Boolean kernels for collaborative filtering in top-N item recommendation

Polato, Mirko; Aiolli, Fabio

doi:10.1016/j.neucom.2018.01.057

Cited by 23 publications

(9 citation statements)

References 27 publications

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“…Besides its theoretical value, this kernels family hugely improves the interpretability of Boolean kernels as shown in [9], and achieves state-of-the-art performance on both classification tasks [26] and on top-N item recommendation tasks [27].…”

Section: Boolean Kernels For Categorical Datamentioning

confidence: 97%

Propositional Kernels

Polato

Aiolli

2021

Entropy

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The pervasive presence of artificial intelligence (AI) in our everyday life has nourished the pursuit of explainable AI. Since the dawn of AI, logic has been widely used to express, in a human-friendly fashion, the internal process that led an (intelligent) system to deliver a specific output. In this paper, we take a step forward in this direction by introducing a novel family of kernels, called Propositional kernels, that construct feature spaces that are easy to interpret. Specifically, Propositional Kernel functions compute the similarity between two binary vectors in a feature space composed of logical propositions of a fixed form. The Propositional kernel framework improves upon the recent Boolean kernel framework by providing more expressive kernels. In addition to the theoretical definitions, we also provide an algorithm (and the source code) to efficiently construct any propositional kernel. An extensive empirical evaluation shows the effectiveness of Propositional kernels on several artificial and benchmark categorical data sets.

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Section: Boolean Kernels For Categorical Datamentioning

confidence: 97%

Propositional Kernels

Polato

Aiolli

2021

Entropy

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“…SVD++ firstly factorizes the user-item rating matrix with implicit feedback [20] and is followed by lots of techniques for recommender systems [13,29,39]. Since user-item interactions on many recommender systems are based on implicit feedback (e.g., view, click) rather explicit ratings, many approaches are proposed on the basis of implicit feedback [3,13,16,17,19,28]. UCF with implicit feedback is usually treated as top-N recommendation task [22], which offers a short ranked list of items to the potential users.…”

Section: User-based Collaborative Filtering Modelsmentioning

confidence: 99%

Deep Item-based Collaborative Filtering for Top-N Recommendation

Xue

Wang

et al. 2019

ACM Trans. Inf. Syst.

278

126

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Item-based Collaborative Filtering (short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users.In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g., similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.

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“…Thus, the server, starting from the linear kernel, can easily compute any dot-product polynomial. Dot-product kernel is a big family of kernels that contains many of the most used kernels, such as the RBF kernel, the polynomial kernel, and the boolean kernels [12].…”

Section: Computing Dot-product Kernelsmentioning

confidence: 99%

Privacy-Preserving Kernel Computation For Vertically Partitioned Data

Polato¹,

Gallinaro²,

Aiolli³

2021

ESANN 2021 Proceedings

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In this paper, we propose a secure and privacy-preserving technique for computing dot-product kernels on vertically distributed data. Our proposal is based on secure multi-party computation which provides theoretical guarantees on both security and privacy. We also provide a practical application of the method by adapting a kernel-based collaborative filtering technique to the federated setting. An extensive experimental evaluation shows the effectiveness of the proposed approach.

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Boolean kernels for collaborative filtering in top-N item recommendation

Cited by 23 publications

References 27 publications

Propositional Kernels

Propositional Kernels

Deep Item-based Collaborative Filtering for Top-N Recommendation

Privacy-Preserving Kernel Computation For Vertically Partitioned Data

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