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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

Performance of the four strategies for original TM and Coo8 bipartite networks.The results of MaxD, MinD, PA and RAN are represented by black squares, red circles, blue triangles and green pentagrams, respectively. Data points are obtained by averaging over 100 independent realizations.
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pone-0113457-g003: Performance of the four strategies for original TM and Coo8 bipartite networks.The results of MaxD, MinD, PA and RAN are represented by black squares, red circles, blue triangles and green pentagrams, respectively. Data points are obtained by averaging over 100 independent realizations.

Mentions: In our simulation, we only consider ranging from 1 to 1000 to see the influence of different on promoting strategies. It is because too large will result in very high cost and indeed can make the item among the most popular ones. Unexpectedly, as shown in figure 3, MaxD hardly makes new items recommended while MinD usually shows better performance. Consider the general case where the target item has established a link to user , and and are two of 's collected items before . For another user who is not connected with . If has collected but not , then both and have the chance to be recommended to . Since in the ICF algorithm, item similarities play the major role, let's compare the similarities and . Statistically speaking, if is a very active user selected by the MaxD strategy, and are probably less popular as indicated by the disassortative nature of the networks, therefore (i.e., ) may be much larger than and then is probably smaller than , resulting in less probability of to be recommended to . In contrast, if is a very inactive user selected by the MinD strategy, and are probably of larger degrees according to the disassortative nature, resulting in smaller and thus larger probability for to be recommended to . In addition, since is very unpopular, it is also possible that and is only connected with . In such case, for all other users connected with , will be the only recommended item related to .


Promoting cold-start items in recommender systems.

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

Performance of the four strategies for original TM and Coo8 bipartite networks.The results of MaxD, MinD, PA and RAN are represented by black squares, red circles, blue triangles and green pentagrams, respectively. Data points are obtained by averaging over 100 independent realizations.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0113457-g003: Performance of the four strategies for original TM and Coo8 bipartite networks.The results of MaxD, MinD, PA and RAN are represented by black squares, red circles, blue triangles and green pentagrams, respectively. Data points are obtained by averaging over 100 independent realizations.
Mentions: In our simulation, we only consider ranging from 1 to 1000 to see the influence of different on promoting strategies. It is because too large will result in very high cost and indeed can make the item among the most popular ones. Unexpectedly, as shown in figure 3, MaxD hardly makes new items recommended while MinD usually shows better performance. Consider the general case where the target item has established a link to user , and and are two of 's collected items before . For another user who is not connected with . If has collected but not , then both and have the chance to be recommended to . Since in the ICF algorithm, item similarities play the major role, let's compare the similarities and . Statistically speaking, if is a very active user selected by the MaxD strategy, and are probably less popular as indicated by the disassortative nature of the networks, therefore (i.e., ) may be much larger than and then is probably smaller than , resulting in less probability of to be recommended to . In contrast, if is a very inactive user selected by the MinD strategy, and are probably of larger degrees according to the disassortative nature, resulting in smaller and thus larger probability for to be recommended to . In addition, since is very unpopular, it is also possible that and is only connected with . In such case, for all other users connected with , will be the only recommended item related to .

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