Efficient Keyword-Aware Representative Travel Route Recommendation (original) (raw)
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With the popularity of social media (e.g., Face book and Flicker), users can easily share their check-in records and photos during their trips. In view of the huge number of user historical mobility records in social media, we aim to discover travel experiences to facilitate trip planning. When planning a trip, users always have specific preferences regarding their trips. Instead of restricting users to limited query options such as locations, activities, or times, we consider arbitrary text descriptions as keywords about personalized requirements. Moreover, a diverse and representative set of recommended travel routes is needed. Prior works have elaborated on mining and ranking existing routes from check-in data. To meet the need for automatic trip organization, we claim that more features of Places of Interest (POIs) should be extracted. Therefore, in this paper, we propose an efficient Keyword-aware Representative Travel Route framework that uses knowledge extraction from users' historical mobility records and social interactions. Explicitly, we have designed a keyword extraction module to classify the POI-related tags, for effective matching with query keywords. We have further designed a route reconstruction algorithm to construct route candidates that fulfill the requirements. To provide befitting query results, we explore Representative Skyline concepts, that is, the Skyline routes which best describe the trade-offs among different POI features. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experiment results show that our methods do indeed demonstrate good performance compared to state-of-the-art works.
cegon technologies, 2019
With the popularity of social media (e.g., Facebook and Flicker), users can easily share their check-in records and photosduring their trips. In view of the huge number of user historical mobility records in social media, we aim to discover travel experiencesto facilitate trip planning. When planning a trip, users always have specific preferences regarding their trips. Instead of restricting usersto limited query options such as locations, activities or time periods, we consider arbitrary text descriptions as keywords aboutpersonalized requirements. Moreover, a diverse and representative set of recommended travel routes is needed. Prior works haveelaborated on mining and ranking existing routes from check-in data. To meet the need for automatic trip organization, we claim thatmore features of Places of Interest (POIs) should be extracted. Therefore, in this paper, we propose an efficient Keyword-awareRepresentative Travel Route framework that uses knowledge extraction from users' historical mobility records and social interactions.Explicitly, we have designed a keyword extraction module to classify the POI-related tags, for effective matching with query keywords.We have further designed a route reconstruction algorithm to construct route candidates that fulfill the requirements. To providebefitting query results, we explore Representative Skyline concepts, that is, the Skyline routes which best describe the trade-offsamong different POI features. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensiveexperiments on real location-based social network datasets, and the experiment results
Efficient Keyword -Aware Skyline Travel Route Recommendation
— With the popularity of social media (e.g., Facebook and Flicker), users might simply share their arrival records and photos throughout their visits .Visible of the massive quantity of checking data and photos in social media, system have a tendency to will discover travel experiences to facilitate trip coming up with. Prior works are elaborated on mining and ranking existing travel routes from check-in knowledge. System has a tendency to observe that once coming up with a visit, users may have some keywords concerning preference on his/her visits. To provide a diverse set of travel routes, System have a tendency to claim that a lot of options of Places of Interests (POIs) should be extracted. Therefore, in system, a Keyword-aware Skyline Travel Route (KSTR) framework that uses data extraction from historical mobility records and also the user's social interactions. Explicitly, system model the Where, When, Who problems by featuring the geographical quality pattern, temporal influence and social influence. Than a keyword extraction module to classify the POI-related tags mechanically into differing types, for effective matching with question keywords. Additional style a route reconstruction algorithmic rule to construct route candidates that fulfill the question inputs. To produce numerous question results, and explore Skyline ideas to rank routes.
A Novel Approach for Optimal Travel Route Search using Spatial Keyword Recommendation
Optimal course look utilizing spatial keywordquery concentrate on keyword searching utilizing best keyword cover query which is a type of spatial keywordquery. Earlier works have expounded on mining and positioning existing courses from registration information. To address the issue for programmed trip association, assert that more highlights of Places of Interest (POIs) ought to be removed. Consequently, propose a productive Keyword-aware they Delegate Travel Route structure that utilizations information extraction from clients' verifiable versatility records and social collaborations. Unequivocally, whit outlined a keyword extraction module to arrange the POI-related labels, for compelling coordinating with query keywords. They have additionally outlined a course remaking algorithm to build course hopefuls that satisfy the prerequisites. To give befitting query comes about, investigate Representative Skyline ideas, that is, the Skyline courses which best portray the exchange offs among various POI highlights. To assess the viability and effectiveness of the proposed algorithms, have led broad investigates genuine location based informal organization datasets, and the examination comes about demonstrate that our strategies do undoubtedly illustrate great performance contrasted with cutting edge works.
