation

Jin, Ruoming; Dong, Li; Jing, Gao; Li, Zhi; Chen, Li; Zhou, Yang

doi:10.1145/3447548.3467428

Cited by 21 publications

(12 citation statements)

References 22 publications

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“…The benchmarks that we pick have been proposed and studied by multiple research groups [18,9,13,1,6] and other researchers have used them for evaluating newly proposed algorithms [26,14,19,27]. The poor iALS results have been established and reproduced by multiple groups [18,13,9,6,1] including a paper focused on reproducibility [1]. However, contrary to these results, we show that iALS can in fact generate high quality results on exactly the same benchmarks using exactly the same evaluation method.…”

Section: Introductionmentioning

confidence: 81%

“…Recall@20 Recall@50 NDCG@100 Result from RecVAE [26] 0.414 0.553 0.442 [26] H+Vamp (Gated) [14] 0.413 0.551 0.445 [14] RaCT [19] 0.403 0.543 0.434 [19] Mult-VAE [18] 0.395 0.537 0.426 [18] LambdaNet [4] 0 authors [26,14,27,19] since then. The benchmarks include results for iALS that were produced in [18,13]. We reinvestigate the results on the Movielens 20M (ML20M) and Million Song Data (MSD) benchmarks.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we revisit four wellstudied item recommendation benchmarks where poor prediction quality was reported for iALS. The benchmarks that we pick have been proposed and studied by multiple research groups [18,9,13,1,6] and other researchers have used them for evaluating newly proposed algorithms [26,14,19,27]. The poor iALS results have been established and reproduced by multiple groups [18,13,9,6,1] including a paper focused on reproducibility [1].…”

Section: Introductionmentioning

confidence: 99%

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Revisiting the Performance of iALS on Item Recommendation Benchmarks

Rendle¹,

Krichene²,

Zhang³

et al. 2021

Preprint

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Matrix factorization learned by implicit alternating least squares (iALS) is a popular baseline in recommender system research publications. iALS is known to be one of the most computationally efficient and scalable collaborative filtering methods. However, recent studies suggest that its prediction quality is not competitive with the current state of the art, in particular autoencoders and other item-based collaborative filtering methods. In this work, we revisit the iALS algorithm and present a bag of tricks that we found useful when applying iALS. We revisit four wellstudied benchmarks where iALS was reported to perform poorly and show that with proper tuning, iALS is highly competitive and outperforms any method on at least half of the comparisons. We hope that these high quality results together with iALS's known scalability spark new interest in applying and further improving this decade old technique.

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Section: Introductionmentioning

confidence: 81%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Revisiting the Performance of iALS on Item Recommendation Benchmarks

Rendle¹,

Krichene²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently researchers [12] pointed out that there exists a unified foundation underlying matrix factorization and linear models. We would like to explore relations between linear models and MatMat-based recommender systems as well.…”

Section: Discussionmentioning

confidence: 99%

MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting

Wang¹

2021

2021 7th International Conference on Computer and Communications (ICCC)

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Movie Recommender System is widely applied in commercial environments such as NetFlix and Tubi. Classic recommender models utilize technologies such as collaborative filtering, learning to rank, matrix factorization and deep learning models to achieve lower marketing expenses and higher revenues. However, audience of movies have different ratings of the same movie in different contexts. Important movie watching contexts include audience mood, location, weather, etc. To be able to take advantage of contextual information is of great benefit to recommender builders. However, popular techniques such as tensor factorization consumes an impractical amount of storage, which greatly reduces its feasibility in real world environment. In this paper, we take advantage of the MatMat framework, which factorizes matrices by matrix fitting to build a context-aware movie recommender system that is superior to classic matrix factorization and comparable in the fairness metric.

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“…Factorization Approaches Similar to GAMs, factorization approaches have been studied extensively. Popularized for recommender systems, different (matrix) factorization approaches have been proposed in the early 2000s (see, e.g., Srebro et al, 2004;Adomavicius and Tuzhilin, 2005;Koren et al, 2009) and are still considered state-of-the-art in terms of performance and efficiency (Rendle et al, 2020;Jin et al, 2021). Closely related to matrix factorization are factorization machines (FMs; Rendle, 2010).…”

Section: Related Literaturementioning

confidence: 99%

Additive Higher-Order Factorization Machines

Rügamer¹

2022

Preprint

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In the age of big data and interpretable machine learning, approaches need to work at scale and at the same time allow for a clear mathematical understanding of the method's inner workings. While there exist inherently interpretable semi-parametric regression techniques for large-scale applications to account for non-linearity in the data, their model complexity is still often restricted. One of the main limitations are missing interactions in these models, which are not included for the sake of better interpretability, but also due to untenable computational costs. To address this shortcoming, we derive a scalable high-order tensor product spline model using a factorization approach. Our method allows to include all (higher-order) interactions of non-linear feature effects while having computational costs proportional to a model without interactions. We prove both theoretically and empirically that our methods scales notably better than existing approaches, derive meaningful penalization schemes and also discuss further theoretical aspects. We finally investigate predictive and estimation performance both with synthetic and real data.

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Towards a Better Understanding of Linear Models for Recommendation

Cited by 21 publications

References 22 publications

Revisiting the Performance of iALS on Item Recommendation Benchmarks

Revisiting the Performance of iALS on Item Recommendation Benchmarks

MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting

Additive Higher-Order Factorization Machines

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