A hybrid recommender system for recommending relevant movies using an expert system

Walek, Bogdan; Fojtik, Vladimir

doi:10.1016/j.eswa.2020.113452

Cited by 145 publications

(65 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Due to unimportant challenges like scalability, dispersion and user's confidence compared with cold start and movies which have been researched till now, the challenges have also been resolved with preprocessing, clustering and classification. Walek et al in [25] the main objective of this paper to propose a hybrid recommender system predictor for recommending suitable movies. This system contains a recommender module combining a collaborative filtering system, a content-based system, and a fuzzy expert system.…”

Section: Related Workmentioning

confidence: 99%

A hybrid recommender system based-on link prediction for movie baskets analysis

2021

View full text Add to dashboard Cite

Over the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.

show abstract

Section: Related Workmentioning

confidence: 99%

A hybrid recommender system based-on link prediction for movie baskets analysis

2021

View full text Add to dashboard Cite

show abstract

“…This embedding model is inspired from word2vec embedding and utilized in collaborative filtering. Further, to recommend the suitable movie, Walek and Fojtik [28] proposed a new hybrid method named Predictory based on fuzzy expert system, content-based system and collaborative filtering system. Proposed system perform well as compare with traditional approach.…”

Section: Literature Surveymentioning

confidence: 99%

User-Review Oriented Social Recommender System for Event Planning

Singh¹,

Singh²,

Bathla³

2020

ISI

View full text Add to dashboard Cite

Recommender system is used to suggest product or topic based on user’s interest. Existing recommender system have focused on books, product, music etc. The problem in existing recommender system is that wedding/event based suggestions are not available. In the modern information era; storage, communication has been a challenge due to information veracity, volume, and velocity. Due to the constant and exponential growth of information, the utilization of information for context-oriented services is not productive. In this paper, a wedding planner recommender system framework has been proposed based on hybrid approach i.e., content based, collaborative filtering technique. The motive of proposed framework is to generate user-specific recommendations for different tasks related to the event specially wedding event, analyzed from the user comments on his social networking portal. Its main objective is to assist the user for organizing the events by suggesting specific vendors needed to arrange the event activities. Also, it would enhance the sales of location sensitive products in social commerce. The trial study conducted using a set of Facebook users is carried out to validate the proposed recommendation system framework. The success of the proposed framework is reported in terms of the level of user satisfaction achieved.

show abstract

“…Walek et al, (2020) in [25] the main objective of this article to propose a hybrid recommender system predictor for recommending suitable movies. This system contains a recommender module combining a collaborative filtering system, a content-based system, and a fuzzy expert sys-tem.…”

Section: Review Of the Literaturementioning

confidence: 99%

A Hybrid Recommender System Based-on Link Prediction for Movie Baskets Analysis

Etebarian

Azmi

Dastjerdi

2020

Preprint

View full text Add to dashboard Cite

The proposed method in this paper consists of three steps: initial clustering of all users and assigning new user to appropriate clusters, assigning appropriate weights to users' characteristics, and identifying new user’s adjacent users using hybrid similarity criteria and adjacency matrix of adjacent users’ rating to the movie services and calculating new user’s rating to each movie considering adjacent users’ rating and the similarity level of each adjacent user to the new user. The results show that the mean squared error of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. Also, MAE of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and RMSE of the proposed method decreased by 6.05% compared to SVD and approximately 6.02% compared to ApproSVD.

show abstract

A hybrid recommender system for recommending relevant movies using an expert system

Cited by 145 publications

References 21 publications

A hybrid recommender system based-on link prediction for movie baskets analysis

A hybrid recommender system based-on link prediction for movie baskets analysis

User-Review Oriented Social Recommender System for Event Planning

A Hybrid Recommender System Based-on Link Prediction for Movie Baskets Analysis

Contact Info

Product

Resources

About