Closed-Form Models for Collaborative Filtering with Side-Information

Jeunen, Olivier; Balen, Jan Van; Goethals, Bart

doi:10.1145/3383313.3418480

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…(ii) Recently, linear item-item models [23,[45][46][47] have shown competitive performance in conventional RS. Motivated by their success, we devise linear item models to build two probability matrices, item transition and item teleportation matrices, to formulate the stochastic process of RWR.…”

Section: Intra-session Relationship Inter-session Relationship Sessio...mentioning

confidence: 99%

S-Walk

Choi

Kim

Lee

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user. Most existing SR models focus only on modeling intra-session characteristics but pay less attention to inter-session relationships of items, which has the potential to improve accuracy. Another critical aspect of recommender systems is computational efficiency and scalability, considering practical feasibility in commercial applications. To account for both accuracy and scalability, we propose a novel session-based recommendation with a random walk, namely S-Walk. Precisely, S-Walk effectively captures intra-and inter-session correlations by handling high-order relationships among items using random walks with restart (RWR). By adopting linear models with closed-form solutions for transition and teleportation matrices that constitute RWR, S-Walk is highly efficient and scalable. Extensive experiments demonstrate that S-Walk achieves comparable or state-of-the-art performance in various metrics on four benchmark datasets. Moreover, the model learned by S-Walk can be highly compressed without sacrificing accuracy, conducting two or more orders of magnitude faster inference than existing DNN-based models, making it suitable for large-scale commercial systems.

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Section: Intra-session Relationship Inter-session Relationship Sessio...mentioning

confidence: 99%

S-Walk

Choi

Kim

Lee

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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show abstract

“…2 𝐹 (which is trivial to optimise for), one can include simple content based matching into the objective. Previous work has demonstrated that this can improve recommendation accuracy, mostly when only very sparse interaction data is available [24].…”

Section: Future Workmentioning

confidence: 99%

Modelling Users with Item Metadata for Explainable and Interactive Recommendation

Pauw¹,

Ruymbeek²,

Goethals³

2022

Preprint

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“…Inspired by the recent successful studies [16,[38][39][40], we reformulate the linear model that captures various characteristics of sessions. Firstly, we devise two linear models focusing on different properties of sessions: (i) Session-aware Linear Item Similarity (SLIS) model aims at better handling session consistency, and (ii) Session-aware Linear Item Transition (SLIT) model focuses more on sequential dependency.…”

Section: Introductionmentioning

confidence: 99%

Session-aware Linear Item-Item Models for Session-based Recommendation

Choi

Kim

Lee

et al. 2021

Proceedings of the Web Conference 2021

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Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e.g., on e-commerce or multimedia streaming services. Specifically, session data exhibits some unique characteristics, i.e., session consistency and sequential dependency over items within the session, repeated item consumption, and session timeliness. In this paper, we propose simple-yet-effective linear models for considering the holistic aspects of the sessions. The comprehensive nature of our models helps improve the quality of session-based recommendation. More importantly, it provides a generalized framework for reflecting different perspectives of session data. Furthermore, since our models can be solved by closed-form solutions, they are highly scalable. Experimental results demonstrate that the proposed linear models show competitive or state-of-the-art performance in various metrics on several real-world datasets.

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Closed-Form Models for Collaborative Filtering with Side-Information

Cited by 8 publications

References 25 publications

S-Walk

S-Walk

Modelling Users with Item Metadata for Explainable and Interactive Recommendation

Session-aware Linear Item-Item Models for Session-based Recommendation

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