title: Promoting cold-start items in recommender systems.
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.
authors.affiliation: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
authors.forename: Jin-Hu
authors.initials: JH
authors.lastname: Liu
authors.affiliation: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhou
authors.affiliation: Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, People's Republic of China.
authors.forename: Zi-Ke
authors.initials: ZK
authors.lastname: Zhang
authors.affiliation: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China; Alibaba Group, Hangzhou, People's Republic of China.
authors.forename: Zimo
authors.initials: Z
authors.lastname: Yang
authors.affiliation: Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, People's Republic of China.
authors.forename: Chuang
authors.initials: C
authors.lastname: Liu
authors.affiliation: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China; Beijing Baifendian Information Technology Co., Ltd., Beijing, People's Republic of China.
authors.forename: Wei-Min
authors.initials: WM
authors.lastname: Li
mesh_terms: D000465:Algorithms; D003201:Computers; D003627:Data Interpretation, Statistical; D006293:Health Promotion / Q000191:economics*; D006801:Humans; D020407:Internet; D040541:Marketing; D012984:Software