Trip Recommendation Research Articles

Enabling personalized smart tourism with location-based social networks

With the rapid advance of mobile internet, communication technology and the Internet of Things (IoT), the tourism industry is undergoing unprecedented transformation. Smart tourism offers users personalized and customized services for travel planning and recommendations. Location-based social networks (LBSNs) play a crucial role in smart tourism industry by providing abundant data sources through their social networking attributes. However, applying LBSNs to smart tourism is a challenge due to the need to deal with complex multi-source information modeling and tourism data sparsity. In this article, to fully harness the potential of LBSNs using deep learning technologies, we propose an knowledge-driven personalized recommendation method for smart tourism. Representation learning techniques can effectively modeling the contextual information (e.g., time, space, and semantics) in LBSNs, while the data augmentation strategy of contrastive learning techniques can explore user personalized travel behaviors and alleviate data sparsity. To demonstrate the effectiveness of the proposed approach, we conducted a case study on trip recommendation. Furthermore, the patterns of human mobility are revealed by exploring the effect of contextual data and tourist potential preferences.

Shopping trip recommendations: A novel deep learning-enhanced global planning approach

Brick-and-mortar shopping malls are embracing Artificial Intelligence (AI) technology and recommender systems to enhance the shopping experience and boost mall revenue. Echoing this trend, we formulate a new shopping trip recommendation problem, which aims to recommend a shopping trip (i.e., a list of stores) that matches customer preferences and has appropriate trip lengths. To solve this problem, we develop a novel deep learning-enhanced global planning (DeepGP) approach featuring three methodological novelties. First, we introduce a new shopping intensity term based on deep neural networks to capture the variation of trip lengths specific to different shopping contexts. Second, we innovatively formulate the learning and optimization objectives in a consistent form by balancing the shopping choice likelihood and the shopping intensity likelihood, thus resolving the inconsistency issue encountered by prior global planning methods. Third, to overcome the computational challenge caused by the nonlinear shopping intensity term, we design a new exact and efficient solution technique based on piecewise linear transformations. Using a real-world offline shopping dataset, we empirically demonstrate the superior performances of our approach compared to representative benchmarks in offering more accurate and relevant shopping trip recommendations. Through a simulation, we show the capacity of our approach to attract and balance customer traffic in practical deployments. Overall, our research highlights the efficacy of combining shopping choices and shopping intensity in a consistent learning and optimization framework for offline shopping trip recommendations.

Faithful Trip Recommender Using Diffusion Guidance (Student Abstract)

Trip recommendation aims to plan user’s travel based on their specified preferences. Traditional heuristic and statistical approaches often fail to capture the intricate nuances of user intentions, leading to subpar performance. Recent deep-learning methods show attractive accuracy but struggle to generate faithful trajectories that match user intentions. In this work, we propose a DDPM-based incremental knowledge injection module to ensure the faithfulness of the generated trajectories. Experiments on two datasets verify the effectiveness of our approach.

Recommendation rules to personalize itineraries for tourists in an unfamiliar city

Currently, tourism has become increasingly related to new technologies. In this context, itinerary planning and trip recommendation are challenging tasks for tourists with different profiles. Tourists are generally not familiar with different Points-of-Interest (POIs) in a new city. To this end, they need to select and organize POIs that align with their interest preferences and trip constraints such as departure location, available trip duration, etc. as an itinerary. In this paper, we propose to extract recommendation rules using Multi-Objective Evolutionary Algorithms (MOEAs), to find a trade-off between two objectives: (1) maximizing the POIs popularity, and (2) maximizing the time user interest. We conducted a comparative study of several Multi-Objective Evolutionary Algorithms (MOEAs), such as NSGAII, SPEA2, and IBEA, based on the Flickr dataset of different cities. Our findings confirm the efficiency of NSGA-II in generating recommendation rules to personalize itineraries for tourists in unfamiliar cities.

Multi-objective reinforcement learning approach for trip recommendation

Multi-objective reinforcement learning approach for trip recommendation

A Point-of-Interest Recommender System for Tourist Groups Based on Cooperative Location Set Cover Problem

Trip recommendation for groups of tourists (TRGT) is a challenging task in tourism since many tourists travel in groups, inducing social interaction and bringing various social benefits. However, TRGT must address various real-life constraints such as limited time for touring, cost, etc. TRGT aims to design personalized tours that meet the preferences of all group members by addressing a variety of tourists’ requirements that may sometimes result in conflicts and stress for the group members. TRGT should satisfy that both the preferences of group members need to be achieved as much as possible and the preferences of group members need to be achieved as evenly as possible. In this paper, we present a methodology for tackling the TRGT problem by reducing it to the Cooperative Location Set Cover Problem (CLSCP), formulated as an integer linear program. The CLSCP aims to select a group of facilities that can satisfy, in aggregate, all demand points. To tackle the CLSCP, we present a new method based on detecting frequent patterns. We also demonstrate the efficiency of the proposed methodology by presenting extensive experimental tests.

