Slope One Predictors for Online Rating-Based Collaborative Filtering Daniel Lemire, Anna Maclachlan In SIAM Data Mining (SDM’05), Newport Beach, California,

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Presentation transcript:

Slope One Predictors for Online Rating-Based Collaborative Filtering Daniel Lemire, Anna Maclachlan In SIAM Data Mining (SDM’05), Newport Beach, California, April 21-23,

Motivation  Separating items that a user liked from items that a user disliked  Providing robust CF schemes that are  Easy to implement and maintain  Updateable on the fly  Efficient at query time  Expect little from first visitors  Accurate within reason 2

Basic Slope One scheme User A = u[2] = {1, 1.5} User B = u[2] = {2, 2.5} S(u) is subset of u[i] X is the training set If each u X, then S i (X) is the set contain item i Slope One form f(x)=x+b To find the best function f that predicts one item’s ratings from the other item’s ratings. 3

Memory-based (Item-based) & Model-based  Similarity  Collecting users rating set data from users (user-based CF)  Predicting rating by precomputing the similarity from another item’s rating  Generating recommendations  Difference  Memory-based will take too much time on computing too much data  Model-based will build models from the history data then use these models computing the predictions 4

CF Algorithms  Three propose scheme  SLOPE ONE  BI-POLAR SLOPE ONE  WEIGHT SLOPE ONE  Four contrast scheme  Memory-based  PERSON  Model-based  PRE USER AVERAGE  BIAS FROM MEAN  ADJESTED COSINE ITEM-BASED 5

PRE USER AVERAGE Item1Item2Item3Item4 UserA 524 UserB 4332 UserC 333 6

BIAS FROM MEAN Item1Item2Item3Item4 UserA 524 UserB 4332 UserC 333 7

ADJESTED COSINE ITEM-BASED (1) Item1Item2Item3Item4 UserA 524 UserB 4332 UserC 333 8

ADJESTED COSINE ITEM-BASED (2) Item1Item2Item3Item4 UserA 524 UserB 4332 UserC 333 9

PERSON  Among the most popular and accurate memory-based scheme  It takes the form of all users in X where is a similarity measure computed from Person’s correlation 10

SLOPE ONE  Building item deviation matrix Item1Item2Item3Item4 UserA 524 UserB 4332 UserC

WEIGHT SLOPE ONE Item1Item2Item3Item4 UserA 524 UserB 4332 UserC

BI-POLAR SLOPE ONE Item1Item2Item3Item4 UserA 524 UserB 4332 UserC  S like (UserA) = {Item1, Item3} S dislike (UserA) = {Item4}  S like (UserB) = {Item1, Item3} S dislike (UserB) = {Item4}

Experiment Results  All But One Mean Average Error (MAE)  Computing the average error on the predictions  Using EachMovie and Movielens data from