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| Content Provider | PubMed Central |
|---|---|
| Author | Liu, Jin-hu Zhou, Tao Zhang, Zi-ke Yang, Zimo Liu, Chuang Li, Wei-min |
| Editor | Amaral, Luís A. Nunes |
| Copyright Year | 2014 |
| Abstract | As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, the so-called item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. Interestingly, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely, these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs. |
| Related Links | http://dx.doi.org/10.1371/journal.pone.0113457 |
| Starting Page | 113457 |
| File Format | |
| ISSN | 19326203 |
| e-ISSN | 19326203 |
| Journal | PLoS ONE |
| Issue Number | 12 |
| Volume Number | 9 |
| Language | English |
| Publisher | Public Library of Science |
| Publisher Date | 2014-12-01 |
| Access Restriction | Open |
| Rights Holder | Public Library of Science |
| Subject Keyword | Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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