Collaborative filtering20081111

DCFLA: A D istributed C ollaborative- F iltering Neighbor- L ocating A lgorithm Authors: Bo Xie, Peng Han, Fan Yang, Rui-Min Shen, Hua- Jun Zeng, and Zheng Chen Source: Information Sciences, vol. 177, no. 6, pp. 1349-1363, 2007. Professor: Dr. Shu-Ching Wang ( 王淑卿 ) Speaker: Yu-Chien Chou( 周裕健 ) Date: Nov. 11, 2008

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Introduction (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Introduction (2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Related work (1/5) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],P a,j is the vote by active user a on item j , v' a is the mean vote by user a , N is the number of users in the user database, v i , j is the vote by user i on item j , v' i is the mean vote by user i . v i,j is the vote by user i on item j , I i is the total items . Nov. 11, 2008

Related work (2/5) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Related work (3/5) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Related work (4/5) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Related work (5/5) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Novel collaborative-filtering (1/10) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Novel collaborative-filtering (2/10) ,[object Object],[object Object],Rid of some noisy users Nov. 11, 2008

Novel collaborative-filtering (3/10) ,[object Object],[object Object],[object Object],M is the total number of users in the system, n i,v is the user voted for item i with rating v . Nov. 11, 2008

Novel collaborative-filtering (4/10) ,[object Object],[object Object],[object Object],C i,j is the consistency of users i and j , v i,k is the vote of user i for item k , v j,k is the vote of user j for item k , N is the total number of items in the system. Nov. 11, 2008

Novel collaborative-filtering (5/10) ,[object Object],Program SimilarityCalculation (Output) For User=1:M For OtherUser=1:M, Calculate similarity between User and OtherUser by “ Pearson correlation coefficient or Vector similarity ” End End. Nov. 11, 2008 ,[object Object],[object Object],[object Object]

Novel collaborative-filtering (6/10) ,[object Object],Program DHT_MSO_NeighborLocating (Output) For each rated item, fetch vector <USERID,IPF> from bucket; End Merge vectors to get consistency by IPF Nov. 11, 2008 ,[object Object]

Novel collaborative-filtering (7/10) ,[object Object],[object Object],Nov. 11, 2008 Item ID 1 2 3 4 5 6 Vote(a) 4 4 5 5 6 6 Vote(b) 3 3 4 4 5 5

Novel collaborative-filtering (8/10) ,[object Object],[object Object],Nov. 11, 2008 Item ID 1 2 3 4 5 6 Vote(a) 4 4 5 5 6 6 Vote(b) 3 3 4 4 5 5

Novel collaborative-filtering (9/10) ,[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008 New approach Former approach Item ID 1 2 3 4 5 6 Vote(a) -1 -1 0 0 1 1 Vote(b) -1 -1 0 0 1 1 Item ID 1 2 3 4 5 6 Vote(a) 4 4 5 5 6 6 Vote(b) 3 3 4 4 5 5

Novel collaborative-filtering (10/10) ,[object Object],[object Object],[object Object],[object Object],v ’ i,j is the ARN_VOTE of user i on item j , v ij is the vote of user i on item j , v ’ i is the mean vote for user i . v req : retrieve similar neighbors, v ack : return the users degree of satisfying, δ : threshold. Nov. 11, 2008

Recommendation systems (1/7) ,[object Object],Nov. 11, 2008 User1 User2 User3 User4 User5

Recommendation systems (2/7) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Recommendation systems (3/7) ,[object Object],[object Object],Construct DHT overlay network put (key), lookup (key) Training set, executing the put (key) function Fetches similar neighbors, executing the lookup (key) function Computes the corresponding prediction put (key) <ITEM_ID, VOTE> Input: training set, test set, target item Output: mean absolute error of prediction Nov. 11, 2008

Recommendation systems (4/7) ,[object Object],P generates a unique 128-bit DHT key k local Local key k local is most similar to K Receives the put message with K Repeats steps 2 and 3 Input: test set ( P ’s vote vector) Output: NULL P hashes one <ITEM_ID, VOTE> combination to key K Finding the similar neighbor Nov. 11, 2008

Recommendation systems (5/7) ,[object Object],P generates a unique 128-bit DHT key k local Local key k local is most similar to K Receives the lookup, message with K Repeats steps 2 and 3 Input: test set ( P ’s vote vector) Output: training set (vote vectors retrieved for similar users) P hashes one <ITEM_ID, VOTE> combination to key K Finding the similar neighbor Received from similar users Nov. 11, 2008

Recommendation systems (6/7) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Recommendation systems (7/7) ,[object Object],[object Object],Construct DHT overlay network put (key), lookup (key) Training set, executing the put (key) function Fetches similar neighbors, executing the lookup (key) function Computes the corresponding prediction Input: training set, test set, target item Output: mean absolute error of prediction put (key) <ITEM_ID, ARN_VOTE> Nov. 11, 2008

Experiments (1/10) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Experiments (2/10) ,[object Object],[object Object],v a,j is the actual rating user a gives to item j , p a,j is the predicted value, A is the active user set, T is the test item set. Nov. 11, 2008

Experiments (3/10) ,[object Object],[object Object],Nov. 11, 2008

Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Nov. 11, 2008

Collaborative filtering20081111

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