Avoiding congestion in recommender systems
Recommender systems use the historical activities and personal profiles of users to uncover their preferences and recommend objects. Most of the previous methods are based on objects’ (and/or users’) similarity rather than on their difference. Such approaches are subject to a high risk of increasing...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2014-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/16/6/063057 |
| _version_ | 1797751456729661440 |
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| author | Xiaolong Ren Linyuan Lü Runran Liu Jianlin Zhang |
| author_facet | Xiaolong Ren Linyuan Lü Runran Liu Jianlin Zhang |
| author_sort | Xiaolong Ren |
| collection | DOAJ |
| description | Recommender systems use the historical activities and personal profiles of users to uncover their preferences and recommend objects. Most of the previous methods are based on objects’ (and/or users’) similarity rather than on their difference. Such approaches are subject to a high risk of increasingly exposing users to a narrowing band of popular objects. As a result, a few objects may be recommended to an enormous number of users, resulting in the problem of recommendation congestion, which is to be avoided, especially when the recommended objects are limited resources. In order to quantitatively measure a recommendation algorithmʼs ability to avoid congestion, we proposed a new metric inspired by the Gini index, which is used to measure the inequality of the individual wealth distribution in an economy. Besides this, a new recommendation method called directed weighted conduction (DWC) was developed by considering the heat conduction process on a user–object bipartite network with different thermal conductivities. Experimental results obtained for three benchmark data sets showed that the DWC algorithm can effectively avoid system congestion, and greatly improve the novelty and diversity, while retaining relatively high accuracy, in comparison with the state-of-the-art methods. |
| first_indexed | 2024-03-12T16:48:50Z |
| format | Article |
| id | doaj.art-9f209d057b0f4448bd7c1051cbc4650d |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:48:50Z |
| publishDate | 2014-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-9f209d057b0f4448bd7c1051cbc4650d2023-08-08T11:27:06ZengIOP PublishingNew Journal of Physics1367-26302014-01-0116606305710.1088/1367-2630/16/6/063057Avoiding congestion in recommender systemsXiaolong Ren0Linyuan Lü1Runran Liu2Jianlin Zhang3Alibaba Research Center for Complexity Sciences, Hangzhou Normal University , 311121 Hangzhou, Peopleʼs Republic of China; Department of Physics, University of Fribourg , Chemin du Musée 3, Fribourg CH-1700, SwitzerlandAlibaba Research Center for Complexity Sciences, Hangzhou Normal University , 311121 Hangzhou, Peopleʼs Republic of ChinaAlibaba Research Center for Complexity Sciences, Hangzhou Normal University , 311121 Hangzhou, Peopleʼs Republic of ChinaAlibaba Research Center for Complexity Sciences, Hangzhou Normal University , 311121 Hangzhou, Peopleʼs Republic of China; Department of Physics, University of Fribourg , Chemin du Musée 3, Fribourg CH-1700, SwitzerlandRecommender systems use the historical activities and personal profiles of users to uncover their preferences and recommend objects. Most of the previous methods are based on objects’ (and/or users’) similarity rather than on their difference. Such approaches are subject to a high risk of increasingly exposing users to a narrowing band of popular objects. As a result, a few objects may be recommended to an enormous number of users, resulting in the problem of recommendation congestion, which is to be avoided, especially when the recommended objects are limited resources. In order to quantitatively measure a recommendation algorithmʼs ability to avoid congestion, we proposed a new metric inspired by the Gini index, which is used to measure the inequality of the individual wealth distribution in an economy. Besides this, a new recommendation method called directed weighted conduction (DWC) was developed by considering the heat conduction process on a user–object bipartite network with different thermal conductivities. Experimental results obtained for three benchmark data sets showed that the DWC algorithm can effectively avoid system congestion, and greatly improve the novelty and diversity, while retaining relatively high accuracy, in comparison with the state-of-the-art methods.https://doi.org/10.1088/1367-2630/16/6/063057diffusionheat conductioninformation filteringbipartite networkcongestionrecommender systems |
| spellingShingle | Xiaolong Ren Linyuan Lü Runran Liu Jianlin Zhang Avoiding congestion in recommender systems New Journal of Physics diffusion heat conduction information filtering bipartite network congestion recommender systems |
| title | Avoiding congestion in recommender systems |
| title_full | Avoiding congestion in recommender systems |
| title_fullStr | Avoiding congestion in recommender systems |
| title_full_unstemmed | Avoiding congestion in recommender systems |
| title_short | Avoiding congestion in recommender systems |
| title_sort | avoiding congestion in recommender systems |
| topic | diffusion heat conduction information filtering bipartite network congestion recommender systems |
| url | https://doi.org/10.1088/1367-2630/16/6/063057 |
| work_keys_str_mv | AT xiaolongren avoidingcongestioninrecommendersystems AT linyuanlu avoidingcongestioninrecommendersystems AT runranliu avoidingcongestioninrecommendersystems AT jianlinzhang avoidingcongestioninrecommendersystems |