Hybrid Recommendation System with Collaborative Filtering and Association Rule Mining Using Big Data

Gandhi, Sonali; Gandhi, Monali

doi:10.1109/i2ct.2018.8529683

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…RSs constitute one of the artificial intelligence fields that provides suggestions to users in line with their interests and search histories [21]. According to [22,23], RSs utilize different techniques such as data mining and prediction algorithms to forecast the interests of users in various aspects such as information, products, and services. Three types of RSs exist: content-based filtering, collaborative filtering (CF), and hybrid filtering.…”

Section: Recommender Systemsmentioning

confidence: 99%

Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN

Al-Ghobari¹,

Muneer²,

Fati³

2021

Computers, Materials &Amp; Continua

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Many tourists who travel to explore different cultures and cities worldwide aim to find the best tourist sites, accommodation, and food according to their interests. This objective makes it harder for tourists to decide and plan where to go and what to do. Aside from hiring a local guide, an option which is beyond most travelers' budgets, the majority of sojourners nowadays use mobile devices to search for or recommend interesting sites on the basis of user reviews. Therefore, this work utilizes the prevalent recommender systems and mobile app technologies to overcome this issue. Accordingly, this study proposes location-aware personalized traveler assistance (LAPTA), a system which integrates user preferences and the global positioning system (GPS) to generate personalized and location-aware recommendations. That integration will enable the enhanced recommendation of the developed scheme relative to those from the traditional recommender systems used in customer ratings. Specifically, LAPTA separates the data obtained from Google locations into name and category tags. After the data separation, the system fetches the keywords from the user's input according to the user's past research behavior. The proposed system uses the K-Nearest algorithm to match the name and category tags with the user's input to generate personalized suggestions.The system also provides suggestions on the basis of nearby popular attractions using the Google point of interest feature to enhance system usability. The experimental results showed that LAPTA could provide more reliable and accurate recommendations compared to the reviewed recommendation applications.

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Section: Recommender Systemsmentioning

confidence: 99%

Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN

Al-Ghobari¹,

Muneer²,

Fati³

2021

Computers, Materials &Amp; Continua

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“…A Recommendation System for the Big Data available on the Web in the structure of ratings, reviews, opinions, complains remarks, feedback and comments about any product is developed using Hadoop Framework [32]; the method uses a hybrid filtering technique based on numerical data such as ratings or ranks. Collaboratively Filtering does not provide enough scalability and accuracy when applied into Big Data Recommender Systems [33]; in order to overcome this issue, Big Data techniques such as association rule mining are inserted in three steps: Feature Extraction to generate the customer's product rating matrix, Low Dimension Latent Factor Matrix that uses the alternating least square method and Association Rule Mining Algorithm for generating multistage rule recommendations.…”

Section: Recommender Systems For Big Datamentioning

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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“…Mu, Xiao, Tang, Luo, and Yina have introduced new similarity measure, which strongly depends on the distance function [9]. The Gandhi's paper has combined the collaborative filtering with the association rule mining to generate recommendations to improve the quality of the recommendation system [10]. Similarly Tewari and Priyanka have also developed a recommendation system for recommending books using user based collaborative filtering and association rule mining [11].…”

Section: Related Workmentioning

confidence: 99%

Generating Quality Items Recommendation by Fusing Content based and Collaborative filtering

2019

IJITEE

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Recommendation system has become an inevitable part of our life. It has already spread its prominence in various fields like movies, music, news, article recommendations etc. Due to the influence of social media, data is streaming from all over the Internet. Collect the relevant information from chunks of data available has become much difficult. Recommender systems guides in filtering data to get the relevant information. Commonly used recommendation approaches are content based filtering and collaborative filtering. Each approach has its own limitations. The hybrid approach combines the advantages of both the approaches. In this paper, we have tried to enhance the quality of the items recommendation system by fusing both content based and collaborative filtering uniquely. The experimental results are compared with that of other traditional approach using precision and recall evaluation measure. The comparison results show that our approach has 10% better precision for top-10 recommendations than other established recommendation technique.

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Hybrid Recommendation System with Collaborative Filtering and Association Rule Mining Using Big Data

Cited by 11 publications

References 12 publications

Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN

Location-Aware Personalized Traveler Recommender System (LAPTA) Using Collaborative Filtering KNN

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

Generating Quality Items Recommendation by Fusing Content based and Collaborative filtering

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