Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
A music search engine built upon audio-based and web-based similarity measures
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Characterizing social cascades in flickr
Proceedings of the first workshop on Online social networks
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Evaluating similarity measures for emergent semantics of social tagging
Proceedings of the 18th international conference on World wide web
Recommending new movies: even a few ratings are more valuable than metadata
Proceedings of the third ACM conference on Recommender systems
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
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Several recent results show the influence of social contacts to spread certain properties over the network, but others question the methodology of these experiments by proposing that the measured effects may be due to homophily or a shared environment. In this paper we justify the existence of the social influence by considering the temporal behavior of Last.fm users. In order to clearly distinguish between friends sharing the same interest, especially since Last.fm recommends friends based on similarity of taste, we separated the timeless effect of similar taste from the temporal impulses of immediately listening to the same artist after a friend. We measured strong increase of listening to a completely new artist in a few hours period after a friend compared to non-friends representing a simple trend or external influence. In our experiment to eliminate network independent elements of taste, we improved collaborative filtering and trend based methods by blending with simple time aware recommendations based on the influence of friends. Our experiments are carried over the two-year "scrobble" history of 70,000 Last.fm users.