3.1 User profiling like-minded peers for a target user. Based on user User profiling is to model users' features or profiles, the similarity of users can be calculated preferences. The approaches of profiling users with through various proximity measures such as Pearson user-item rating matrix and keywords vectors are correlation and cosine similarity. In Tso-Sutter's work, widely used in recommender systems. However, these the overlap of tags shared by users was used to measure approaches are used for describing two-dimensional the similarity [3 just like the traditional collaborative relationships between users and items. Though filtering( CF) using the overlap of commonly rated Tso-Sutter's approach takes the relationship between items. Tso-Sutter's method actually is the traditional CF tags and items into consideration [3], it ignores the with an extended dataset treating tags as additional relationship between tags and items for each user. The items. The improvement is very limit, because the user should be profiled not only by the tags and items, users neighborhood may be incorrectly formed if only but also the relationship between the tags and items of treating users tagging as implicit rating and ignoring to he user measure the similarities of the relationships of tags and To profile users tagging behavior accurately, we items. We propose to measure the similarity of two propose to model a user in a collaborative tagging users from the following three aspects community in three aspects, i.e., the tags used by the (1)UTsim(ui, ui): The similarity of users' tags, which user, the items tagged by the user, and the relationship measured by the percentage of common tags used between the tags and the tagged items. For easy by the two users approach, we give TSim(ui4u∩ following definitions first max U=uI, u2.un): Set of users in the collaborative (2) UPsim(ui, u): the similarity of user's items, which is measured by the percentage of common items P=(pl, p2.Pmi: Set of items that have been tagged tagged by the two users: Pu∩P PSim(ui, Uj) T=t, t2.,t: Set of tags that have been used by max( Pu l E(u t, Px=(0, 1): a function that specifies whether user (3)UTPsim(u, u ) the similarity of the users'tag-item u; has used the tag t to tag item pk relationship, which is measured by the percentage The user profile is defined as follows of common relations shared by the two users For a user u;, i-l n, let Tu, be the tag set of u TP∩TPl UTPsim(u;, u;F Tu={t∈T,彐p∈P,F(un4P)=1},TucT,Pube max TPk the item set of u,Pu={pkk∈T,彐t∈P,(u,tpk) =1), Pu,CP, TP, be the relationship between u,'s tag Thus, the overall similarity measure of two users is and item set, TP=(<t, pe>l tET, pEP, and defined as below E(ui, t;, px=1), UF=(Tu, Pui, TPi)is defined as the Simu(ui, uF-WuT*UTsim(u;, u)+WUP"UPsim(u;, u) user profile of user ui. The user profile or user model of +WUTPUTPsim(u, u)(4) all users is denoted as UF, UF=(UFiln j Where WUT WUP+ WUTp=l, wuT, Wup and WUTp are 3.2 Neighborhood formation Similarly, the similarity between two items is defined3.1 User profiling User profiling is to model users’ features or preferences. The approaches of profiling users with user-item rating matrix and keywords vectors are widely used in recommender systems. However, these approaches are used for describing two-dimensional relationships between users and items. Though Tso-Sutter’s approach takes the relationship between tags and items into consideration [3], it ignores the relationship between tags and items for each user. The user should be profiled not only by the tags and items, but also the relationship between the tags and items of the user. To profile user’s tagging behavior accurately, we propose to model a user in a collaborative tagging community in three aspects, i.e., the tags used by the user, the items tagged by the user, and the relationship between the tags and the tagged items. For easy describing the proposed approach, we give the following definitions first: U= {u1, u2…un}: Set of users in the collaborative tagging community. P= {p1, p2… pm}: Set of items that have been tagged by users. T= {t1, t2…, tl}: Set of tags that have been used by users. E(ui,tj,pk)={0,1}: a function that specifies whether user ui has used the tag tj to tag item pk The user profile is defined as follows: For a user ui, i=1..n, let Tui be the tag set of ui, Tui={tj|tj∈T, ∃pk∈P, E(ui,tj, pk) =1}, Tui ⊆ T, Pui be the item set of ui, Pui={pk|pk∈T, ∃tj∈P, E(ui,tj, pk) =1}, Pui⊆ P, TPi be the relationship between ui’s tag and item set, TPi={<tj, pk>| tj∈T, pk∈P, and E(ui,tj,pk)=1} , UFi = (Tui, Pui, TPi) is defined as the user profile of user ui. The user profile or user model of all users is denoted as UF, UF={UFi|i=1..n }. 3.2 Neighborhood Formation Neighbourhood formation is to generate a set of like-minded peers for a target user. Based on user profiles, the similarity of users can be calculated through various proximity measures such as Pearson correlation and cosine similarity. In Tso-Sutter’s work, the overlap of tags shared by users was used to measure the similarity [3] just like the traditional collaborative filtering (CF) using the overlap of commonly rated items. Tso-Sutter’s method actually is the traditional CF with an extended dataset treating tags as additional items. The improvement is very limit, because the user’s neighborhood may be incorrectly formed if only treating users’ tagging as implicit rating and ignoring to measure the similarities of the relationships of tags and items. We propose to measure the similarity of two users from the following three aspects: (1) UTsim(ui, uj): The similarity of users’ tags, which is measured by the percentage of common tags used by the two users: (2) UPsim(ui, uj): the similarity of user’s items, which is measured by the percentage of common items tagged by the two users: (3) UTPsim(ui, uj): the similarity of the users’ tag-item relationship, which is measured by the percentage of common relations shared by the two users: Thus, the overall similarity measure of two users is defined as below: Simu(ui, uj)=wUT*UTsim(ui, uj)+wUP*UPsim(ui, uj ) +wUTP*UTPsim(ui , uj ) (4) Where wUT + wUP+ wUTP=1, wUT, wUP and wUTP are the weights to the three similarity measures, respectively. Similarly, the similarity between two items is defined UTsim(ui,uj)= |Tui∩Tuj| max{|Tuk|} uk ∈U (1) UPsim(ui,uj)= |Pui∩Puj| max{|Puk|} uk ∈U (2) UTPsim(ui,uj)= |TPi∩TPj| max {|TPk|} uk ∈U (3) 60 Authorized licensed use limited to: PORTLAND STATE UNIVERSITY. Downloaded on July 31, 2009 at 18:37 from IEEE Xplore. Restrictions apply