A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance i...

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Autores principales:	Guisheng Chen, Zhanshan Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	recommendation systems market basket recommendation data mining periodic pattern sequential rule association rule Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/d3a27c93567741589f53465176117f67
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spelling	oai:doaj.org-article:d3a27c93567741589f53465176117f672021-11-25T17:29:36ZA New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation10.3390/e231114301099-4300https://doaj.org/article/d3a27c93567741589f53465176117f672021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1430https://doaj.org/toc/1099-4300Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.Guisheng ChenZhanshan LiMDPI AGarticlerecommendation systemsmarket basket recommendationdata miningperiodic patternsequential ruleassociation ruleScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1430, p 1430 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	recommendation systems market basket recommendation data mining periodic pattern sequential rule association rule Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	recommendation systems market basket recommendation data mining periodic pattern sequential rule association rule Science Q Astrophysics QB460-466 Physics QC1-999 Guisheng Chen Zhanshan Li A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation
description	Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.
format	article
author	Guisheng Chen Zhanshan Li
author_facet	Guisheng Chen Zhanshan Li
author_sort	Guisheng Chen
title	A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation
title_short	A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation
title_full	A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation
title_fullStr	A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation
title_full_unstemmed	A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation
title_sort	new method combining pattern prediction and preference prediction for next basket recommendation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d3a27c93567741589f53465176117f67
work_keys_str_mv	AT guishengchen anewmethodcombiningpatternpredictionandpreferencepredictionfornextbasketrecommendation AT zhanshanli anewmethodcombiningpatternpredictionandpreferencepredictionfornextbasketrecommendation AT guishengchen newmethodcombiningpatternpredictionandpreferencepredictionfornextbasketrecommendation AT zhanshanli newmethodcombiningpatternpredictionandpreferencepredictionfornextbasketrecommendation
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A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

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