A Fortunate Refining Trip Recommendation Model

Author:

Abbas RizwanORCID,Amran Gehad AbdullahORCID,Abdulraheem Ahmed A.,Hussain IrshadORCID,Ghoniem Rania M.,Ewees Ahmed A.

Abstract

Personalized travel recommendations propose locations of interest (LOIs) for users. The LOI sequence suggestion is more complicated than a single LOI recommendation. Only a few studies have considered LOI sequence recommendations. Creating a reliable succession of LOIs is difficult. The two LOIs that follow each other should not be identical or from the same category. It is vital to examine the types of subsequent LOIs when designing a sequence of LOIs. Another issue is that providing precise and accurate location recommendations bores users. It can be tedious and monotonous to look at the same types of LOIs repeatedly. Users may want to change their plans in the middle of a trip. The trip must be dynamic rather than static. To address these concerns in the recommendations, organize a customized journey by looking for continuity, implications, innovation, and surprising (i.e., high levels of amusement) LOIs. We use LOI-likeness and category differences between subsequent LOIs to build sequential LOIs. In our travel recommendations, we leveraged luck and dynamicity. We suggest a fortunate refining trip recommendation (FRTR) to address the issues of identifying and rating user pleasure. An algorithm oof compelling recommendation should offer what we are likely to enjoy and provide spontaneous yet objective components to maintain an open doorway to new worlds and discoveries. In addition, two advanced novel estimations are presented to examine the recommended precision of a sequence of LOIs: regulated precision (RP) and pattern precision (PP). They consider the consistency and order of the LOIs. We tested our strategy using data from a real-world dataset and user journey records from Foursquare dataset. We show that our system outperforms other recommendation algorithms to meet the travel interests of users.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Reference34 articles.

1. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency

2. Recommending tours and places-of-interest based on user interests from geo-tagged photos;Lim;Proceedings of the 2015 ACM SIGMOD on PhD Symposium,2015

3. Automatic construction of travel itineraries using social breadcrumbs;Choudhury;Proceedings of the 21st ACM Conference on Hypertext and Hypermedia,2010

4. Efficient Itinerary Planning with Category Constraints;Bolzoni;Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems,2014

5. Personalized Pois Travel Route Recommendation System Based on Tourism Big Data;Bin;Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence,2018

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