Co-pairwise ranking model for item recommendation
Most of existing recommendation models constructed pairwise samples only from a user’s perspective.Nevertheless,they overlooked the functional relationships among items--A key factor that could significantly influence user purchase decision-making process.To this end,a co-pairwise ranking model was...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019137/ |
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| _version_ | 1841539349977497600 |
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| author | Bin WU Yun CHEN Zhongchuan SUN Yangdong YE |
| author_facet | Bin WU Yun CHEN Zhongchuan SUN Yangdong YE |
| author_sort | Bin WU |
| collection | DOAJ |
| description | Most of existing recommendation models constructed pairwise samples only from a user’s perspective.Nevertheless,they overlooked the functional relationships among items--A key factor that could significantly influence user purchase decision-making process.To this end,a co-pairwise ranking model was proposed,which modeled a user’s preference for a given item as the combination of user-item interactions and item-item complementarity relationships.Considering that the rank position of positive sample and the negative sampler had a direct impact on the rate of convergence,a rank-aware learning algorithm was devised for optimizing the proposed model.Extensive experiments on four real-word datasets are conducted to evaluate of the proposed model.The experimental results demonstrate that the devised algorithm significantly outperforms a series of state-of-the-art recommendation algorithms in terms of multiple evaluation metrics. |
| format | Article |
| id | doaj-art-5fb1c2e3f6bf48d2a1c7180abae41f49 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-5fb1c2e3f6bf48d2a1c7180abae41f492025-01-14T07:17:49ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-09-014019320659729906Co-pairwise ranking model for item recommendationBin WUYun CHENZhongchuan SUNYangdong YEMost of existing recommendation models constructed pairwise samples only from a user’s perspective.Nevertheless,they overlooked the functional relationships among items--A key factor that could significantly influence user purchase decision-making process.To this end,a co-pairwise ranking model was proposed,which modeled a user’s preference for a given item as the combination of user-item interactions and item-item complementarity relationships.Considering that the rank position of positive sample and the negative sampler had a direct impact on the rate of convergence,a rank-aware learning algorithm was devised for optimizing the proposed model.Extensive experiments on four real-word datasets are conducted to evaluate of the proposed model.The experimental results demonstrate that the devised algorithm significantly outperforms a series of state-of-the-art recommendation algorithms in terms of multiple evaluation metrics.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019137/item recommendationpairwise rankingcollaborative filteringimplicit feedbackmatrix factorization |
| spellingShingle | Bin WU Yun CHEN Zhongchuan SUN Yangdong YE Co-pairwise ranking model for item recommendation Tongxin xuebao item recommendation pairwise ranking collaborative filtering implicit feedback matrix factorization |
| title | Co-pairwise ranking model for item recommendation |
| title_full | Co-pairwise ranking model for item recommendation |
| title_fullStr | Co-pairwise ranking model for item recommendation |
| title_full_unstemmed | Co-pairwise ranking model for item recommendation |
| title_short | Co-pairwise ranking model for item recommendation |
| title_sort | co pairwise ranking model for item recommendation |
| topic | item recommendation pairwise ranking collaborative filtering implicit feedback matrix factorization |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019137/ |
| work_keys_str_mv | AT binwu copairwiserankingmodelforitemrecommendation AT yunchen copairwiserankingmodelforitemrecommendation AT zhongchuansun copairwiserankingmodelforitemrecommendation AT yangdongye copairwiserankingmodelforitemrecommendation |