Proactive Recommendation System for m-Tourism Application

Smirnov, Alexander; Kashevnik, Alexey; Ponomarev, Andrew; Shilov, Nikolay; Teslya, Nikolay

doi:10.1007/978-3-319-11370-8_9

Cited by 11 publications

(7 citation statements)

References 23 publications

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“…The feedback obtained at the stage has been used to generate recommendations to the modern conditions and issues. Smirnov et al [21] modeled a proactive recommender system that supports tourism. The method has been developed to support the people in planning their tourism, which has been provided through their mobile devices.…”

Section: Literature Review On Recommendation Systemsmentioning

confidence: 99%

Real-Time Opinion Prediction Method for Emergency Public Events in Social Media Networks Using Opinion Hit Matrix

Uthirapathy¹,

Domnic²

2020

RIA

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Modern society has a great influence on social networks which have been used to share user’s opinions and ideologies. Opinions discussed in social media about any emergency public event happenings. However, analyzing the opinion proliferation, producing interesting facts, which helps to enhance public security in emergencies. A lot of approaches are available to analyze the problem but suffer to achieve higher performance. This paper presents a real-time opinion prediction method. It analyzes the influence or hit rate of opinion in any case. This method first generates the network with several nodes where each user has been considered as a node. With the trace of social chat, the method classifies and groups the users under different categories of interest. The interest detection is performed according to the Class Level Post Measure (CLPM) which represents the interest of the user under a specific category. Using the actors identified, the method generates an Opinion Hit Matrix (OHM) based on the events and opinions posted. Using the matrix, the method computes the opinion support measure (OSM) to select a subset of opinions to generate recommendations. The proposed algorithm improves the performance of the recommendation generation.

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Section: Literature Review On Recommendation Systemsmentioning

confidence: 99%

Real-Time Opinion Prediction Method for Emergency Public Events in Social Media Networks Using Opinion Hit Matrix

Uthirapathy¹,

Domnic²

2020

RIA

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“…While considering the tourism domain the most familiar and most widely used contextual information is Location. This contextual data explains the current position of a tourist [2] [10]. Next contextual information is Time.…”

Section: Context Informationmentioning

confidence: 99%

“…The weather may be characterized as sunny, rainy and winter [3]. Other contextual information is user preference where user has to manually enter his/her preference [2][6] [10]. Season is another contextual information which is classified into rainy, summer and winter [5].…”

Section: Context Informationmentioning

confidence: 99%

“…Such tree representation defines as a set of contextual dimensions, each having a set of attributes in which each attributes in the set has a hierarchical structure having a particular type of context. Ontology [1][5] [10] and Context dimension tree [8] are the methods being used in hierarchical structure. Gediminas et al in [3] proposed second way to generate the rating model is by defining the context data as multidimensional model.…”

Section: E Rating Modelmentioning

confidence: 99%

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Context Attentive Recommender System: A Survey

Gaude¹

2019

IJRASET

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Recommendation is a process of suggesting something to someone. Since there is lot of information available around us, users find it difficult to find proper information. In order to solve this problem recommendation systems were being built that allow users to reach necessary information without wasting much time. The recommendation system is a weapon that help users in finding the approp riate service within no time. Most of the traditional recommendation system were being developed using content based filtering and collaborative filtering which works only on user and item profile. Many research were done where it found that beyond user and item profile, incorporating contextual information will enhance the prediction task and providing the more accurate recommendations. This paper surveyed the background, contextual information used in context based destination recommender system for tourism and types of algorithms.

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“…In mobile travel applications, active recommendation is particularly practical. Literature [8] proposes a method and research prototype based on smart space and active recommendation system technology, and the system implementing the proposed method helps tourists use their mobile devices. Literature [9] proposes the overall structure and functionality of an intelligent recommendation system that enables a user to identify his/her own destination, bundle a set of products, and make a travel plan for personalized travel.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Recommendation Business Model of Tourism Enterprise Value Platform from the Perspective of Value Cocreation

Wang

Tang

2021

Complexity

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With the rapid development of China’s economy, people pay attention to their own quality of life, and tourism has become the first choice for people from all walks of life to relax themselves. Tourism travel has mainly developed from the form of travel agency registration to the form of online registration based on the network platform business model. Considering the value cocreation and the diversity of tourism enterprise platform, this paper puts forward the business model research of intelligent recommendation of tourism enterprise platform from the perspective of value cocreation. Firstly, the commonly used recommendation algorithms are introduced, which are collaborative filtering recommendation algorithm, content filtering recommendation algorithm, and association rule recommendation algorithm. Secondly, it analyzes the number of tourists and economic benefits of the business platform of tourism enterprises from April 2020 to April 2021 and also analyzes the business models of five modules under the tourism platform on different platforms. Finally, three recommendation algorithms are used to compare the comprehensive performance of five modules in different business models. Finally, we find that the rate of accuracy and recall of business is above 88%, which can have good economic benefits and provide customers with high-quality recommendation service and good satisfaction.

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Proactive Recommendation System for m-Tourism Application

Cited by 11 publications

References 23 publications

Real-Time Opinion Prediction Method for Emergency Public Events in Social Media Networks Using Opinion Hit Matrix

Real-Time Opinion Prediction Method for Emergency Public Events in Social Media Networks Using Opinion Hit Matrix

Context Attentive Recommender System: A Survey

Research on Intelligent Recommendation Business Model of Tourism Enterprise Value Platform from the Perspective of Value Cocreation

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