Sequence-aware item recommendations for multiply repeated user-item interactions Article Swipe
Juan Pablo Equihua
,
Maged Ali
,
Henrik Nordmark
,
Berthold Lausen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.00578
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.00578
Recommender systems are one of the most successful applications of machine learning and data science. They are successful in a wide variety of application domains, including e-commerce, media streaming content, email marketing, and virtually every industry where personalisation facilitates better user experience or boosts sales and customer engagement. The main goal of these systems is to analyse past user behaviour to predict which items are of most interest to users. They are typically built with the use of matrix-completion techniques such as collaborative filtering or matrix factorisation. However, although these approaches have achieved tremendous success in numerous real-world applications, their effectiveness is still limited when users might interact multiple times with the same items, or when user preferences change over time. We were inspired by the approach that Natural Language Processing techniques take to compress, process, and analyse sequences of text. We designed a recommender system that induces the temporal dimension in the task of item recommendation and considers sequences of item interactions for each user in order to make recommendations. This method is empirically shown to give highly accurate predictions of user-items interactions for all users in a retail environment, without explicit feedback, besides increasing total sales by 5% and individual customer expenditure by over 50% in an A/B live test.
Related Topics
Concepts
Computer science
Recommender system
Collaborative filtering
Personalization
Process (computing)
Task (project management)
Variety (cybernetics)
Identification (biology)
Matrix decomposition
Dimension (graph theory)
Order (exchange)
Information retrieval
World Wide Web
Artificial intelligence
Botany
Biology
Mathematics
Operating system
Physics
Finance
Pure mathematics
Eigenvalues and eigenvectors
Quantum mechanics
Economics
Management
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2304.00578
- https://arxiv.org/pdf/2304.00578
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362597671
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4362597671Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2304.00578Digital Object Identifier
- Title
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Sequence-aware item recommendations for multiply repeated user-item interactionsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-02Full publication date if available
- Authors
-
Juan Pablo Equihua, Maged Ali, Henrik Nordmark, Berthold LausenList of authors in order
- Landing page
-
https://arxiv.org/abs/2304.00578Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2304.00578Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2304.00578Direct OA link when available
- Concepts
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Computer science, Recommender system, Collaborative filtering, Personalization, Process (computing), Task (project management), Variety (cybernetics), Identification (biology), Matrix decomposition, Dimension (graph theory), Order (exchange), Information retrieval, World Wide Web, Artificial intelligence, Botany, Biology, Mathematics, Operating system, Physics, Finance, Pure mathematics, Eigenvalues and eigenvectors, Quantum mechanics, Economics, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.such | 80 |
| abstract_inverted_index.take | 132 |
| abstract_inverted_index.task | 153 |
| abstract_inverted_index.that | 127, 146 |
| abstract_inverted_index.user | 40, 58, 116, 165 |
| abstract_inverted_index.were | 122 |
| abstract_inverted_index.when | 104, 115 |
| abstract_inverted_index.wide | 20 |
| abstract_inverted_index.with | 74, 110 |
| abstract_inverted_index.built | 73 |
| abstract_inverted_index.email | 30 |
| abstract_inverted_index.every | 34 |
| abstract_inverted_index.items | 63 |
| abstract_inverted_index.media | 27 |
| abstract_inverted_index.might | 106 |
| abstract_inverted_index.order | 167 |
| abstract_inverted_index.sales | 44, 197 |
