Read e-book online Advances in Knowledge Discovery and Data Mining: 17th PDF

By Xiongcai Cai, Michael Bain, Alfred Krzywicki (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)

ISBN-10: 3642374557

ISBN-13: 9783642374555

ISBN-10: 3642374565

ISBN-13: 9783642374562

The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed lawsuits of the seventeenth Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. the entire of ninety eight papers provided in those lawsuits was once conscientiously reviewed and chosen from 363 submissions. They conceal the overall fields of information mining and KDD commonly, together with development mining, type, graph mining, functions, laptop studying, function choice and dimensionality relief, a number of info assets mining, social networks, clustering, textual content mining, textual content category, imbalanced facts, privacy-preserving facts mining, suggestion, multimedia information mining, movement info mining, facts preprocessing and representation.

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Extra info for Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II

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However, most of prior research only focused on a single-domain recommendation and thus the solutions are less likely to work well in open domain recommenders systems. There are three differences between the two kinds of recommender systems: 1) Data is sparser in the open-domain systems. Opendomain systems have much more items but less user feedback. That means the user-item rating matrix is sparser in open-domain systems. Thus traditional collaborative filtering cannot achieve as good performance as in the single-domain systems.

U v w The solution can be found using conjugate gradient algorithm. The gradient of ui , vj and w can be calculated as below: ∂loss = ∂ui ∂loss = ∂vj ∂loss = ∂w αi,j (ˆ ri,j − ri,j )vj + ri,j ∈R (1 − αi,j )(ˆ rt,j − rt,j )st,i vj + λu ui t∈ϕ(i) rt,j ∈R (ˆ ri,j − ri,j )(αi,j ui + (1 − αi,j ) ri,j ∈R (ˆ ri,j − ri,j )(uTi vj − ri,j ∈R si,k uk ) + λv vj k∈τ (i) si,k uTk vj )αi,j fi,j + λw w k∈τ (i) where αi,j = exp(wT fi,j )/(1 + exp(wT fi,j ))2 is the derivative of the sigmoid function. ϕ(i) is the set of all the users who trust user i.

As a result, we collected a data set that contains 56,859 users, 271,365 items, and 1,154,812 ratings. There are totally 603,686 trust statements. com. 3%) users only rate one item. 26,712 users(47%) rated no more than 5 items. We use two sets of binary features to represent recommendation context. com. The second is the group id that characterizes the number of items the user rated. We classify users into 7 groups (1:“1”, 2:“2-5”, 3:“6-10”, 4:“11-20”, 5:“21-40”, 6:“41-80”, 7:“>80”). We carry out experiments on two recommendation tasks: Rating Prediction Given a user i and an item j, the task is to predict the rating of user i on item j.

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Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II by Xiongcai Cai, Michael Bain, Alfred Krzywicki (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)


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