Personalized Travel Route Research Articles

DeepSneak: User GPS Trajectory Reconstruction from Federated Route Recommendation Models

Decentralized machine learning, such as Federated Learning (FL), is widely adopted in many application domains. Especially in domains like recommendation systems, sharing gradients instead of private data has recently caught the research community’s attention. Personalized travel route recommendation utilizes users’ location data to recommend optimal travel routes. Location data is extremely privacy sensitive, presenting increased risks of exposing behavioural patterns and demographic attributes. FL for route recommendation can mitigate the sharing of location data. However, this paper shows that an adversary can recover the user trajectories used to train the federated recommendation models with high proximity accuracy. To this effect, we propose a novel attack called DeepSneak, which uses shared gradients obtained from global model training in FL to reconstruct private user trajectories. We formulate the attack as a regression problem and train a generative model by minimizing the distance between gradients. We validate the success of DeepSneak on two real-world trajectory datasets. The results show that we can recover the location trajectories of users with reasonable spatial and semantic accuracy.

A Personalized Recommendation Technique for Travel Route Based on Fuzzy Consistent Matrix

Recommending travel routes for tourists has become a research hotspot in the present tourism industry. Various machine learning methods have been well studied for recommendation systems. However, the accuracy of these systems is not up to the expected levels. In order to increase the ranking accuracy, as well as, the recall rate of the recommended routes by the personalized travel recommendation algorithm, in this paper, a fuzzy consistent matrix technique is introduced for personalized travel recommendation systems. First of all, the user’s travel interest data are obtained through the fuzzy consistency matrix. Next, the user’s travel interest data are de-noised to determine the user’s preferred interest points and the coverage of the user’s interest points. Besides this, the constraints of the route recommendation systems have also become a research topic. Therefore, the proposed method also uses the fuzzy consistent matrix technology to determine the users’ similarity and classifies the personalized recommendation algorithm. Finally, a personalized travel recommendation model is constructed to comprehend the personalized travel route recommendation. The investigational outcomes, using real datasets, illustrate that when the number of routes is 5000, then the accuracy rate of the proposed method is approximately 97.21%, and the accuracy rate of the recommendation ranking is 97.54%. We also observed that when using 60 GB of data, the recall rate of the proposed method, for personalized travel recommendation routes, can reach as high as 97.59%.

A Personalized Travel Route Recommendation Model Using Deep Learning in Scenic Spots Intelligent Service Robots

This paper proposes a personalized tourist interest demand recommendation model based on deep neural network. Firstly, the basic information data and comment text data of tourism service items are obtained by crawling the relevant website data. Furthermore, word segmentation and word vector transformation are carried out through Jieba word segmentation tool and Skip-gram model, the semantic information between different data is deeply characterized, and the problem of very high vector sparsity is solved. Then, the corresponding features are obtained by using the feature extraction ability of DNN’s in-depth learning. On this basis, the user’s score on tourism service items is predicted through the model until a personalized recommendation list is generated. Finally, through simulation experiments, the recommendation accuracy and average reciprocal ranking of the proposed algorithm model and the other two algorithms in three different databases are compared and analyzed. The results show that the overall performance of the proposed algorithm is better than the other two comparison algorithms.

Personalized Travel Route Recommendation Model of Intelligent Service Robot Using Deep Learning in Big Data Environment

Aiming at the problems that the traditional model is difficult to extract information features, difficult to learn deep knowledge, and cannot automatically and effectively obtain features, which leads to the problem of low recommendation accuracy, this paper proposes a personalized tourism route recommendation model of intelligent service robot using deep learning in a big data environment. Firstly, by crawling the relevant website data, obtain the basic information data and comment the text data of tourism service items, as well as the basic information data, and comment the text data of users and preprocess them, such as data cleaning. Then, a neural network model based on the self-attention mechanism is proposed, in which the data features are obtained by the Gaussian kernel function and node2vec model, and the self-attention mechanism is used to capture the long-term and short-term preferences of users. Finally, the processed data is input into the trained recommendation model to generate a personalized tourism route recommendation scheme. The experimental analysis of the proposed model based on Pytorch deep learning framework shows that its Pre@10, Rec@10 values are 88% and 83%, respectively, and the mean square error is 1.537, which are better than other comparison models and closer to the real tourist route of the tourists.