A sequential neural recommendation system exploiting BERT and LSTM on social media posts

Tourists share opinions about Points of Interest (POIs) through online posts and social media platforms. Opinion mining is a popular technique for extracting feedback from tourists who visited various places hidden in reviews, which are used in several tourist applications that generally reflect their preference towards POI. On the other hand, a trip schema is difficult for tourists because they must pick up sequential POIs in unknown areas that meet their limitations and preferences. However, most prior trip suggestion methods are suboptimal for several reasons, including that they do not consider valuable user reviews and rely exclusively on left-to-right unidirectional discovery sequence models. This study proposes a Neural Network-Long Short-Term Memory (LSTM) POI recommendation system for calculating user similarity based on opinions and preferences. In addition, it presents a method for discovering sequential trip recommendations with Bidirectional Encoder Representations from Transformer (BERT) using a deep learning method. Furthermore, this neural hybrid framework identifies a list of optimal trip candidates by combining personalized POIs with multifaceted context. Furthermore, this method employs the valuable information contained in user posts and their demographic information on social media to mitigate the well-known cold start issue. In the experimental evaluation based on two datasets, Tripadvisor and Yelp, this hybrid method outperforms other state-of-the-art methods when considering F-Score, nDCG, RMSE, and MAP.

A design of trip recommendation robot agents with opinions

A design of trip recommendation robot agents with opinions

Motivating change in commuters’ mobility behaviour: Digital nudging for public transportation use

ABSTRACT The urgency of climate change is evident worldwide, but current mobility patterns still cause severe environmental damage . The largest share of these mobility-related problems is caused by everyday private car use, such as commuting. One way to change mobility patterns is through digital nudging in the form of trip recommendations to increase commuters’ public transportation use. To examine the effects of the recommendations, we undertake a choice-based conjoint analysis and determine whether participants choose the recommended trip option more frequently and whether the recommendations influence participants’ public transportation preferences. Our results show differences based on commuters’ travel times and mobility app use. Significant positive effects were observed among commuters with a short travel time who do not usually use mobility apps. This study contributes to the digital nudging and transportation literature, and has practical implications in terms of using recommendations to encourage switching from private cars to public transportation.

Adversarial Human Trajectory Learning for Trip Recommendation.

The problem of trip recommendation has been extensively studied in recent years, by both researchers and practitioners. However, one of its key aspects-understanding human mobility-remains under-explored. Many of the proposed methods for trip modeling rely on empirical analysis of attributes associated with historical points-of-interest (POIs) and routes generated by tourists while attempting to also intertwine personal preferences-such as contextual topics, geospatial, and temporal aspects. However, the implicit transitional preferences and semantic sequential relationships among various POIs, along with the constraints implied by the starting point and destination of a particular trip, have not been fully exploited. Inspired by the recent advances in generative neural networks, in this work we propose DeepTrip-an end-to-end method for better understanding of the underlying human mobility and improved modeling of the POIs' transitional distribution in human moving patterns. DeepTrip consists of: a trip encoder (TE) to embed the contextual route into a latent variable with a recurrent neural network (RNN); and a trip decoder to reconstruct this route conditioned on an optimized latent space. Simultaneously, we define an Adversarial Net composed of a generator and critic, which generates a representation for a given query and uses a critic to distinguish the trip representation generated from TE and query representation obtained from Adversarial Net. DeepTrip enables regularizing the latent space and generalizing users' complex check-in preferences. We demonstrate, both theoretically and empirically, the effectiveness and efficiency of the proposed model, and the experimental evaluations show that DeepTrip outperforms the state-of-the-art baselines on various evaluation metrics.

