Co-purchaser Recommendation for Online Group Buying
Abstract Online group buying is a burgeoning business model of Internet shopping, in which people with the same merchandise interests form a group and co-purchase goods with favorable prices. The buyer who launches the co-purchase is called the initiator, and other buyers are called the co-purchaser...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-08-01
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| Series: | Data Science and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s41019-020-00138-w |
| _version_ | 1819093829776572416 |
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| author | Jihong Chen Wei Chen Jinjing Huang Jinhua Fang Zhixu Li An Liu Lei Zhao |
| author_facet | Jihong Chen Wei Chen Jinjing Huang Jinhua Fang Zhixu Li An Liu Lei Zhao |
| author_sort | Jihong Chen |
| collection | DOAJ |
| description | Abstract Online group buying is a burgeoning business model of Internet shopping, in which people with the same merchandise interests form a group and co-purchase goods with favorable prices. The buyer who launches the co-purchase is called the initiator, and other buyers are called the co-purchasers. Although recommending co-purchasers for a target buyer (co-purchase initiator) on the group buying is an interesting problem, existing studies have paid few attention to this topic. Different from the collaborator recommendation that only considers users with high similarity to the target user, co-purchaser recommendation takes both users with high and weak similarity into account, and the recommendation results can achieve high recall and diversity. However, the task turns out to be a challenging problem since it is hard to make a precise recommendation for buyers with weak similarity. To address the problem, we propose the following two methods. In the first one, we directly impose a penalty to the weak similar co-purchasers in the embedding space. To further improve the recommendation performance, in the second one, we smoothly increase the co-occurrence probability of the weak similar co-purchasers by truncated bias walk. Our experimental results on real datasets show that the proposed methods, particularly the latter, can effectively complete the co-purchaser recommendation and has high recommendation performance. In addition, considering that co-purchase may last longer, the total recommendation result can be generated in multiple stages and adjust the current recommendation list based on the feedback from the recommendation of previous stages. It is a trick for all co-purchaser recommendation methods to make the total result better. |
| first_indexed | 2024-12-21T23:17:44Z |
| format | Article |
| id | doaj.art-b6831b3e99344ea0a8ed14301be6eb39 |
| institution | Directory Open Access Journal |
| issn | 2364-1185 2364-1541 |
| language | English |
| last_indexed | 2024-12-21T23:17:44Z |
| publishDate | 2020-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Data Science and Engineering |
| spelling | doaj.art-b6831b3e99344ea0a8ed14301be6eb392022-12-21T18:46:52ZengSpringerOpenData Science and Engineering2364-11852364-15412020-08-015328029210.1007/s41019-020-00138-wCo-purchaser Recommendation for Online Group BuyingJihong Chen0Wei Chen1Jinjing Huang2Jinhua Fang3Zhixu Li4An Liu5Lei Zhao6School of Computer Science and Technology, Soochow UniversitySchool of Computer Science and Technology, Soochow UniversitySchool of Computer Science and Technology, Soochow UniversitySchool of Computer Science and Technology, Soochow UniversitySchool of Computer Science and Technology, Soochow UniversitySchool of Computer Science and Technology, Soochow UniversitySchool of Computer Science and Technology, Soochow UniversityAbstract Online group buying is a burgeoning business model of Internet shopping, in which people with the same merchandise interests form a group and co-purchase goods with favorable prices. The buyer who launches the co-purchase is called the initiator, and other buyers are called the co-purchasers. Although recommending co-purchasers for a target buyer (co-purchase initiator) on the group buying is an interesting problem, existing studies have paid few attention to this topic. Different from the collaborator recommendation that only considers users with high similarity to the target user, co-purchaser recommendation takes both users with high and weak similarity into account, and the recommendation results can achieve high recall and diversity. However, the task turns out to be a challenging problem since it is hard to make a precise recommendation for buyers with weak similarity. To address the problem, we propose the following two methods. In the first one, we directly impose a penalty to the weak similar co-purchasers in the embedding space. To further improve the recommendation performance, in the second one, we smoothly increase the co-occurrence probability of the weak similar co-purchasers by truncated bias walk. Our experimental results on real datasets show that the proposed methods, particularly the latter, can effectively complete the co-purchaser recommendation and has high recommendation performance. In addition, considering that co-purchase may last longer, the total recommendation result can be generated in multiple stages and adjust the current recommendation list based on the feedback from the recommendation of previous stages. It is a trick for all co-purchaser recommendation methods to make the total result better.https://doi.org/10.1007/s41019-020-00138-wGroup buyingCollaborator recommendationNetwork embeddingTruncated walk |
| spellingShingle | Jihong Chen Wei Chen Jinjing Huang Jinhua Fang Zhixu Li An Liu Lei Zhao Co-purchaser Recommendation for Online Group Buying Data Science and Engineering Group buying Collaborator recommendation Network embedding Truncated walk |
| title | Co-purchaser Recommendation for Online Group Buying |
| title_full | Co-purchaser Recommendation for Online Group Buying |
| title_fullStr | Co-purchaser Recommendation for Online Group Buying |
| title_full_unstemmed | Co-purchaser Recommendation for Online Group Buying |
| title_short | Co-purchaser Recommendation for Online Group Buying |
| title_sort | co purchaser recommendation for online group buying |
| topic | Group buying Collaborator recommendation Network embedding Truncated walk |
| url | https://doi.org/10.1007/s41019-020-00138-w |
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