Personalized Tour Recommendation using Location-based Social Media
PhD Thesis, 2017
Tourism is a popular leisure activity and an important industry, where the main task involves visiting unfamiliar Places-of-Interest (POI) in foreign cities. Recommending POIs and tour planning are challenging and time-consuming tasks for tourists due to: (i) the need to identify and recommend captivating POIs in an unfamiliar city; (ii) having to schedule POI visits as a connected itinerary that satisfies trip constraints such as starting/ending near a specific location (e.g., the tourist's hotel) and completing the itinerary within a limited touring duration; and (iii) having to satisfy the diverse interest preferences of each unique tourist. While tourism-related information can be obtained from the Internet, travel guides and tour agencies, many of these resources simply recommend individual POIs or popular itineraries, but otherwise do not appeal to the interest preferences of users or adhere to their trip constraints. In contrast to existing works on next-POI prediction and top-k POI recommendation that recommend a single POI or a ranked list of POIs, the task of tour recommendation involves the need to identify a set of interesting POIs and schedule them as an itinerary with various time and space constraints. While there are works on path planning that recommend an itinerary, this itinerary is typically optimized based on a global utility such as POI popularity, and thus offer no personalization for a tourist based on his/her interest preferences. This thesis addresses the challenges associated with the automation and personalization of tour recommendation using data mining techniques to model user interest and POI-related information, and using optimization problems and techniques to formulate and solve more realistic tour recommendation problems. Our main contributions include: 1.) Proposing and implementing a framework that utilizes Flickr geo-tagged photos and Wikipedia to automatically determine user trajectories, interest preferences and POI-related information such as POI popularity and visiting times. 2.) Proposing the PersTour algorithm for recommending personalized tour itineraries based on POI popularity, users' interest preferences and trip constraints, where POI visit durations are customized based on user interests. 3.) Formulating the QueueTourRec problem for recommending queue-aware and personalized itineraries that schedule visits to popular and interesting POIs at times with minimal queuing times, and proposing a novel implementation of Monte Carlo Tree Search to solve this problem. 4.) Developing the TourRecInt algorithm for tour recommendation based on a variant of the Orienteering problem with a mandatory POI category, which is defined as the POI category that a tourist has most frequently visited. 5.) Formulating and solving the novel GroupTourRec problem, which involves recommending tour itineraries to groups of tourists with diverse interests and assigning tour guides with the right expertise to lead each tour group. 6.) Illustrating the application of our proposed approach in practice, by presenting a web-based system implementation of our PersTour algorithm, with the front-end component developed using HTML, PHP, jQuery and the Google Maps API, and the back-end based on Python, Java and PHP.
World Academy of Research in Science and Engineering, 2019
Social media like Facebook and Flicker are most popular in these days, so that sharing of images and visited records by users can be easy. The system aim to make an easy trip planning based upon travel history of users records. Users have their own views, requirements and preferences for their planning trips. So that here introducing text description as keywords instead of limited queries. Also different set of travel route recommendations are needed. Large amount of recorded data mining and ranked preferences are the main expanded version of already existing system to get an automatic system.Therefore, proposing an Efficient Travel Route Recommendation System Based On Keyword that uses data from users trip and travelling records. So in this system for the classification of places of interests uses a keyword extraction module, for accurate and matching with query keywords. An algorithm called route reconstruction is developed to make route of candidate preferences. To get the results, the system used Skyline routing concepts. And for the verification of genuinity of rated locations here introducing NER method.
Social itinerary recommendation from user-generated digital trails
2012
Abstract Planning travel to unfamiliar regions is a difficult task for novice travelers. The burden can be eased if the resident of the area offers to help. In this paper, we propose a social itinerary recommendation by learning from multiple user-generated digital trails, such as GPS trajectories of residents and travel experts. In order to recommend satisfying itinerary to users, we present an itinerary model in terms of attributes extracted from user-generated GPS trajectories.
Road-based travel recommendation using geo-tagged images
Geotagged photos on social media like Flickr explicitly indicate the trajectories of tourists. They can be employed to reveal the tourists’ preference on landmarks and routings of tourism. Most of existing works on routing searches are based on the trajectories of GPS-enabled devices’ users. From a distinct point of view, we attempt to propose a novel approach in which the basic unit of routing is separate road segment instead of GPS trajectory segment. In this paper, we build a recommendation system that provides users with the most popular landmarks as well as the best travel routings between the landmarks. By using Flickr geotaggged photos, the top ranking travel destinations in a city can be identified and then the best travel routes between the popular travel destinations are recommended. We apply a spatial clustering method to identify the main travel landmarks and subsequently rank these landmarks. Using machine learning method, we calculate the tourism popularity of the road in terms of relevant parameters, e.g., the number of users and the number of Point-of-Interests. These popularity assessments are integrated into the routing recommendation system. The routing recommendation system takes into consideration both the popularity assessment and the length of the road. The best route recommended to the user minimizes the distance while including maximal tourism popularity. Experiments were conducted in two different scenarios. The empirical results show that the recommendation system is able to provide the user good travel planning including both top ranking landmarks and suitable routings in a city. Besides, the system offers user-generated semantic information for the recommended routes.
Graph-Based Approach for Personalized Travel Recommendations
Transport and Telecommunication Journal
In the evolving domain of urban mobility systems, the integration of technology with user-centric strategies is pivotal. This research stands on the foundational concept of Mobility-as-a-Service, a user-centric intelligent mobility management distribution system that seeks to prioritize human needs over mere technological infrastructure. The study delves deep into the wealth of data available through mobile sensing technologies, highlighting the unprecedented understanding it offers into human mobility patterns, thus facilitating personalized route recommendations. The literature categorizes the study area into three interlinked categories: point-of-interest (POI) recommendation, travel planning, and trajectory modelling. In a significant stride, this research introduces a comprehensive understanding of hu-man mobility data and proposes a novel framework designed to tender personalized recommendations to travel planner users. The innovative framework employs a graph-based approach r...
Location recommendation in location-based social networks using user check-in data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL'13, 2013
This paper studies the problem of recommending new venues to users who participate in location-based social networks (LBSNs). As an increasingly larger number of users partake in LBSNs, the recommendation problem in this setting has attracted significant attention in research and in practical applications. The detailed information about past user behavior that is traced by the LBSN differentiates the problem significantly from its traditional settings. The spatial nature in the past user behavior and also the information about the user social interaction with other users, provide a richer background to build a more accurate and expressive recommendation model.