Improving recommendation by connecting user behavior in temporal and topological dimensions
The rapid development of Internet provides many high quality data sets for understanding human online behavior patterns. Many analysis tools from complexity science such as complex networks and human dynamics have been applied to study human online activities. However, in most existing works, the to...
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| Vydáno v: | Physica A Ročník 585; s. 126378 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.01.2022
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| ISSN: | 0378-4371, 1873-2119 |
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| Abstract | The rapid development of Internet provides many high quality data sets for understanding human online behavior patterns. Many analysis tools from complexity science such as complex networks and human dynamics have been applied to study human online activities. However, in most existing works, the topological and temporal features of human online activities are analyzed independently. In this paper, we connect these two dimensions by investigating the relations between online users inter-event time and the network distance between the items selected by users. We find that users will choose items with longer network distance when the inter-event time is long, and vice versa. This finding is then applied to improve the recommendation process where recommendation diversity should be given more weight when users return from a long inactive period. Finally, we use a trade-off between accuracy and complexity to further improve the recommendation algorithms. |
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| AbstractList | The rapid development of Internet provides many high quality data sets for understanding human online behavior patterns. Many analysis tools from complexity science such as complex networks and human dynamics have been applied to study human online activities. However, in most existing works, the topological and temporal features of human online activities are analyzed independently. In this paper, we connect these two dimensions by investigating the relations between online users inter-event time and the network distance between the items selected by users. We find that users will choose items with longer network distance when the inter-event time is long, and vice versa. This finding is then applied to improve the recommendation process where recommendation diversity should be given more weight when users return from a long inactive period. Finally, we use a trade-off between accuracy and complexity to further improve the recommendation algorithms. |
| ArticleNumber | 126378 |
| Author | Li, Heyang Zeng, An |
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| Cites_doi | 10.1145/1345448.1345465 10.1145/1935826.1935863 10.1126/science.1144581 10.1109/TKDE.2015.2508816 10.1103/RevModPhys.74.47 10.1109/JPROC.2012.2189529 10.1038/445489a 10.1016/j.physrep.2005.10.009 10.1103/PhysRevE.71.056103 10.1038/nature03248 10.1016/j.physrep.2012.02.006 10.1016/j.physrep.2017.10.001 10.1016/j.eswa.2016.09.040 10.1142/S0219525913500240 10.1038/nature03459 10.1103/PhysRevLett.87.278701 10.1016/j.physa.2018.08.014 10.1038/s41467-020-16088-w 10.1111/j.1469-8137.1912.tb05611.x 10.1038/s41586-020-2909-1 10.1137/070710111 10.1103/PhysRevE.76.046115 10.1038/30918 10.1145/1645953.1646225 10.1140/epjb/e2009-00335-8 10.1038/s41598-018-24456-2 10.1038/nature06958 10.1137/S003614450342480 10.1038/ncomms9166 10.1088/1742-5468/2008/10/P10008 10.1103/PhysRevE.73.036127 |
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| Snippet | The rapid development of Internet provides many high quality data sets for understanding human online behavior patterns. Many analysis tools from complexity... |
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| Title | Improving recommendation by connecting user behavior in temporal and topological dimensions |
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