Exploiting semantics for context-aware itinerary recommendation

Fogli, Alessandro; Sansonetti, Giuseppe

doi:10.1007/s00779-018-01189-7

Cited by 37 publications

(20 citation statements)

References 40 publications

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“…To optimized the minimum trip duration of each itinerary, Hsueh et al [40] calculated the distances from the beginning location to the destination. Fogli and Sansonetti [6] exploited feedback from the user to maximize the user's satisfaction for the itinerary recommendation. Nurbakova et al [14] merged the activities by maximizing the sum of the user's satisfaction scores within the itinerary under spatio-temporal constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Recent works have shown the effectiveness of geotagged photos in improving the itinerary recommendation performance [2]- [5]. In particular, the main idea of these approaches is to learn a sequence of POIs and consider many factors such as user interest [6], POI popularity [8], POI category [9], and trip constraints like time [7] and cost [15] for constructing the itinerary planning models. However, most of these works are proposed based on the Orienteering Problem (OP) or traveling salesman problem (TSP) variants.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Genetic Algorithm for Personalized Itinerary Planning

Yochum

Chang

et al. 2020

IEEE Access

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Traveling as a very popular leisure activity enjoyed by many people all over the world. Typically, tourists have different kinds of preferences about their itineraries, limited time budgets, unfamiliar with the wide range of Points-of-Interest (POIs) in a city, so that planning an itinerary is quite tedious, time-consuming, and challenging for them. In this paper, we propose an adaptive genetic algorithm for personalized itinerary planning for travelers to plan their itineraries better. Firstly, desired starting POIs (e.g., POIs that are close to their hotels) and destination POIs (e.g., POIs that are near train stations or airports) are considered in our approach. Secondly, we also take some general factors into account that travelers would consider in their preferences of an itinerary, which are mandatory POIs, the total number of POIs, the overall POI popularity, the overall cost, and the overall rating. Thirdly, we view this kind of recommendation task as a Multi-Objective Optimization problem, and we propose an adaptive genetic algorithm with the crossover and mutation probabilities (AGAM) for solving this problem to better find the best global solution. Fourthly, we allocate different weights to every factor which considered in our paper to generate a personalized itinerary recommendation for better meet many kinds of preferences of tourists. Finally, we compare our approach against baselines on real-world datasets which include six touristic cities, and the experimental results show that the AGAM achieves better recommendation performance in terms of the mandatory POIs, total POI visits, overall POI popularity, total travel time (including travel time and visit duration), overall cost, and overall rating.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Genetic Algorithm for Personalized Itinerary Planning

Yochum

Chang

et al. 2020

IEEE Access

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“…Artificial Intelligence (AI) is increasingly part of our lives. It organizes the services of our cities [ 7 ], suggests which points [ 28 ] are likely to be of interest to us (e.g., artistic and cultural resources [ 29 ] or restaurants [ 2 ]) and how to reach them [ 11 ]. It recommends us which news articles [ 4 ] or research papers [ 16 ] to read, which movies to watch [ 1 ], which products to buy [ 3 ], which music artists and songs to listen to [ 26 ], and even which people to attend [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Machine Learning: Prospects and Challenges

Vaccaro

Sansonetti

Micarelli

2020

Computational Science and Its Applications – ICCSA 2020

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The State of the Art of the young field of Automated Machine Learning (AutoML) is held by the connectionist approach. Several techniques of such an inspiration have recently shown promising results in automatically designing neural network architectures. However, apart from back-propagation, only a few applications of other learning techniques are used for these purposes. The back-propagation process takes advantage of specific optimization techniques that are best suited to specific application domains (e.g., Computer Vision and Natural Language Processing). Hence, the need for a more general learning approach, namely, a basic algorithm able to make inference in different contexts with distinct properties. In this paper, we deal with the problem from a scientific and epistemological point of view. We believe that this is needed to fully understand the mechanisms and dynamics underlying human learning. To this aim, we define some elementary inference operations and show how modern architectures can be built by a combination of those elementary methods. We analyze each method in different settings and find the best-suited application context for each learning algorithm. Furthermore, we discuss experimental findings and compare them with human learning. The discrepancy is particularly evident between supervised and unsupervised learning. Then, we determine which elementary learning rules are best suited for unsupervised systems, and, finally, we propose some improvements in reinforcement learning architectures.

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“…RSs assist users in the fruition of cultural heritage resources [ 30 ] and points of general interest [ 28 ] as well. They also suggest personalized itineraries [ 12 ] based on the user’s interests and the context of use [ 2 ]. With the spread of RSs over the years, we have witnessed more and more proposals of recommendation techniques that show off better results compared to classic approaches.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of State-of-the-Art Recommendation Techniques in the Movie Domain

Valeriani

Sansonetti

Micarelli

2020

Computational Science and Its Applications – ICCSA 2020

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Recommender systems (RSs) represent one of the manifold applications in which Machine Learning can unfold its potential. Nowadays, most of the major online sites selling products and services provide users with RSs that can assist them in their online experience. In recent years, therefore, we have witnessed an impressive series of proposals for novel recommendation techniques that claim to ensure significative improvements compared to classic techniques. In this work, we analyze some of them from a theoretical and experimental point of view and verify whether they can deliver tangible real improvements in terms of performance. Among others, we have experimented with traditional model-based and memory-based collaborative filtering, up to the most recent recommendation techniques based on deep learning. We have chosen the movie domain as an application scenario, and a version of the classic MovieLens as a dataset for training and testing our models.

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Exploiting semantics for context-aware itinerary recommendation

Cited by 37 publications

References 40 publications

An Adaptive Genetic Algorithm for Personalized Itinerary Planning

An Adaptive Genetic Algorithm for Personalized Itinerary Planning

Automated Machine Learning: Prospects and Challenges

A Comparative Analysis of State-of-the-Art Recommendation Techniques in the Movie Domain

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