Cross-channel Recommendation for Multi-channel Retail
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913434936803328 |
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| author | Choi, Yijin Shin, Jongkyung Lim, Chiehyeon |
| author_facet | Choi, Yijin Shin, Jongkyung Lim, Chiehyeon |
| contents | An increasing number of retailers are expanding their channels to the offline and online domains, transforming them into multi-channel retailers. This transition emphasizes the need for cross-channel recommendations. Given that each retail channel represents a separate domain with a unique context, this can be regarded as a cross-domain recommendation (CDR). However, existing studies on CDR did not address the scenarios where both users and items partially overlap across multi-retail channels which we define as "cross-channel retail recommendation (CCRR)". This paper introduces our original work on CCRR using a real-world dataset from a multi-channel retail store. Specifically, we study significant challenges in integrating user preferences across both channels and propose a novel model for CCRR using a channel-wise attention mechanism. We empirically validate our model's superiority in addressing CCRR over existing models. Finally, we offer implications for future research on CCRR, delving into our experiment results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00972 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-channel Recommendation for Multi-channel Retail Choi, Yijin Shin, Jongkyung Lim, Chiehyeon Information Retrieval An increasing number of retailers are expanding their channels to the offline and online domains, transforming them into multi-channel retailers. This transition emphasizes the need for cross-channel recommendations. Given that each retail channel represents a separate domain with a unique context, this can be regarded as a cross-domain recommendation (CDR). However, existing studies on CDR did not address the scenarios where both users and items partially overlap across multi-retail channels which we define as "cross-channel retail recommendation (CCRR)". This paper introduces our original work on CCRR using a real-world dataset from a multi-channel retail store. Specifically, we study significant challenges in integrating user preferences across both channels and propose a novel model for CCRR using a channel-wise attention mechanism. We empirically validate our model's superiority in addressing CCRR over existing models. Finally, we offer implications for future research on CCRR, delving into our experiment results. |
| title | Cross-channel Recommendation for Multi-channel Retail |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2404.00972 |