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Title:Joint analysis of user-generated content and product information to enhance user experience in e-commerce
Author(s):Park, Dae Hoon
Director of Research:Zhai, ChengXiang
Doctoral Committee Chair(s):Zhai, ChengXiang
Doctoral Committee Member(s):Han, Jiawei; Chang, Kevin C.C.; Fang, Yi
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Information Retrieval
Text Mining
User Review Mining
Abstract:The development of Internet has brought us a more convenient way to purchase goods through e-commerce, which has gradually pervaded our life. However, shopping experience of users in e-commerce has been far from the optimum. In order to enhance user experience in e-commerce, we propose a series of novel studies based on joint analysis of user-generated content and product information; in this dissertation, user-generated content includes user reviews and social media text data, and product information includes product descriptions and product specifications in general. This dissertation aims at assisting e-commerce users in two directions: discovering products and making purchase decisions. To help users discover products, we first propose to leverage user reviews to improve accuracy of product search. We carefully combine product descriptions and user reviews to improve product search. Then, we also propose to recommend products via inference of implicit intent in social media text. We infer implicit intent in user status text leveraging parallel corpora we build from social media, and we recommend products whose descriptions satisfy the inferred intent. In order to help users make purchase decisions, we first propose to generate augmented product specifications leveraging user reviews. Product specifications are often difficult to understand especially for high-technology products that contain many advanced features. We jointly model user reviews and product specifications to augment product specifications with useful information in the user reviews. We also propose to retrieve relevant opinions for new products. New or unpopular products often have no reviews, and such lack of information makes consumers hesitate to make a purchase decision. We leverage user reviews of similar products, where similarity is estimated using product specifications, to retrieve relevant opinions for new products. The experiment results show the proposed models are effective in general. The models are also general enough to be applied to any entities with their text data. Furthermore, the models can benefit both product manufacturers and consumers, so their potential impact may be even bigger.
Issue Date:2016-04-21
Rights Information:Copyright 2016 Dae Hoon Park
Date Available in IDEALS:2016-07-07
Date Deposited:2016-05

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