{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:53:26Z","timestamp":1775584406722,"version":"3.50.1"},"reference-count":50,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2018,8,7]],"date-time":"2018-08-07T00:00:00Z","timestamp":1533600000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["OIR"],"published-print":{"date-parts":[[2018,8,21]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>This paper employs a Markov chain model to generate recommendations for the shopper\u2019s 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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/oir-04-2016-0104","type":"journal-article","created":{"date-parts":[[2018,8,7]],"date-time":"2018-08-07T04:34:35Z","timestamp":1533616475000},"page":"468-481","source":"Crossref","is-referenced-by-count":8,"title":["A grocery recommendation for off-line shoppers"],"prefix":"10.1108","volume":"42","author":[{"given":"Jae Kyeong","family":"Kim","sequence":"first","affiliation":[]},{"given":"Hyun Sil","family":"Moon","sequence":"additional","affiliation":[]},{"given":"Byong Ju","family":"An","sequence":"additional","affiliation":[]},{"given":"Il Young","family":"Choi","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2018,8,7]]},"reference":[{"issue":"1","key":"key2021041509445652400_ref001","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1108\/17465661211208794","article-title":"Improving inventory performance with clustering based demand forecasts","volume":"7","year":"2012","journal-title":"Journal of Modelling in Management"},{"issue":"4","key":"key2021041509445652400_ref002","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1108\/00070709510085648","article-title":"Involvement in a routine food shopping context","volume":"97","year":"1995","journal-title":"British Food Journal"},{"issue":"4","key":"key2021041509445652400_ref003","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1016\/j.aei.2015.04.005","article-title":"Applying artificial immune systems to collaborative filtering for movie recommendation\u201d","volume":"29","year":"2015","journal-title":"Advanced Engineering Informatics"},{"issue":"3","key":"key2021041509445652400_ref004","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.ijinfomgt.2016.01.005","article-title":"Collaborative filtering with facial expressions for online video recommendation","volume":"36","year":"2016","journal-title":"International Journal of Information Management"},{"issue":"4","key":"key2021041509445652400_ref005","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1108\/OIR-08-2013-0187","article-title":"Social influence in group recommender systems","volume":"38","year":"2014","journal-title":"Online Information Review"},{"issue":"4","key":"key2021041509445652400_ref006","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1108\/03090569410061196","article-title":"\u201cDecision making and habit in shopping times","volume":"28","year":"1994","journal-title":"European Journal of Marketing"},{"key":"key2021041509445652400_ref007","first-page":"2","article-title":"Web path recommendations based on page ranking and Markov models","year":"2005"},{"key":"key2021041509445652400_ref008","unstructured":"Food Marketing Institute (2015), \u201cUS grocery shopper trends 2014 overview\u201d, available at: www.fmi.org\/docs\/default-source\/research\/presentation.pdf?sfvrsn=0 (accessed June 25, 2015)."},{"key":"key2021041509445652400_ref009","doi-asserted-by":"crossref","unstructured":"Gedikli, F. and Jannach, D. 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