Presentation on theme: "Learning to Question: Leveraging User Preferences for Shopping Advice"— Presentation transcript:
1Learning to Question: Leveraging User Preferences for Shopping Advice Date : 2013/12/11Author : Mahashweta Das, Aristides Gionis,Gianmarco De Francisci Morales,and Ingmar WeberSource : KDD’13Advisor : Jia-ling KohSpeaker : Yi-hsuan Yeh
3IntroductionMotivationCustomers shop online, from their homes, without any human interaction involved.Catalogs of online shops are so big and with so many continuous updates that no human, however expert, can effectively comprehend the space of available products.Use a flowchart asks the shopper a question, and the sequence of answers leads the shopper to the suggested shopping option.
4IntroductionSHOPPINGADVISOR is a novel recommender system that helps users in shopping for technical products.car
5IntroductionSHOPPINGADVISOR generates a tree-shaped flowchart, in which the internal nodes of the tree contain questions involve only attributes from the user space.non-expert users can understand easily.
6Introduction Find the best user attribute to ask at each node. How to learn the structure of the tree, i.e., which questions to ask at each node.Find the best user attribute to ask at each node.This paper focus on identifying the attribute of interest, and not on the task of formulating the question in a human interpretable way.How to produce a suitable ranking at each node.Learning-to-rank approach
16Rank list RANKSVM Count payoff productABDC.FERank listRANKSVMCount payoffConsider all possible user attributes 𝛼, and choose as splitter the one that maximizes the pay-off.
17Stopping criterion Grow the tree to its “entirety” Post-pruning If a node’s child node is split by the “near-synonomous” tag trim the child nodeExample:travelvacationEmploy pruning rules on the validation set.
20Experiment setup SHOPPINGADVISOR RANKSVM k-NN SA.k-NN Author’s method The ranked list returned by SHOPPINGADVISOR at the rootk-NNk-nearest neighbors algorithmSA.k-NNFeatures are selected from SHOPPINGADVISOR
21Quality evaluation A B D 25 topics 12 topics .System result ranking listaverage MRRIf user prefer “B” 1 𝑟𝑎𝑛𝑘 𝑖 = 1 2
24ConclusionProposed a novel recommender system, SHOPPINGADVISOR, that helps users to shop for technical products.SHOPPINGADVISOR leverages both user preferences and technical product attributes in order to generate its suggestions.At each node, SHOPPINGADVISOR suggests a ranking of products matching the preferences of the user.Compared with a baseline, and demonstrated the effectiveness of the approach.