{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T14:20:11Z","timestamp":1775571611225,"version":"3.50.1"},"reference-count":56,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T00:00:00Z","timestamp":1686528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ahold Delhaize, the China Scholarship Council","award":["20190607154"],"award-info":[{"award-number":["20190607154"]}]},{"name":"Hybrid Intelligence Center, a 10-year program"},{"name":"Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>\n            Recommender systems have become an essential instrument to connect people to the items that they need. Online grocery shopping is one scenario where this is very clear. So-called\n            <jats:italic>user-centered<\/jats:italic>\n            recommendations take a user as input and suggest items based on the user\u2019s preferences. Such user-centered recommendations have received significant attention and uptake. Instead, we focus on an\n            <jats:italic>item-centered<\/jats:italic>\n            recommendation task, again in the grocery-shopping scenario. In the\n            <jats:italic>reverse next-period recommendation<\/jats:italic>\n            (\n            <jats:bold>RNPR<\/jats:bold>\n            ) task, we are given an item and have to identify potential users who would like to consume it in the next period.\n          <\/jats:p>\n          <jats:p>\n            We consider three sub-tasks of the overall reverse next-period recommendation (\n            <jats:bold>RNPR<\/jats:bold>\n            ) task, (i) Expl-\n            <jats:bold>RNPR<\/jats:bold>\n            , (ii) Rep-\n            <jats:bold>RNPR<\/jats:bold>\n            , and (iii) Mixed-\n            <jats:bold>RNPR<\/jats:bold>\n            , where we consider different types of target users, i.e., (i) explore users, who are new to a given item, (ii) repeat users, who previously purchased a given item, and (iii) both explore users and repeat users. To address the Expl-\n            <jats:bold>RNPR<\/jats:bold>\n            task, we propose a habit-interest fusion model that employs frequency information to capture the repetition-exploration habits of users and that uses pre-trained item embeddings to model the user\u2019s interests. For the Mixed-\n            <jats:bold>RNPR<\/jats:bold>\n            task, we propose a repetition-exploration user ranking algorithm to decouple the repetition and exploration task and investigate the tradeoff between targeting different types of users for a given item. Furthermore, to reduce the computational cost at inference, we analyze the repetition behavior from both user and item perspectives and then introduce a repetition-based candidate filtering method for each sub-task. We conduct experiments on two public grocery-shopping datasets. Our experimental results not only demonstrate the difference between repetition and exploration, but also the effectiveness of the proposed methods.\n          <\/jats:p>","DOI":"10.1145\/3595384","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T12:14:13Z","timestamp":1683288853000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Who Will Purchase This Item Next? Reverse Next Period Recommendation in Grocery Shopping"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7430-4961","authenticated-orcid":false,"given":"Ming","family":"Li","sequence":"first","affiliation":[{"name":"AIRLab, University of Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1113-8094","authenticated-orcid":false,"given":"Mozhdeh","family":"Ariannezhad","sequence":"additional","affiliation":[{"name":"AIRLab, University of Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5970-880X","authenticated-orcid":false,"given":"Andrew","family":"Yates","sequence":"additional","affiliation":[{"name":"University of Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1086-0202","authenticated-orcid":false,"given":"Maarten","family":"De Rijke","sequence":"additional","affiliation":[{"name":"University of Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,6,12]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"273","volume-title":"Proceedings of the 15th ACM Conference on Recommender Systems","author":"Amagata Daichi","year":"2021","unstructured":"Daichi Amagata and Takahiro Hara. 2021. 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