Trip Reinforcement Recommendation with Graph-based Representation Learning

Tourism is an important industry and a popular leisure activity involving billions of tourists per annum. One challenging problem tourists face is identifying attractive Places-of-Interest (POIs) and planning the personalized trip with time constraints. Most of the existing trip recommendation methods mainly consider POI popularity and user preferences, and focus on the last visited POI when choosing the next POI. However, the visit patterns and their asymmetry property have not been fully exploited. To this end, in this article, we present a GRM-RTrip (short for G raph-based R epresentation M ethod for R einforce Trip Recommendation) framework. GRM-RTrip learns POI representations from incoming and outgoing views to obtain asymmetric POI-POI transition probability via POI-POI graph networks, and then fuses the trained POI representation into a user-POI graph network to estimate user preferences. Finally, after formulating the personalized trip recommendation as a Markov Decision Process (MDP), we utilize a reinforcement learning algorithm for generating a personalized trip with maximal user travel experience. Extensive experiments are performed on the public datasets and the results demonstrate the superiority of GRM-RTrip compared with the state-of-the-art trip recommendation methods.

GC-TripRec: Graph contextualized generative network with adversarial learning for trip recommendation

GC-TripRec: Graph contextualized generative network with adversarial learning for trip recommendation

Dual-grained human mobility learning for location-aware trip recommendation with spatial–temporal graph knowledge fusion

Trip recommendation is a popular and significant location-aware service that can help visitors make more accurate travel plans. Its principal purpose is to provide a sequence of points of interest (POIs) for a visitor who has a specific demand, such as the number of attractions, start location, end location, etc. Previous studies either regard the trip recommendation task as a simplistic orienteering problem or endeavor to maximize the users’ visiting preferences and transitional regularities in a multi-round iteration manner. However, significant contexts such as long-term transitional dependencies and spatial–temporal correlations are under-explored. Although the emerging deep recursive models (e.g., recurrent neural networks) enable us to relieve the above limitations, there still exist three major challenges: (1) most conventional offline-homogeneous knowledge distillation approaches for key entities (e.g, POI and visiting time) could lead to the inadequacy of capturing the inherent heterogeneous interactions among the different entities and even result in the deviation of understanding human real individual preferences; (2) data sparsity in transitional regularity learning impedes the ability to comprehend human diverse mobility patterns; and (3) the lack of considering contextual facts such as attractive bias would degrade the generalization ability of the model. To remedy the above issues, this work presents a novel framework based on spatial–temporal graph representation learning, namely GraphTrip. Specifically, we first introduce a location-aware information fusion after building the spatial–temporal graph (ST-Graph). Then, a dual-grained human mobility learning module is proposed to address the sparsity of periodic regularity. In addition, we fuse the explicit information (e.g., POI popularity) behind the trip data as prior knowledge to facilitate the performance of trip inference. In the end, the experimental results conducted on five real-world trip datasets demonstrate our proposed GraphTrip achieves promising gains against several cutting-edge baselines, e.g., up to 5.75% and 4.45% improvements on Toronto regarding F1 and pairs-F1.

A Fortunate Refining Trip Recommendation Model

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.

A Serendipity-Oriented Personalized Trip Recommendation Model

Personalized trip recommendation attempts to recommend a sequence of Points of Interest (POIs) to a user. Compared with a single POI recommendation, the POIs sequence recommendation is challenging. There are only a couple of studies focusing on POIs sequence recommendations. It is a challenge to generate a reliable sequence of POIs. The two consecutive POIs should not be similar or from the same category. In developing the sequence of POIs, it is necessary to consider the categories of consecutive POIs. The user with no recorded history is also a challenge to address in trip recommendations. Another problem is that recommending the exact and accurate location makes the users bored. Looking at the same kind of POIs, again and again, is sometimes irritating and tedious. To address these issues in recommendation lies in searching for the sequential, relevant, novel, and unexpected (with high satisfaction) Points of Interest (POIs) to plan a personalized trip. To generate sequential POIs, we will consider POI similarity and category differences among consecutive POIs. We will use serendipity in our trip recommendation. To deal with the challenges of discovering and evaluating user satisfaction, we proposed a Serendipity-Oriented Personalized Trip Recommendation (SOTR). A compelling recommendation algorithm should not just prescribe what we are probably going to appreciate but additionally recommend random yet objective elements to assist with keeping an open window to different worlds and discoveries. We evaluated our algorithm using information acquired from a real-life dataset and user travel histories extracted from a Foursquare dataset. It has been observationally confirmed that serendipity impacts and increases user satisfaction and social goals. Based on that, SOTR recommends a trip with high user satisfaction to maximize user experience. We show that our algorithm outperforms various recommendation methods by satisfying user interests in the trip.

