Social Media Recommender Systems: Review and Open Research Issues

Anandhan, Anitha; Shuib, Liyana; Ismail, Maizatul Akmar; Mujtaba, Ghulam

doi:10.1109/access.2018.2810062

Cited by 90 publications

(36 citation statements)

References 71 publications

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“…In [6], a dynamic personalized recommendation system enhances the precision and recall of tweet identification. A comprehensive review and analysis to explore several recommendation approaches was presented in [7]. A hybrid approach of Social and Content aware Oneclass Recommendation of Papers (SCORP) was introduced in [8].…”

Section: Related Workmentioning

confidence: 99%

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Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

Devi¹,

Pattabiraman²

2020

IJIES

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Recommender systems are in recent past becoming more notable in the age of swift evolution of the information technology ensuring e-commerce users' suitable items. In recent years, various thread-based and trust based approaches have been proposed, which improve the precision and recall of recommendation to certain extent. However, these approaches are less accurate than expected when users' preferences evolve over time and when several aspects of the users are involved. To solve these problems, we propose a novel recommendation method called, Soft Cosine Gradient and Gaussian Joint Probability (SCG-GJP) for Online Social Networks (OSN). Specifically, the Local Soft Cosine Clique Clustering (LSCCC) is first used to identify good candidate items to be recommended. Second, using adjacency matrix factorization, probable past preference of individual user and overall preference of whole community is formed to reduce the problem of mean absolute error. Again, the obtained matrix is trained through the stochastic function gradient to rank the most suitable candidates. Finally, Gaussian Mixture Joint Probability Postfiltering model is proposed to provide useful recommendations. The experiments are executed with real-world dataset (DS) and compared with the conventional recommendation methods. Experimental results demonstrate that the SCG-GJP obtains higher accuracy, and lesser error as well as complexity.

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Section: Related Workmentioning

confidence: 99%

“…Evaluate user feature matrix and item feature matrix based on overall performance of whole community. 7 From Eq. (5), ' * ' denotes the relationship between ' ' users and ' ' propositions. '…”

Section: Algorithm 2: Latest Stochastic Gradient Algorithmmentioning

confidence: 99%

Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

Devi¹,

Pattabiraman²

2020

IJIES

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“…Recommender system has been applied to many application areas like Music [4], Friend [5], Trip [6], Movie [7][8] [9], News [10], Social-Media recommendation [11], and many more. Recommending similar movies to the active user as per the likeness is called Movie Recommender System (MRS).…”

Section: Thisinformationmentioning

confidence: 99%

A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System

Behera*,

Das,

Swetanisha

2019

IJRTE

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Recommender System or Recommendation Engine gaining popularity as it can tackle information overload problem. Initially it was considered as a domain of Information Retrieval system and was limited to few applications. With the advancement of different state-of-the-art modeling approaches recommender system can be applicable to many application domains. Movie Recommender System (MRS) is widely explored domain and used by many streaming service providers like Netflix, Amazon Prime, YouTube and many more. This system makes use of users’ data to explore and recommends personally as per their taste. In this paper a detailed study on recently published article related to movie recommendation is carried out. Popular techniques for MRS are commonlycategorized into collaborative filtering, content-based and hybridmethod. Neighborhood-based, latent factor based, neural network based and deep learning based techniques have been continuously evolved with application to MRS. Recently proposed models have been reviewed and it is found that hybrid method performs better as compared to individual model.

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“…RSs [1], [2] are now widely use in research [3], industry [4], and education community [5], [6], where many approaches have been developed for improving recommendations. Many real world examples of recommendation operation can be found for books on Amazon [7], music on Spotify [8], activities on social media [9], [10], services on Twitter [11], [12], or movies on Netflix [13]. The design of these systems depends on the particular characteristics of the datasets, e.g.…”

Section: Introductionmentioning

confidence: 99%

Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems

Duong-Trung¹,

Nguyen²,

Ha³

et al. 2020

IJACSA

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A movie recommender system has been proven to be a convincing implement on carrying out comprehensive and complicated recommendation which helps users find appropriate movies conveniently. It follows a mechanism that a user can be accurately recommended movies based on other similar interests, e.g. collaborative filtering, and the movies themselves, e.g. contentbased filtering. Therefore, the systems should come with predetermined information either by users or by movies. One interesting research question should be asked: "what if this information is missing or not manually manipulated?" The problem has not been addressed in the literature, especially for the 100K and 1M variations of the MovieLens datasets. This paper exploits the movie recommender system based on movies' genres and actors/actresses themselves as the input tags or tag interpolation. We apply tag-based filtering and collaborative filtering that can effectively predict a list of movies that is similar to the movie that a user has been watched. Due to not depending on users' profiles, our approach has eliminated the effect of the coldstart problem. The experiment results obtained on MovieLens datasets indicate that the proposed model may contribute adequate performance regarding efficiency and reliability, and thus provide better-personalized movie recommendations. A movie recommender system has been deployed to demonstrate our work. The collected datasets have been published on our Github repository to encourage further reproducibility and improvement.

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Social Media Recommender Systems: Review and Open Research Issues

Cited by 90 publications

References 71 publications

Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

A Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System

Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems

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