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Promoting cold-start items in recommender systems.

Liu JH, Zhou T, Zhang ZK, Yang Z, Liu C, Li WM - PLoS ONE (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

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.

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.

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Related in: MedlinePlus

Degree distributions and degree correlations.All degree distributions are power-law-like.  and  are respectively showed in the 3rd and 4th rows, where red and black lines representing the results from original and reshuffled networks. Results of reshuffled networks are obtained by averaging over five independent realizations.
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pone-0113457-g002: Degree distributions and degree correlations.All degree distributions are power-law-like. and are respectively showed in the 3rd and 4th rows, where red and black lines representing the results from original and reshuffled networks. Results of reshuffled networks are obtained by averaging over five independent realizations.

Mentions: We consider two real data sets with anonymous users in this paper (datasets are free to download as Dataset S1), including (a) Tmall.com (TM): an open business-to-consumer (B2C) platform where enrolled businessmen can sell legal items to customers; (b) Coo8.com (Coo8): a well established online retailer mainly trading in electrical household appliances and a leading supplier to daily necessities. In order to avoid the isolate nodes in the data sets, each user has bought at least one item, and each item has been purchased at least once. Table 1 shows the basic statistics of the two data sets. Due to the different types of products, these networks have much different average item degrees. As shown in Figure 2, all degree distributions are heavy-tailed and the item degree distributions are generally more heterogenous than the corresponding user degree distributions. These observations complement previous empirical analyses on user-item bipartite networks [47]–[50].


Promoting cold-start items in recommender systems.

Liu JH, Zhou T, Zhang ZK, Yang Z, Liu C, Li WM - PLoS ONE (2014)

Degree distributions and degree correlations.All degree distributions are power-law-like.  and  are respectively showed in the 3rd and 4th rows, where red and black lines representing the results from original and reshuffled networks. Results of reshuffled networks are obtained by averaging over five independent realizations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4257537&req=5

pone-0113457-g002: Degree distributions and degree correlations.All degree distributions are power-law-like. and are respectively showed in the 3rd and 4th rows, where red and black lines representing the results from original and reshuffled networks. Results of reshuffled networks are obtained by averaging over five independent realizations.
Mentions: We consider two real data sets with anonymous users in this paper (datasets are free to download as Dataset S1), including (a) Tmall.com (TM): an open business-to-consumer (B2C) platform where enrolled businessmen can sell legal items to customers; (b) Coo8.com (Coo8): a well established online retailer mainly trading in electrical household appliances and a leading supplier to daily necessities. In order to avoid the isolate nodes in the data sets, each user has bought at least one item, and each item has been purchased at least once. Table 1 shows the basic statistics of the two data sets. Due to the different types of products, these networks have much different average item degrees. As shown in Figure 2, all degree distributions are heavy-tailed and the item degree distributions are generally more heterogenous than the corresponding user degree distributions. These observations complement previous empirical analyses on user-item bipartite networks [47]–[50].

Bottom Line: 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.

View Article: PubMed Central - PubMed

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.

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.

Show MeSH
Related in: MedlinePlus