Combination of individual and group patterns for time-sensitive purchase recommendation

Lysenko, Anton; Shikov, Egor; Bochenina, Klavdiya

doi:10.1007/s41060-020-00233-1

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…By considering the spatial and categorical characteristics of POIs, the system enhances the relevance and quality of recommendations for users. Lysenko et al [78] propose a generative model of client spending based on the temporal point processes framework. This model predicts the expected time and category of a client's next purchase, providing valuable insights for personalized recommendations and marketing strategies in the context of customer behavior prediction.…”

Section: Characterized Application Areas Of Recommendationmentioning

confidence: 99%

Data science for next-generation recommender systems

Wang

Sivrikaya

et al. 2023

Int J Data Sci Anal

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Data science has been the foundation of recommender systems for a long time. Over the past few decades, various recommender systems have been developed using different data science and machine learning methodologies and techniques. However, no existing work systematically discusses the significant relationships between data science and recommender systems. To bridge this gap, this paper aims to systematically investigate recommender systems from the perspective of data science. Firstly, we introduce the various types of data used for recommendations and the corresponding machine learning models and methods that effectively represent each type. Next, we provide a brief outline of the representative data science and machine learning models utilized in building recommender systems. Subsequently, we share some preliminary thoughts on next-generation recommender systems. Finally, we summarize this special issue on data science for next-generation recommender systems.

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Section: Characterized Application Areas Of Recommendationmentioning

confidence: 99%