By Xiongcai Cai, Michael Bain, Alfred Krzywicki (auth.), Jian Pei, Vincent S. Tseng, Longbing Cao, Hiroshi Motoda, Guandong Xu (eds.)
The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed complaints of the seventeenth Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. the complete of ninety eight papers awarded in those court cases was once rigorously reviewed and chosen from 363 submissions. They hide the final fields of knowledge mining and KDD generally, together with trend mining, class, graph mining, functions, desktop studying, characteristic choice and dimensionality relief, a number of details assets mining, social networks, clustering, textual content mining, textual content category, imbalanced facts, privacy-preserving info mining, suggestion, multimedia information mining, move facts mining, info preprocessing and representation.
Read or Download Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part II PDF
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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
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.
In online dating, people are looking for potential partners. A user contacts people they like by sending messages. Receivers of messages then have options to reply, positively if they like the sender, negatively if they do not like the sender or are not sure, or they may just not reply. Speciﬁcally, the data contains interaction records, each of which represents a contact by a tuple containing the identities of the sender and receiver and whether the contact was accepted (positive response from receiver to sender) or not.
For example, if item k is a movie, user i may ask his/her friends or read reviews before watching it. If item k is a hard drive, the user i may have clear idea about his/her preferences (size, price range) and can judge the quality easily without consulting friends. To make αi,j context-sensitive, we propose to set the value of αi,j based on the features of user i and item j using the following sigmoid function: αi,j = sigmoid(wT fi,j ) (3) where w ∈ RP and fi,j is a P -dimensional feature vector about user i and item j.