Personalized Travel Recommendation Based on the Fusion of TGI and POI Algorithms

On the way to travel, the public expect to get a tourism experience with low cost, convenient travel, and high comfort. At the same time, they also have different tourism needs such as history and culture, natural landscape, and food shopping. To address the problem that traditional travel route recommendation algorithms have limited accuracy and only analyze text or pictures alone, we propose a personalized travel route recommendation algorithm that integrates text and photo information from travelogues and obtain the historical tourism footprint of tourists by analyzing travel notes. According to the frequency and cooccurrence of scenic spots in the travel notes and the number of photos taken by each scenic spot, the popularity of scenic spots and the interest preferences of various types of tourists are analyzed. Under the given starting and ending points or passing points, the optimal tourism route generation method is designed. Experiments on the real data set of Ctrip Travel website show that the recommendation accuracy of this algorithm is significantly improved compared with the traditional algorithm which only uses travel notes text or photos. Compared with the algorithm that only considers the popularity of interest points or tourists’ interest preferences, the accuracy of the route recommended by the algorithm is improved. Compared with the algorithm that only considers the cooccurrence of scenic spots or only considers the influence of photos, this algorithm can obtain a better popularity score of scenic spots. This method integrates the two kinds of information including picture and text, fully considering the interest of users with high practicability.

Research on the Realization of Travel Recommendations for Different Users Through Deep Learning Under Global Information Management

This article is mainly to study the realization of travel recommendations for different users through deep learning under global information management. The personalized travel route recommendation is realized by establishing personalized travel dynamic interest (PTDR) algorithm and distributed lock manager (DLM) model. It is hoped that this model can provide more complete data information of tourist destinations on the basis of the past, and can also meet the needs of users. The innovation of this article is to compare and analyze with a large number of baseline algorithms, highlighting the superiority of this model in personalized travel recommendation. In addition, the model incorporates the topic factor features, geographic factor features, and user preference features to make the data more in line with user needs and improve the efficiency and applicability of the model. It is hoped that the plan proposed in this article can help users make choices of tourist destinations more conveniently.

RETRACTED ARTICLE: A user preference tree based personalized route recommendation system for constraint tourism and travel

Personalized recommendation systems recommend the target destination based on user-generated data from social media and geo-tagged photos that are currently available as a most pertinent source. This paper proposes a tourism destination recommendation system which uses heterogeneous data sources that interprets both texts posted on social media and images of tourist places visited and shared by tourists. For this purpose, we propose an enhanced user profile that uses user-location vector with LDA and Jaccard coefficients. Moreover, a new tourist destination tree is constructed using the posts extracted from trip advisor where each node of the destination tree consists of tourist destination data. Finally, we build a personalized recommendation system based on user preferences, A* algorithm and heuristic shortest path algorithm with cost optimization based on the backtracking based traveling salesman problem solution, tourist destination tree and tree-based hybrid recommendations. Here, the 0/1 knapsack algorithm is used for recommending the best tourist destination travel route plans according to the travel time and cost constraints of the tourists. The experimental results obtained from this work depict that the proposed user centric personalized destination and travel route recommendation system is providing better recommendation of tourist places than the existing systems by handling multiple heterogeneous data sources efficiently for recommending optimal tour plans with minimum cost and time.

Personalized travel route recommendation algorithm based on improved genetic algorithm

The analysis of user trajectory information and social relationships in social media, combined with the personalization of travel needs, allows users to better plan their travel routes. However, existing methods take only local factors into account, which results in a lack of pertinence and accuracy for the recommended route. In this study, we propose a method by which user clustering, improved genetic, and rectangular region path planning algorithms are combined to design personalized travel routes for users. First, the social relationships of users are analyzed, and close friends are clustered into categories to obtain several friend clusters. Next, the historical trajectory data of users in the cluster are analyzed to obtain joint points in the trajectory map, these are matched according to the keywords entered by users. Finally, the search area is narrowed and the recommended travel route is obtained through improved genetic and rectangular region path planning algorithms. Theoretical analyses and experimental evaluations show that the proposed method is more accurate at path prediction and regional coverage than other methods. In particular, the average area coverage rate of the proposed method is better than that of the existing algorithm, with a maximum increasement ratio of 31.80%.

