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Neural Computing and Applications

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01.10.2014 | Original Article

Increasing recommended effectiveness with markov chains and purchase intervals

verfasst von: Wanrong Gu, Shoubin Dong, Zhizhao Zeng

Erschienen in: Neural Computing and Applications | Ausgabe 5/2014

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Abstract

Recommendation system is an important component of many websites and has brought huge economic benefits and challenges for online shoppers and e-commerce companies. Existing recommendation systems focus on producing a list of products which users may be interested to purchase, while overlooking the purchase chain and temporal diversity which may increase the likelihood of a purchase decision. In this paper, we propose to utilize the Markov chain to track the chain of users’ purchase behaviors and utilize the purchase intervals to improve the temporal diversity for e-commerce recommender. We design and implement several algorithms and integrate these into our recommendation model. We evaluate our system on a real-world e-commerce dataset. Experimental results demonstrate that our approach significantly improves the accuracy, conversion rate and temporal diversity compared to the state-of-the-art algorithms.

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Titel: Increasing recommended effectiveness with markov chains and purchase intervals
verfasst von: Wanrong Gu
Shoubin Dong
Zhizhao Zeng
Publikationsdatum: 01.10.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1599-8

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