Clustering based association rule mining on online stores for optimized cross product recommendation

Riaz, Mohsin; Arooj, Ansif; Hassan, Malik Tahir; Kim, Jeongbae

doi:10.1109/iccais.2014.7020553

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…Furthermore, grocery stores have the potential risk of overstock or understock due to demand uncertainty (Roy et al, 2018). To overcome these circumstances, grocery stores have adopted data mining techniques, such as association rule mining and sequential association rule mining, to provide personalized recommendations and to forecast the demand for products (Bala, 2012;Hipp et al, 2000;Pasquier et al, 1999;Riaz et al, 2014;Shim et al, 2012). However, such data mining techniques cannot generate shopper-specific rules (Yap et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A grocery recommendation for off-line shoppers

Kim

Moon

et al. 2018

OIR

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Purpose Many off-line retailers have experienced a slump in sales and have the potential risk of overstock or understock. To overcome these problems, retailers have applied data mining techniques, such as association rule mining or sequential association rule mining, to increase sales and predict product demand. However, because these techniques cannot generate shopper-centric rules, many off-line shoppers are often inconvenienced after writing their shopping lists carefully and comprehensively. Therefore, the purpose of this paper is to propose a personalized recommendation methodology for off-line grocery shoppers. Design/methodology/approach This paper employs a Markov chain model to generate recommendations for the shopper’s next shopping basket. The proposed methodology is based on the knowledge of both purchased products and purchase sequences. This paper compares the proposed methodology with a traditional collaborative filtering (CF)-based system, a bestseller-based system and a Markov-chain-based system as benchmark systems. Findings The proposed methodology achieves improvements of 15.87, 14.06 and 37.74 percent with respect to the CF-, Markov chain-, and best-seller-based benchmark systems, respectively, meaning that not only the purchased products but also the purchase sequences are important elements in the personalization of grocery recommendations. Originality/value Most of the previous studies on this topic have proposed on-line recommendation methodologies. However, because off-line stores collect transaction data from point-of-sale devices, this research proposes a methodology based on purchased products and purchase patterns for off-line grocery recommendations. In practice, this study implies that both purchased products and purchase sequences are viable elements in off-line grocery recommendations.

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