Explainable Matrix Factorization for Collaborative Filtering

Abdollahi, Behnoush; Nasraoui, Olfa

doi:10.1145/2872518.2889405

Cited by 81 publications

(64 citation statements)

References 5 publications

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“…The similarity-based methods [1,2] present explanations as a list of most similar users or items. For example, Behnoush et al [1] used Restricted Boltzmann Machines to compute the explainability scores of the items in the top-K recommendation list. While the similarity-based explanation can serve as a generic solution for explaining a CF recommender, the drawback is that it lacks concrete reasoning.…”

Section: Related Workmentioning

confidence: 99%

Tem

Wang

Feng

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

166

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While collaborative filtering is the dominant technique in personalized recommendation, it models user-item interactions only and cannot provide concrete reasons for a recommendation. Meanwhile, the rich side information affiliated with user-item interactions (e.g., user demographics and item attributes), which provide valuable evidence that why a recommendation is suitable for a user, has not been fully explored in providing explanations.On the technical side, embedding-based methods, such as Wide&Deep and neural factorization machines, provide state-ofthe-art recommendation performance. However, they work like a black-box, for which the reasons underlying a prediction cannot be explicitly presented. On the other hand, tree-based methods like decision trees predict by inferring decision rules from data. While being explainable, they cannot generalize to unseen feature interactions thus fail in collaborative filtering applications.In this work, we propose a novel solution named Tree-enhanced Embedding Method that combines the strengths of embeddingbased and tree-based models. We first employ a tree-based model to learn explicit decision rules (aka. cross features) from the rich side information. We next design an embedding model that can incorporate explicit cross features and generalize to unseen cross features on user ID and item ID. At the core of our embedding method is an easy-to-interpret attention network, making the recommendation process fully transparent and explainable. We conduct experiments on two datasets of tourist attraction and restaurant recommendation, demonstrating the superior performance and explainability of our solution. CCS CONCEPTS• Information systems → Recommender systems; KEYWORDS Explainable Recommendation, Tree-based Model, Embedding-based Model, Neural Attention Network * Xiangnan He is the corresponding author. This paper is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

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Section: Related Workmentioning

confidence: 99%

Tem

Wang

Feng

et al. 2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

166

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“…Collaborative methods can be divided into two models: the neighbourhood-based model (NBM) (Alqadah et al 2015;Xiaojun 2017) and the latent factor model (LFM) (Langseth and Nielsen 2015). Some of the most successful realizations of LFMs are based on matrix factorization (MF) (Yu et al 2017;Abdollahi and Nasraoui 2016;Bokde et al 2015). However, pure latent factor models suffer from several problems, such as poor prediction, sparsity, scalability, etc.…”

Section: Introductionmentioning

confidence: 99%

Restoring latent factors against negative transfer using partial-adaptation nonnegative matrix factorization

Zhang

2019

CCF Trans. Pervasive Comp. Interact.

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Collaborative filtering usually suffers from limited performance due to a data sparsity problem. Transfer learning presents an unprecedented opportunity to alleviate this issue through the transfer of useful knowledge from an auxiliary domain to a target domain. However, the situation becomes complicated when the source and target domain share partial knowledge with each other. Transferring the unshared part across domains will cause negative transfer and may degrade the prediction accuracy in the target domain. To address this issue, in this paper, we present a novel model that exploits the latent factors in the target domain against the negative transfer. First, we transfer rating patterns from the source domain to approximate and reconstruct the target rating matrix. Second, to be specific, we propose a partial-adaptation nonnegative matrix factorization method to correct the transfer learning result and restore latent factors in the target. The final experiments completed on real world datasets demonstrate that our proposed approach effectively addresses the negative transfer and significantly outperforms the state-of-art transfer-learning model.

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“…where R is the set of user-item pairs for which the ratings are available, 1 2 (||p u || 2 +||q i || 2 ) is an L2 regularization term weighted by the coefficient β, and λ is an explainability regularization coefficient that controls the smoothness of the new representation and trade-off between explainability and accuracy [82,83]. The idea here is that if item i is explainable for user u, meaning W u,i > θ, then their representations in the latent domain (q i and p u ) should be close to each other, or p u − q i is close to zero, in order for the objective function to be minimized.…”

Section: Explainable Matrix Factorizationmentioning

confidence: 99%

Accurate and justifiable : new algorithms for explainable recommendations.

Abdollahi¹

Self Cite

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Studies for providing me the opportunity to pursue my degree and for their unfailing support and assistance throughout my graduate studies. I would also like to thank the Kentucky Science and Engineering Foundation for partially providing the funding for the work. My sincere thanks goes to my fellow lab-mates, with a special mention to Wenlong Sun and Gopi Chand Nutakki. Our conversations on research and their good-hearted support and friendship will not be forgotten. Last but by no means least, I am so grateful to my family and friends who have supported me in every possible way unconditionally and selflessly. Many thanks to my sister Behnaz Abdollahi for encouraging me and expressing confidence in my abilities when I could only do the opposite. Moreover, thanks to Milad Nikoukar for motivating me to keep reaching for excellence. iii

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Explainable Matrix Factorization for Collaborative Filtering

Cited by 81 publications

References 5 publications

Tem

Tem

Restoring latent factors against negative transfer using partial-adaptation nonnegative matrix factorization

Accurate and justifiable : new algorithms for explainable recommendations.

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