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Cited by 5Recommender Systems and TechniquesHuman Mobility and Location-Based AnalysisInformation Retrieval and Search Behavior

Recommending Reforming Trip to a Group of Users

Open access
Abbas, Rizwan, Amran, Gehad Abdullah, Alsanad, Ahmed, Ma, Shengjun, Almisned, Faisal Abdulaziz, Huang, Jianfeng, Al Bakhrani, Ali Ahmed, Ahmed, Almesbahi Belal +1 more·2022·Electronics
DOI 10.3390/electronics11071037
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Abstract

With the quick evolution of mobile apps and trip guidance technologies, a trip recommender that recommends sequential points of interest (POIs) to travelers has emerged and recently received popularity. Compared to other outing recommenders, which suggest the following single POI, our proposed trip proposal research centers around the POI sequence proposal. An advanced sequence of the POI recommendation system named Recommending Reforming Trip (RRT) is presented, recommending a dynamic sequence of POIs to a group of users. It displays the information progression in a verifiable direction, and the output produced is the arrangement of POIs to be expected for a group of users. A successful plan is executed depending upon the deep neural network (DNN) to take care of this sequence-to-sequence problem. From start to finish of the work process, RRT can permit the input to change over time by smoothly recommending a dynamic sequence of POIs. Moreover, two advanced new estimations, adjusted precision (AP) and sequence-mindful precision (SMP), are introduced to analyze the recommended precision of a sequence of POIs. It considers the POIs’ consistency and also meets the sequence of order. We evaluate our algorithm using users’ travel histories extracted from a Weeplaces dataset. We argue that our algorithm outperforms various benchmarks by satisfying user interests in the trips.

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  • Adaptation used in [34] to dynamically recommend points of interest based on characteristics of a user group has been identified as a potential means to enhance motivation.

    “Although none of these studies implement information adaptation specifically targeted at a group of users, this type of adaptation-used, for example, in [34] to dynamically recommend points of interest based on the characteristics of a user group-has been identified as a potential means to enhance motivation.”

    From Motiv-ARCHE: co-creation of augmented reality educational content to motivate cultural and natural heritage learning (2025) · cites this paper as [34]

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Cited by

Sorted by citations

Authors
Abbas, R., Amran, G. A., Alsanad, A., Ma, S., Almisned, F. A., Huang, J., Al Bakhrani, A. A., Ahmed, A. B., & Alzahrani, A. I.
Affiliations
  • Northeastern University (CN)
  • Dalian University of Technology (CN)
  • King Saud University (SA)
  • College of Computer Science and Engineering, Peking University, Beijing 100871, China
  • Department of Computer Science, Technique Leaders College, Sanaa 31220, Yemen
Corresponding author
  • Abbas, Rizwan
  • Amran, Gehad Abdullah
  • Alsanad, Ahmed
ORCID
Venue
Electronics
Publisher
MDPI AG
Published
2022-03-25
Volume
11
Issue
7
Pages
1037
ISSN
2079-9292 (ISSN-L)
DOI
10.3390/electronics11071037
OpenAlex
W4220813456
Cited by
5
References
1
Citations by year
2025: 2 · 2023: 1 · 2022: 2
FWCI
1.24
Type
journal article
Language
en
Open access
Licence
Locations
  • Electronics (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • King Saud University
  • Deanship of Scientific Research, King Saud University
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Keywords
points of interest, group recommendation, sequence-to-sequence learning, deep neural network, travel history, recommender systems

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