A Three Way Hybrid Movie Recommendation Syste

Soni, Karan; Goyal, Rinky; Vadera, Bhagyashree; More, Siddhi

doi:10.5120/ijca2017913026

Cited by 14 publications

(7 citation statements)

References 6 publications

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“…Statistics-based frameworks utilize the statistical data of clients, for example, nationality, age and educational level, to offer recommendations. The arrangement of the stereotype run-of-the-mill classes here is one of a kind, which is unique in relation to other recommender frameworks designed for use in a general-purpose setting [10][11][12].…”

Section: Related Workmentioning

confidence: 99%

Using Affiliation Rules-based Data Mining Technique in Referral System

Odule

Adesina

Abdullah

et al. 2020

eijs

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Referral techniques are normally employed in internet business applications. Existing frameworks prescribe things to a particular client according to client inclinations and former high evaluations. Quite a number of methods, such as cooperative filtering and content-based methodologies, dominate the architectural design of referral frameworks. Many referral schemes are domain-specific and cannot be deployed in a general-purpose setting. This study proposes a two-dimensional (User × Item)-space multimode referral scheme, having an enormous client base but few articles on offer. Additionally, the design of the referral scheme is anchored on the and articles, as expressed by a particular client, and is a combination of affiliation rules mining and the content-based method. The experiments used the dataset of MovieLens, consisting of 100,000 motion pictures appraisals on a size of 1-5, from 943 clients on 1,682 motion pictures. It utilised a five-overlap cross appraisal on a (User × Item)-rating matrix with 12 articles evaluated by a minimum of 320 clients. A total of 16 rules were generated for both and articles, at 35% minimum support and 80% confidence for the articles and 50% similitude for the . Experimental results showed that the anticipated appraisals in denary give a better rating than other measures of exactness. In conclusion, the proposed algorithm works well and fits on two dimensional -space with articles that are significantly fewer than users, thus making it applicable and effective in a variety of uses and scenarios as a general-purpose utility.

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Section: Related Workmentioning

confidence: 99%

Using Affiliation Rules-based Data Mining Technique in Referral System

Odule

Adesina

Abdullah

et al. 2020

eijs

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show abstract

“…Gunawardana and Meek introduced unified Boltzmann machines to hybrid collaborative filtering method and content-based method by encoding their information [26]. On the basis of integration of contentbased method and collaborative filtering method, Soni et al joined the analysis of review based text mining algorithm, making the recommendation more accurate [27]. Moreover, Ling et al employed a rating model with a topic model based on reviews to make accurate predictions [28].…”

Section: Collaborative Filtering-based Recommendationmentioning

confidence: 99%

A Sentiment‐Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

Wang

2018

Wireless Communications and Mobile Computing

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Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.

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“…(1) Input the population Epa (2) for each p∈Epa (3) S p =⌀, M P = 0 (4) for each q∈Epa (5) if (p≻q) then // If p dominates q (6) S p = S p ∪ { } // Add q to the set of solutions dominated by p (7) else if (q≻p) then (8) M P = M P +1 // Increment the domination counter of p (9) if M P = 0 then // p belongs to the first front (10) rank = 1 (11)…”

Section: Complete Algorithm Of Moea-epgmentioning

confidence: 99%

“…In recent years, many RSs have been proposed [9][10][11][12], including the content-based RSs, the collaborative filtering based RSs, the knowledge-based RSs, and the hybrid RSs. The content-based RSs suggest similar items by extracting the content's features from the profiles of items and users; the collaborative filtering based RSs exploit the community data (e.g., feedback rating, tags, or clicks from other users) to make recommendation, while the knowledge-based RSs use knowledge bases and knowledge models (e.g., ontologies) for recommendation.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

Lin

Wang

et al. 2018

Complexity

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Recommender systems suggest items to users based on their potential interests, and they are important to alleviate the search and selection pressures induced by the increasing item information. Classical recommender systems mainly focus on the accuracy of recommendation. However, with the increase of the diversified demands of users, multiple metrics which may conflict with each other have to be considered in modern recommender systems, especially for the personalized recommender system. In this paper, we design a personalized recommendation system considering the three conflicting objectives, i.e., the accuracy, diversity, and novelty. Then, to let the system provide more comprehensive recommended items, we present a multiobjective personalized recommendation algorithm using extreme point guided evolutionary computation (called MOEA-EPG). The proposed MOEA-EPG is guided by three extreme points and its crossover operator is designed for better satisfying the demands of users. The experimental results validate the effectiveness of MOEA-EPG when compared to some state-of-the-art recommendation algorithms in terms of accuracy, diversity, and novelty on recommendation.

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A Three Way Hybrid Movie Recommendation Syste

Cited by 14 publications

References 6 publications

Using Affiliation Rules-based Data Mining Technique in Referral System

Using Affiliation Rules-based Data Mining Technique in Referral System

A Sentiment‐Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

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