Hybrid User Clustering-Based Travel Planning System for Personalized Point of Interest Recommendation

Ravi, Logesh; Subramaniyaswamy, V.; Vijayakumar, V.; Jhaveri, Rutvij H.; Shah, Jigarkumar

doi:10.1007/978-981-15-9953-8_27

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…Personalized recommendations [11], [12] are capable of delivering interesting content that corresponds to users' interests, hence reducing the issue of information overload. Various practical RS are developed based on users' preferences [23], [24], improving community membership information [25], deep neural network method with item embedding [26], hybrid RS for patron-driven library collection and pruning [27], [28], A travel planning system using clustering to provide personalized point-of-interest recommendations [29]. However, the main technical issues that restrict the widespread deployment of the RS are still issues with data sparsity, cold start, and interpretability [30], [31].…”

Section: A Personalized Recommendationsmentioning

confidence: 99%

Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning

et al. 2023

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Learning activities are considerably supported and improved by the rapid advancement of elearning systems. This gives students a tremendous chance to participate in learning activities worldwide. The Massive Open Online Courses (MOOCs) platform has emerged as one of the most significant platforms for e-learning as a result of the rapid growth of network information technologies. Due to the increased number of online courses available, it is harder for individual learners to choose the appropriate courses, activities, and learning paths for the actual necessities they want, which reduces their learning performance. Moreover, a sequential Recommender System (RS) can identify the learner's future interest and suggest the subsequent item or learning content given a sequence of past interactions. This is in contrast to interactive recommendation methods that can create recommendations based on the learner's feedback via constant interactions. The goal of this paper is to propose a Reinforcement Learning (RL) based smart e-learning framework with Markov Decision Process (MDP) that have the potential to enhance the learning experience for each student by providing them with a personalized and effective learning path. Applying the MDP and RL-based techniques such as Q-learning for Sequential Path Recommendation (SPR) and learning development is more achievable. This is because the MDP allows for adjusting the recommendations method to find new activities and learning paths based on the learners' feedback on recommendations results. Experimental findings reveal that the suggested model obtains significant improvements and provides viable performance under different parameters optimization. Furthermore, we also show that the proposed method outperforms a long session (long-term rewards such that they maximize learning progress while minimizing frustration and disengagement). This demonstrates the model's improvements in simulating the learner's sequential behavior, learning activities, various learning materials, and learning paths simultaneously. These promising initial results provide a possible solution to assess this challenge further in future work.

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Section: A Personalized Recommendationsmentioning

confidence: 99%

Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning

et al. 2023

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“…In tourism, personalization has been studied and applied to different areas, such as travel planning and recommender systems. Existing recommender systems have been analyzed in the literature [15,16], and new ones have been proposed: Missaoui S. [17] presented the mobile app LOOKER, a recommender system based on a user generated content-based filtering algorithm; Amer-Yahia et al [18] developed an interactive framework that generates customized travel packages for individuals or for groups of travelers; De Carolis et al [19] analyzed the potential of social robots in providing effective tourism recommender systems; Wang [20] proved how including data on online behavior of potential travelers can improve personalized recommendations systems; Ravi et al [21] worked on an algorithm-and hybrid user clusteringbased travel planning system for personalized point of interest recommendation; Choi et al [22] presented a recommender system to help tourists create a personalized travel plan based on collaborative and constraint satisfaction filtering; Chen et al [23] developed a method to design personalized travel routes for tourists by combining user clustering, improved genetic, and rectangular region path planning algorithms. Personalization can be used also to provide specific information to tourists with particular needs, as proposed by Ribeiro et al [24] with their personalized system to assist mobility disabled tourists during their visit and activities.…”

Section: Personalization In Tourismmentioning

confidence: 99%

Understanding Preferences in Tourism Email Marketing

Garbani-Nerini

Marchiori

Reale

et al. 2022

Information and Communication Technologies in Tourism 2022

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Nowadays, advanced tools allow the personalization of email communication with tourism clients or prospects based on explicit (e.g. name, age, language, country) and implicit indicators (e.g. ranking of activity in the opening rate of the newsletter, browsing preferences, online preferences provided by cookies, etc.). However, knowing how audiences react to emails allows Destination Marketing Organizations (DMOs) to create content clusters for personalized communication. The purpose of this study is to empirically investigate the preferences on tourism email marketing of different audiences based on a specific explicit indicator, namely the language chosen by users to receive communications by a DMO. A content analysis on a longitudinal dataset based on 131 newsletter messages sent between 2018 and 2021 to more than 50′000 contacts by a DMO in Switzerland was performed. Results show that content should be adapted to different audiences speaking different languages instead of providing just a translation. Specifically, the German-speaking audience seems to be more inclined to messages that focus on winter sports and hiking, the Italian-speaking audience to news about hiking and information on COVID-19, the French-speaking audience to news about promotions, while the English-speaking audience to contents on discounts and COVID-19-related. These results provide an important contribution to studies on tourism personalization of communication in the context of email marketing, suggesting the role of content adaptation according to the language and cultural background of the audience. DMO managers can also benefit from this research in understanding how to address a similar study on their datasets and compare the emerged content clusters.

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Machine Learning and Marketing Campaign: Innovative Approaches and Creative Techniques for Increasing Efficiency and Profit

Hicham

Karim

2023

Lecture Notes on Data Engineering and Communications Technologies

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Hybrid User Clustering-Based Travel Planning System for Personalized Point of Interest Recommendation

Cited by 4 publications

References 42 publications

Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning

Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning

Understanding Preferences in Tourism Email Marketing

Machine Learning and Marketing Campaign: Innovative Approaches and Creative Techniques for Increasing Efficiency and Profit

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