A Tourism Route-Planning Approach Based on Comprehensive Attractiveness

In recent years, “free travel” has been increasingly popular. How to plan personalized travel routes based on the perspective of tourists, rather than that of tourism intermediaries, is in great need. However, some factors reflecting tourists’ preferences are ignored in the related work. What’s more, the evaluation about scenic spots is incomplete. Besides, real data sets are seldom used in existing works. We propose a novel route-planning method that considerate multiple factors (that is, the distance between sites, initial travel position, initial departure time, time duration of tour, total cost, scores and popularities of sites) comprehensively, and routes were rated by what we call a comprehensive attractiveness index. We conducted comprehensive case studies based on the real-world data of sites from the Baidu and Xiecheng websites and found that our proposed method is feasible. It is also found that the genetic algorithm outperformed two baseline ones in terms of run time.

Personalized travel route recommendation from multi-source social media data

Personalized travel route recommendation aims to recommend tourist attractions based on user’s interest and generate routes which can be viewed as a recommendation and sequence task. How to build interest model for users to find personalized attractions and generate the location sequence brings great challenges. In this work, multi-source social media (e.g., travelogues and check-in records) are leveraged to find user interests and model route attributes. In order to fuse multi-source data in a unify metric space, a topical package is built as the measurement space. Then, a long short-term memory (LSTM) based method is used to generate some candidate positions based on sparse user-specified inputs. Finally, top ranked routes are recommended as final results. The proposed approach combining multi-source social based topical package and LSTM is evaluated on a real travel dataset and compared with three state-of-the-art methods. The experimental result shows that our method performs better for providing personalized travel routes.

Personalized travel route recommendation using collaborative filtering based on GPS trajectories

ABSTRACTTravelling is a critical component of daily life. With new technology, personalized travel route recommendations are possible and have become a new research area. A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations, based on the road networks and users’ travel preferences. In this paper, we define users’ travel behaviours from their historical Global Positioning System (GPS) trajectories and propose two personalized travel route recommendation methods – collaborative travel route recommendation (CTRR) and an extended version of CTRR (CTRR+). Both methods consider users’ personal travel preferences based on their historical GPS trajectories. In this paper, we first estimate users’ travel behaviour frequencies by using collaborative filtering technique. A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model. The CTRR+ method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability. This paper also conducts some case studies based on a real GPS trajectory data set from Beijing, China. The experimental results show that the proposed CTRR and CTRR+ methods achieve better results for travel route recommendations compared with the shortest distance path method.

Exploring Personalized Travel Route Using POIs

Exploring Personalized Travel Route Using POIs

Research on Personalized Travel Route Recommendation from Social Image Community

Research on Personalized Travel Route Recommendation from Social Image Community

Ontological recommendation multi-agent for Tainan City travel

Due to the gradual increase in travel, the travel agent plays an important role in both planning and recommending a personalized travel route. Tainan City, located in the southern Taiwan, is famous for its abundant historic sites and delicious snack food, and it has been one of the top tourist attractions in Taiwan for years. In this paper, we propose an ontological recommendation multi-agent for Tainan City travel. The core technologies of the agent contain the ontology model, fuzzy inference mechanism, and ant colony optimization. The proposed agent can recommend the tourist a personalized travel route to enjoy Tainan City according to the tourist’s requirements. It includes a context decision agent and a travel route recommendation agent. First, the context decision agent finds a suitable location distance, counts the context relation, and infers the context information based on the tourist’s requirements and Tainan City travel ontology. Next, the travel route recommendation agent is responsible for finding a personalized tour and plotting this travel route on the Google Map. Finally, the tourist can follow the personalized travel route to enjoy the cultural heritage and the local gourmet food during his stay at Tainan City. The experimental results show that the proposed approach can effectively recommend a travel route matched with the tourist’s requirements.