G
Guixiang Zhu
Researcher at Nanjing University of Finance and Economics
Publications - 18
Citations - 157
Guixiang Zhu is an academic researcher from Nanjing University of Finance and Economics. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 5, co-authored 12 publications receiving 59 citations. Previous affiliations of Guixiang Zhu include Nanjing University of Science and Technology.
Papers
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Journal ArticleDOI
Online purchase decisions for tourism e-commerce
TL;DR: This study considers the purchase prediction problem in the context of e-tourism, an emerging and prevailing application in e-commerce and presents a novel model called co-EM Logistic Regression (co-EM-LR) which combines the semi-supervised learning and the multi-view learning into its procedure.
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A recommendation engine for travel products based on topic sequential patterns
TL;DR: This work presents a novel recommendation engine (SECT for short) for travel products based on topic sequential patterns, and proposes a Markov n-gram model for matching the real-time click-stream of users with the click patterns library and thus computing recommendation scores.
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Attentive multi-task learning for group itinerary recommendation
TL;DR: An AMT-IRE (short for Attentive Multi-Task learning-based group Itinerary REcommendation) framework, which can dynamically learn the inner relations between group members and obtain consensus group preferences via the attention mechanism is proposed.
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Travel Recommendation via Fusing Multi-Auxiliary Information into Matrix Factorization
TL;DR: The e-tourism has become one of the hottest industries with the adoption of self-service booking systems, and the personalized recommendation is invariably highly-valued by both consumers and merchants.
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Neural Attentive Travel package Recommendation via exploiting long-term and short-term behaviors
TL;DR: This paper proposes a novel model named Neural Attentive Travel package Recommendation (NATR) for tourism e-commerce by combining users’ long-term preferences with short-term Preferences by adopting a gated fusion approach to coalesce these two kinds of preferences for learning high-quality the user’s representation.