Self-supervised representation learning for trip recommendation

Trip recommendation is a significant and engaging location-based service that can help new tourists make more customized travel plans. It often attempts to suggest a sequence of points of interest (POIs) for a user who requests a personalized travel demand. Conventional methods either leverage the heuristic algorithms (e.g., dynamic programming) or statistical analysis (e.g., Markov models) to search or rank a POI sequence. These procedures may fail to capture the diversity of human needs and transitional regularities. They even provide recommendations that deviate from tourists’ real travel intention when the trip data is sparse. Although recent deep recursive models (e.g., RNN) are capable of alleviating these concerns, existing solutions hardly recognize the practical reality, such as the diversity of tourist demands, uncertainties in the trip generation, and the complex visiting preference. Inspired by the advance in deep learning, we introduce a novel self-supervised representation learning framework for trip recommendation — SelfTrip, aiming at tackling the aforementioned challenges. Specifically, we propose a two-step contrastive learning mechanism concerning the POI representation, as well as trip representation. Furthermore, we present four trip augmentation methods to capture the visiting uncertainties in trip planning. We evaluate our SelfTrip on four real-world datasets, and extensive results demonstrate the promising gain compared with several cutting-edge benchmarks, e.g., up to 4% and 10% improvements on Osaka regarding F1 and pair-F1.

A novel Sequence-Aware personalized recommendation system based on multidimensional information

Due to the rapid growth of the information overload issue, recommender systems have become necessary and are implemented in numerous facets of human life, including the tourism industry. Today, technological advancements have significantly altered our travels, and these advancements promise even more interactive and exciting experiences in the future. Nowadays, planning and arranging a customized trip well in advance is ideal, as the process can be challenging and time-consuming. This paper proposes a hybrid approach that leverages multidimensional data to enhance personalized trip recommendations while addressing several shortcomings of existing recommender systems, such as their inability to account for dynamic user preferences and diverse contexts. To this end, the proposed method, considering the data sparsity issue, employs a clustering algorithm to reduce the time complexity of discovering Points of Interest (POIs). This approach utilizes user demographic information to address the cold start issue while improving the collaborative filtering (CF) paradigm through an asymmetric schema. Furthermore, this study exploits the context vector model and the Term-Frequency-Inverse-Document-Frequency (TF-IDF) algorithm to present a novel method for quantifying context similarity. Moreover, the method retrieves and ranks a list of candidate routes optimized by applying personalized POIs to sequential travel patterns. Finally, the experimental results demonstrate the superiority of the approach in terms of Precision, Recall, RMSE, MAP, F-Score, and nDCG compared to previous works based on Flickr and STS datasets.

Recommending Reforming Trip to a Group of Users

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.

Distributing Tourists among POIs with an Adaptive Trip Recommendation System

Traveling is part of many people leisure activities and an increasing fraction of the economy comes from the tourism. Given a destination, the information about the different attractions, or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">points of interest</i> (POIs), can be found on many sources. Among these attractions, finding the ones that could be of interest for a specific user, who may have different constraints—such as the available time or budget—represents a challenging task. Travel recommendation systems deal with this type of problems. Despite the vast literature on this topic, most of the solution does not take into account the impact of the suggestions on the level of crowding of POIs. This paper considers the trip planning problem focusing on user balancing among the different POIs. To this aim, we consider the effects of the previous recommendations, as well as estimates based on historical data, while devising a new recommendation. The problem is formulated as a multi-objective optimization problem, and a recommendation engine has been designed and implemented for exploring the solution space in near real-time, through a distributed version of the Simulated Annealing approach. We test our solution using a real dataset of users visiting the POIs of a touristic city, and we show that we are able to provide high quality recommendations, yet maintaining the attractions not overcrowded.

Road Trip Recommendation System Using User Preferences

Travel planning is essential for everyone in road trip to make the journey memorable one. To have a joyful journey, users need to know the details of popular site seeing places to visit on the way to their destination. Due to the growth of the mobile technology, people tend to utilize their time in planning by searching through their handheld devices like mobile phone, palm tops etc. The Road Trip Recommendation System is an application that helps to plan the trip effectively by using the user preferences. This system takes source, destination and users' interests/preferences as the input to generate the most optimal path from the source to the destination considering most of the places which falls under the users‟ interest along the way. This generated optimal path is displayed as recommended route map to the user including the intermediate places. In addition to generating the most optimal path, this system also recommends the best season to visit the places selected in the route using decision tree classifier. This helps the users to decide the best month to travel from source to destination so that the user will get the most out of the trip. This system minimizes the planning time of the travelers by integrating both the generation of the optimal path and prediction of the best months to travel.