| abstract_inverted_index.shown | 175 |
| abstract_inverted_index.still | 102 |
| abstract_inverted_index.test. | 211 |
| abstract_inverted_index.text. | 140 |
| abstract_inverted_index.their | 99 |
| abstract_inverted_index.these | 52, 89 |
| abstract_inverted_index.time. | 120 |
| abstract_inverted_index.times | 109 |
| abstract_inverted_index.total | 196 |
| abstract_inverted_index.users | 105, 186 |
| abstract_inverted_index.where | 36 |
| abstract_inverted_index.which | 62 |
| abstract_inverted_index.better | 39 |
| abstract_inverted_index.boosts | 43 |
| abstract_inverted_index.change | 118 |
| abstract_inverted_index.highly | 178 |
| abstract_inverted_index.items, | 113 |
| abstract_inverted_index.matrix | 85 |
| abstract_inverted_index.method | 172 |
| abstract_inverted_index.retail | 189 |
| abstract_inverted_index.system | 145 |
| abstract_inverted_index.users. | 69 |
| abstract_inverted_index.Natural | 128 |
| abstract_inverted_index.analyse | 56, 137 |
| abstract_inverted_index.besides | 194 |
| abstract_inverted_index.induces | 147 |
| abstract_inverted_index.limited | 103 |
| abstract_inverted_index.machine | 10 |
| abstract_inverted_index.predict | 61 |
| abstract_inverted_index.success | 94 |
| abstract_inverted_index.systems | 1, 53 |
| abstract_inverted_index.variety | 21 |
| abstract_inverted_index.without | 191 |
| abstract_inverted_index.However, | 87 |
| abstract_inverted_index.Language | 129 |
| abstract_inverted_index.accurate | 179 |
| abstract_inverted_index.achieved | 92 |
| abstract_inverted_index.although | 88 |
| abstract_inverted_index.approach | 126 |
| abstract_inverted_index.content, | 29 |
| abstract_inverted_index.customer | 46, 202 |
| abstract_inverted_index.designed | 142 |
| abstract_inverted_index.domains, | 24 |
| abstract_inverted_index.explicit | 192 |
| abstract_inverted_index.industry | 35 |
| abstract_inverted_index.inspired | 123 |
| abstract_inverted_index.interact | 107 |
| abstract_inverted_index.interest | 67 |
| abstract_inverted_index.learning | 11 |
| abstract_inverted_index.multiple | 108 |
| abstract_inverted_index.numerous | 96 |
| abstract_inverted_index.process, | 135 |
| abstract_inverted_index.science. | 14 |
| abstract_inverted_index.temporal | 149 |
| abstract_inverted_index.behaviour | 59 |
| abstract_inverted_index.compress, | 134 |
| abstract_inverted_index.considers | 158 |
| abstract_inverted_index.dimension | 150 |
| abstract_inverted_index.feedback, | 193 |
| abstract_inverted_index.filtering | 83 |
| abstract_inverted_index.including | 25 |
| abstract_inverted_index.sequences | 138, 159 |
| abstract_inverted_index.streaming | 28 |
| abstract_inverted_index.typically | 72 |
| abstract_inverted_index.virtually | 33 |
| abstract_inverted_index.Processing | 130 |
| abstract_inverted_index.approaches | 90 |
| abstract_inverted_index.experience | 41 |
| abstract_inverted_index.increasing | 195 |
| abstract_inverted_index.individual | 201 |
| abstract_inverted_index.marketing, | 31 |
| abstract_inverted_index.real-world | 97 |
| abstract_inverted_index.successful | 7, 17 |
| abstract_inverted_index.techniques | 79, 131 |
| abstract_inverted_index.tremendous | 93 |
| abstract_inverted_index.user-items | 182 |
| abstract_inverted_index.Recommender | 0 |
| abstract_inverted_index.application | 23 |
| abstract_inverted_index.e-commerce, | 26 |
| abstract_inverted_index.empirically | 174 |
| abstract_inverted_index.engagement. | 47 |
| abstract_inverted_index.expenditure | 203 |
| abstract_inverted_index.facilitates | 38 |
| abstract_inverted_index.predictions | 180 |
| abstract_inverted_index.preferences | 117 |
| abstract_inverted_index.recommender | 144 |
| abstract_inverted_index.applications | 8 |
| abstract_inverted_index.environment, | 190 |
| abstract_inverted_index.interactions | 162, 183 |
| abstract_inverted_index.applications, | 98 |
| abstract_inverted_index.collaborative | 82 |
| abstract_inverted_index.effectiveness | 100 |
| abstract_inverted_index.factorisation. | 86 |
| abstract_inverted_index.recommendation | 156 |
| abstract_inverted_index.personalisation | 37 |
| abstract_inverted_index.recommendations. | 170 |
| abstract_inverted_index.matrix-completion | 78 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |