Cross-Selling with Collaborative Filtering Qiang Yang HKUST Thanks: Sonny chee
1 Cross-Selling with Collaborative Filtering Qiang Yang HKUST Thanks: Sonny Chee
Motivation Question: a user bought some products already what other products to recommend to a user Collaborative Filtering(CF Automates circle of advisors +
2 Motivation ◼ Question: ◼ A user bought some products already ◼ what other products to recommend to a user? ◼ Collaborative Filtering (CF) ◼ Automates “circle of advisors”. +
Collaborative Filtering people collaborate to help one another perform filtering by recording their reactions. ,(Tapestry) Finds users whose taste is similar to you and uses them to make recommendations Complimentary to IR/IF IR/IF finds similar documents-CF finds similar users
3 Collaborative Filtering “..people collaborate to help one another perform filtering by recording their reactions...” (Tapestry) ◼ Finds users whose taste is similar to you and uses them to make recommendations. ◼ Complimentary to IR/IF. ◼ IR/IF finds similar documents – CF finds similar users
Example Which movie would sammy watch next? Ratings 1--5 Titles Starship Sleepless Trooper in Seattle MI-2 Matrix Titanic (R (R) Sammy Beatrice Dylan g Mathew 44423 Gum-Fat A333445 333345 1344? 454? Basil If we just use the average of other users who voted on these movies then we get Matrix 3: Titanic 1474=3.5 Recommend titanic But is this reasonable?
4 Example ◼ Which movie would Sammy watch next? ◼ Ratings 1--5 • If we just use the average of other users who voted on these movies, then we get •Matrix= 3; Titanic= 14/4=3.5 •Recommend Titanic! •But, is this reasonable? Starship Trooper (A) Sleepless in Seattle (R) MI-2 (A) Matrix (A) Titanic (R) Sammy 3 4 3 ? ? Beatrice 3 4 3 1 1 Dylan 3 4 3 3 4 Mathew 4 2 3 4 5 Gum-Fat 4 3 4 4 4 Basil 5 1 5 ? ? Titles Users
Types of Collaborative Filtering Algorithms Collaborative filters Statistical collaborative filters Probabilistic Collaborative Filters [PHlooj Bayesian Filters [BP9 9][BHK98] Association Rules [agrawal, Han] Open problems Sparsity First Rater, scalability
5 Types of Collaborative Filtering Algorithms ◼ Collaborative Filters ◼ Statistical Collaborative Filters ◼ Probabilistic Collaborative Filters [PHL00] ◼ Bayesian Filters [BP99][BHK98] ◼ Association Rules [Agrawal, Han] ◼ Open Problems ◼ Sparsity, First Rater, Scalability