Modeling Personalized Recommendations of Unvisited Tourist Places Using Genetic Algorithms
23 Mar 2015-pp 264-276
TL;DR: A novel approach based on Genetic Algorithm (GA) to model the interest of user for unvisited location and the recommendation results are comparable with matrix factorization based approach and shows improvement of 4.1 % on average root mean squared error (RMSE).
Abstract: Immense amount of data containing information about preferences of users can be shared with the help of WWW and mobile devices. The pervasiveness of location acquisition technologies like Global Positioning System (GPS) has enabled the convenient logging of movement histories of users. GPS logs are good source to extract information about user’s preferences and interests. In this paper, we first aim to discover and learn individual user’s preferences for various locations they have visited in the past by analyzing and mining the user’s GPS logs. We have used the GPS trajectory dataset of 178 users collected by Microsoft Research Asia’s GeoLife project collected in a period of over four years. These preferences are further used to predict individual’s interest in an unvisited location. We have proposed a novel approach based on Genetic Algorithm (GA) to model the interest of user for unvisited location. The two approaches have been implemented using Java and MATLAB and the results are compared for evaluation. The recommendation results of proposed approach are comparable with matrix factorization based approach and shows improvement of 4.1 % (approx.) on average root mean squared error (RMSE).
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TL;DR: In this paper, a mapping science analysis of publications on the movement of tourists and traceability has been carried out in the two main sources WOS and SCOPUS, and a set of methodological considerations and a classification of information capture tools are proposed.
Abstract: The tracking of tourist movements is an essential aspect in the management of sustainable tourist destinations. The current information and communication technologies provide innovative ways of collecting data on tourist movements, but it is still necessary to evaluate tools and methods of study for this challenge. At this point, mobile technologies are the best candidate for this task. Given the relevance of the topic, this paper proposes a mapping science analysis of publications on “movement of tourists” and “traceability.” It has been carried out in the two main sources WOS and SCOPUS. The term “traceability” is brought from industry and technology areas to be applied to the tourist movement/mobility tracking and management. The methodological scheme is based on a selection of search criteria with combinations of terms. The sources of specialized information in applied social sciences and technology were then selected. From there, the searches have been executed for their subsequent analysis in three stages—(I) relevance analysis filtering the results to obtain the most pertinent; (II) analysis of articles with similarity thematic, authors, journals or citations; (III) analysis of selected papers as input for the mapping analysis using Citespace. The automatic naming of clusters under the selected processing confirms that the analysis of movements is a valid scientific trend but research-oriented from the perspective of traceability is non-existent, so this approach is novel and complementary to existing ones and a potential contribution to knowledge about tourist movements. Finally, a set of methodological considerations and a classification of information capture tools are proposed. In this classification, mobile technology is the best option to enable tourist movement analysis.
27 citations
TL;DR: Implicitly infer user interest with good accuracy is proposed here and this understanding of interests can later be updated by observing user actions as they interact with the system.
9 citations
02 May 2018
TL;DR: This work proposes an approach to recommend the personalized news to the users based on their individual preferences and believes that the interest of the user, popularity of article and other attributes of news are implicitly fuzzy in nature and therefore this is exploited for generating the recommendation score for articles to be recommended.
Abstract: The mobile and handheld devices have become an indispensable part of life in this era of technological advancement. Further, the ubiquity of location acquisition technologies like global positioning system (GPS) has opened the new avenues for location aware applications for mobile devices. Reading online news is becoming increasingly popular way to gather information from news sources around the globe. Users can search and read the news of their preference wherever they want. The news preferences of individuals are influenced by several factors including the geographical contexts and the recent trends on social media. In this work we propose an approach to recommend the personalized news to the users based on their individual preferences. The model for user preferences are learned implicitly for individual users. Also, the popularity of trending articles floating around the twitter are exploited to provide news interesting recommendations to the user. We believe that the interest of the user, popularity of article and other attributes of news are implicitly fuzzy in nature and therefore we propose to exploit this for generating the recommendation score for articles to be recommended. The prototype is developed for testing and evaluation of proposed approach and the results of the evaluation are motivating.
