A personalized mobile travel recommender system using hybrid algorithm

Renjith, Shini; Anjali, C

doi:10.1109/compsc.2014.7032612

Cited by 20 publications

(13 citation statements)

References 7 publications

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“…This kind of data are most suitable for density-based clustering. Besides, we have also conducted experiments on Absenteeism at work [14] , BuddyMove [15] , Gesture Phase Segmentation [16] , and Anuran Calls.…”

Section: Setupmentioning

confidence: 99%

Effective density-based clustering algorithms for incomplete data

Xue

Wang

2021

Big Data Min. Anal.

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Density-based clustering is an important category among clustering algorithms. In real applications, many datasets suffer from incompleteness. Traditional imputation technologies or other techniques for handling missing values are not suitable for density-based clustering and decrease clustering result quality. To avoid these problems, we develop a novel density-based clustering approach for incomplete data based on Bayesian theory, which conducts imputation and clustering concurrently and makes use of intermediate clustering results. To avoid the impact of low-density areas inside non-convex clusters, we introduce a local imputation clustering algorithm, which aims to impute points to high-density local areas. The performances of the proposed algorithms are evaluated using ten synthetic datasets and five real-world datasets with induced missing values. The experimental results show the effectiveness of the proposed algorithms.

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Section: Setupmentioning

confidence: 99%

Effective density-based clustering algorithms for incomplete data

Xue

Wang

2021

Big Data Min. Anal.

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“…Comput. 2019, 3, 15 2 of 29 manipulate the ranking algorithms. The main consequence to the final customer is that irrelevant products may be shown with a higher rank whereas relevant ones hidden at the very bottom of the recommended list.…”

Section: Recommender Systemsmentioning

confidence: 99%

“…Comput. 2019, 3, 15 5 of 29 information to make more intelligent and user acceptable recommendations [15]; the model merges demographic and collaborative filtering methods with the characteristics of content-based travel.…”

Section: Personalized Information and Recommender Systemsmentioning

confidence: 99%

Intelligent Recommender System for Big Data Applications Based on the Random Neural Network

Serrano¹

2019

BDCC

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Online market places make their profit based on their advertisements or sales commission while businesses have the commercial interest to rank higher on recommendations to attract more customers. Web users cannot be guaranteed that the products provided by recommender systems within Big Data are either exhaustive or relevant to their needs. This article analyses the product rank relevance provided by different commercial Big Data recommender systems (Grouplens film, Trip Advisor and Amazon); it also proposes an Intelligent Recommender System (IRS) based on the Random Neural Network; IRS acts as an interface between the customer and the different Recommender Systems that iteratively adapts to the perceived user relevance. In addition, a relevance metric that combines both relevance and rank is presented; this metric is used to validate and compare the performance of the proposed algorithm. On average, IRS outperforms the Big Data recommender systems after learning iteratively from its customer.

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“…A user model web recommender system for recommending, predicting and personalizing music playlists is based on a hybrid similarity matching method that combines collaborative filtering with ontology-based semantic distance measurements [123]; a personalized music playlist, from a selection of recommended playlists that comprises the most relevant tracks to the user is dynamically generated. A hybrid travel recommender system makes use of the individual demographic information to suggest what suits the user thereby making it more intelligent and user acceptable [124]. The model combines the features of content-based travel with collaborative and demographic filtering techniques.…”

Section: Personalized Information and Recommender Systemsmentioning

confidence: 99%

Neural Networks in Big Data and Web Search

Serrano

2018

Data

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As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in Big Data and web search that covers web search engines, ranking algorithms, citation analysis and recommender systems. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction.

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A personalized mobile travel recommender system using hybrid algorithm

Cited by 20 publications

References 7 publications

Effective density-based clustering algorithms for incomplete data

Effective density-based clustering algorithms for incomplete data

Intelligent Recommender System for Big Data Applications Based on the Random Neural Network

Neural Networks in Big Data and Web Search

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