Personalized itinerary recommendation: Deep and collaborative learning with textual information-Reference-Cited by-同舟云学术

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Personalized itinerary recommendation: Deep and collaborative learning with textual information

Published:2020-04 Issue: Volume:144 Page:113070
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Chen Lei,Zhang Lu^ORCID,Cao Shanshan,Wu Zhiang^ORCID,Cao Jie

Funder

National Key Research and Development Program

National Natural Science Foundation of China

National Center for International Joint Research on E-Business Information Processing

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference43 articles.

1. Efficient itinerary planning with category constraints;Bolzoni,2014

2. Where shall we go today?: Planning touristic tours with tripbuilder;Brilhante,2013

3. On planning sightseeing tours with tripbuilder;Brilhante;Information Processing and Management,2015

4. Discovering popular routes from trajectories;Chen,2011

5. Personalized travel recommendation by mining people attributes from community-contributed photos;Cheng,2011

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1. Selection and visiting sequence of daily attractions: Multi-day travel itinerary recommendation based on multi-source online data;Expert Systems with Applications;2024-09

2. Shopping trip recommendations: A novel deep learning-enhanced global planning approach;Decision Support Systems;2024-07

3. An effective approach for bi-objective multi-period touristic itinerary planning;Expert Systems with Applications;2024-04

4. A personalized context and sequence aware point of interest recommendation;Multimedia Tools and Applications;2024-02-27

5. A survey on personalized itinerary recommendation: From optimisation to deep learning;Applied Soft Computing;2024-02

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