paper-with-me

홈 › Papers

Location and Activity Recommendation by Using Consecutive Itinerary Matching Model

2013-10-01 · ROCLINGIJCLCLP 2013 10 · Jiun-Shian Liu, Wen-Hsiang Lu
📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

POIBERT: A Transformer-based Model for the Tour Recommendation Problem

2022-12-16 · Ngai Lam Ho, Kwan Hui Lim

Tour itinerary planning and recommendation are challenging problems for tourists visiting unfamiliar cities. Many tour recommendation algorithms only consider factors such as the location and popularity of Points of Inte…

Language ModelingLanguage ModellingSentenceSentence Completion

Itinerary-aware Personalized Deep Matching at Fliggy

2021-08-05 · Jia Xu, Ziyi Wang, Zulong Chen, Detao Lv 외

Matching items for a user from a travel item pool of large cardinality have been the most important technology for increasing the business at Fliggy, one of the most popular online travel platforms (OTPs) in China. There…

DiversityRecommendation Systems

User Preferential Tour Recommendation Based on POI-Embedding Methods

2021-03-03 · Ngai Lam Ho, Kwan Hui Lim

Tour itinerary planning and recommendation are challenging tasks for tourists in unfamiliar countries. Many tour recommenders only consider broad POI categories and do not align well with users' preferences and other loc…

Optimizing Group Utility in Itinerary Planning: A Strategic and Crowd-Aware Approach

2023-04-04 · Junhua Liu, Kwan Hui Lim, Kristin L. Wood, Menglin Li

Itinerary recommendation is a complex sequence prediction problem with numerous real-world applications. This task becomes even more challenging when considering the optimization of multiple user queuing times and crowd …

Utilizing Language Models for Tour Itinerary Recommendation

2023-11-21 · Ngai Lam Ho, Kwan Hui Lim

Tour itinerary recommendation involves planning a sequence of relevant Point-of-Interest (POIs), which combines challenges from the fields of both Operations Research (OR) and Recommendation Systems (RS). As an OR proble…

Recommendation Systems