Improving Collaborative Filtering by Incorporating Customer Reviews Hui Hui Supervisor Prof Min-Yen Kan Dr. Kazunari Sugiyama 1.

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Presentation transcript:

Improving Collaborative Filtering by Incorporating Customer Reviews Hui Hui Supervisor Prof Min-Yen Kan Dr. Kazunari Sugiyama 1

Introduction E-commerce is very important in people’s lives. People in China spent > 50 billion purchasing on Taobao on 11/11/2015. Many similar products exist Lots of reviews for a single product Difficult to choose the right product 2

Related Work [Zhang et al. SIGIR’14] Proposes the Explicit Factor Model (EFM) to generate explainable recommendations, meanwhile keeps a high prediction accuracy. [Hu and Liu. AAAI’04] Proposes a method to identify product features customer mentions in his/her reviews. Similar method is used to extract product aspects. 3

Related Work [Koren et al. IEEE’09] Describes the Matrix Factorization model to provide better recommendation results, compared to collaborative filtering. Similar approach is implemented to factorize the user-product_aspect matrix constructed in this experiment. [Sarwar et al. ACM’01] Describes user-based collaborative filtering and proposes item-based collaborative filtering. Base model to be improved in this experiment. 4

Related Work (Last semester) [Platt, Advances in Large Margin Classifiers’99] Compares classification error rate and likelihood scores for an SVM plus sigmoid versus a kernel method trained with a regularized likelihood error function. Provides information on obtaining confidence scores for SVM [Zadronzny and Elkan, ICML’01] [Zadronzny and Elkan, ICML’01] Presents simple but successful methods for obtaining calibrated probability estimates from decision tree and naive Bayesian classifiers. Provides information on obtaining confidence scores for decision trees and naïve Bayesian classifiers 5

Proposed Method 6

Intuition Two users may give similar rating for a product, but they care different aspects of a product. 7

Proposed Method Tokenize users’ reviews Extract product aspects mentioned in reviews Generate user-aspect matrix. 8

Proposed Method 9

Intuition 10 OS BatteryScreenOutlookSize Latent Feature PhoneRing

Experiments 11

Future Work Finish experiment. Improve item-based CF similarity computation Adapt to other language 12