5 citations
02 May 2018
TL;DR: This work proposes an approach to learn implicit user preferences by making use of YouTube Video Tags, which is generic and may be used for a wide variety of domains of recommender systems.
Abstract: Recommender systems have become essential in several domains to deal with the problem of information overload. Collaborative filtering is one of the most popularly used paradigm of recommender systems for over a decade. The personalized recommender systems use past preference history of the users to make future recommendations for them. The cold start problem of recommender system concerns with the personalized recommendation to the users having no or few past history. In this work we propose an approach to learn implicit user preferences by making use of YouTube Video Tags. The profile of a new user is created from his/her preferences in watching the YouTube videos. This profile is generic and may be used for a wide variety of domains of recommender systems. In this work we have used it for a biography recommender system. However this may be used for several other types of recommender system.
4 citations
24 May 2019
TL;DR: It can be shown that evolutionary algorithms can lead to a significant improvement with respect to its predictive performance, as well as to the time needed for the model’s optimization.
Abstract: Location prediction has gained enormously in importance in the recent years. For this reason, there exists a great variety of research work carried out at both the academia and the industry. At the same time, there is an increasing trend towards utilizing additional semantic information aiming at building more accurate algorithms. Existing location prediction approaches rely mostly on data-driven models, such as Hidden Markov Chains, Bayes Networks and Artificial Neural Networks (ANN), with the latter achieving usually the best results. Most ANN-based solutions apply Grid Parameter Search and Stochastic Gradient Descent for training their models, that is, for identifying the optimal structure and weights of the network. In this work, motivated by the promising results of genetic algorithms in optimizing neural networks in temporal sequence learning areas, such as the gene and the stock price index prediction, we propose and evaluate their use in optimizing our ANN-based semantic location prediction model. It can be shown that evolutionary algorithms can lead to a significant improvement with respect to its predictive performance, as well as to the time needed for the model’s optimization.
4 citations
References
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20 Apr 2009
TL;DR: This work first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG), and proposes a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location.
Abstract: The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.
1,903 citations
TL;DR: The Cyberguide project is presented, in which the authors are building prototypes of a mobile context‐aware tour guide that is used to provide more of the kind of services that they come to expect from a real tour guide.
Abstract: Future computing environments will free the user from the constraints of the desktop. Applications for a mobile environment should take advantage of contextual information, such as position, to offer greater services to the user. In this paper, we present the Cyberguide project, in which we are building prototypes of a mobile context-aware tour guide. Knowledge of the user's current location, as well as a history of past locations, are used to provide more of the kind of services that we come to expect from a real tour guide. We describe the architecture and features of a variety of Cyberguide prototypes developed for indoor and outdoor use on a number of different hand-held platforms. We also discuss the general research issues that have emerged in our context-aware applications development in a mobile environment.
1,659 citations
01 Oct 2003
TL;DR: This work presents a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales and incorporates these locations into a Markov model that can be consulted for use with a variety of applications in both single-user and collaborative scenarios.
Abstract: Wearable computers have the potential to act as intelligent agents in everyday life and to assist the user in a variety of tasks, using context to determine how to act. Location is the most common form of context used by these agents to determine the user's task. However, another potential use of location context is the creation of a predictive model of the user's future movements. We present a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales. These locations are then incorporated into a Markov model that can be consulted for use with a variety of applications in both single-user and collaborative scenarios.
1,211 citations
21 Sep 2008
TL;DR: An approach based on supervised learning to infer people's motion modes from their GPS logs is proposed, which identifies a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used.
Abstract: Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems As a kind of user behavior, the transportation modes, such as walking, driving, etc, that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs The contribution of this work lies in the following two aspects On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement
1,054 citations
01 Aug 2009
TL;DR: This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information, which includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the geometric nature of the data.
Abstract: This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information. This definition includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the geometric nature of the data. The survey omits non-computational techniques like manually inspecting geotagged photos, and it omits techniques like encryption or access control that treat location data as general symbols. The paper reviews studies of peoples' attitudes about location privacy, computational threats on leaked location data, and computational countermeasures for mitigating these threats.
732 citations