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

Wang, Yibo; Wang, Mingming; Xu, Wei

doi:10.1155/2018/8263704

Cited by 68 publications

(35 citation statements)

References 47 publications

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“…It does not declare how data are fetched nor categorized with their popularity. In [15, 72–74], multivariates are not used, and the study [73] is just based on microblogs, while the study [74] uses movie feature ratings.…”

Section: Evaluation and Discussionmentioning

confidence: 99%

A Neural Network-Inspired Approach for Improved and True Movie Recommendations

Ibrahim

Bajwa

UlAmin

et al. 2019

Computational Intelligence and Neuroscience

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In the last decade, sentiment analysis, opinion mining, and subjectivity of microblogs in social media have attracted a great deal of attention of researchers. Movie recommendation systems are the tools, which provide valuable services to the users. The data available online are growing gradually because the online activities of users or viewers are increasing day by day. Because of this, big data, analytics, and computational issues have raised. Therefore, we have to improve recommendations services upon the traditional one to make the recommendation system significant and efficient. This article presents the solution for these issues by producing the significant and efficient recommendation services using multivariates (ratings, votes, Twitter likes, and reviews) of movies from multiple external resources which are fetched by the web bot and managed by the Apache Hadoop framework in a distributed manner. Reviews are analyzed by a deep semantic analyzer based on the recurrent neural network (RNN/LSTM attention) with user movie attention (UMA) to produce the emotion. The proposed recommender evaluates multivariates and produces a more significant movie recommendation list according to the taste of the user on a mobile app in an efficient way.

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Section: Evaluation and Discussionmentioning

confidence: 99%

A Neural Network-Inspired Approach for Improved and True Movie Recommendations

Ibrahim

Bajwa

UlAmin

et al. 2019

Computational Intelligence and Neuroscience

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“…If the value is 0 then the document is said to be similar if the result is 1 then the value is said to be not similar. Note that this limit applies to a number of dimensions, and Cosine similarity is most often used in high dimensional positive space [18] Text mining can provide solutions to problems such as processing, organizing / grouping and analyzing large amounts of unstructured text [19,20].…”

Section: Text Miningmentioning

confidence: 99%

Research Supervisor Recommendation System Based on Topic Conformity

Rismanto¹,

Syulistyo²,

Agusta³

2020

IJMECS

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In a higher education such as universities, final project are under supervision of one or more supervisors with a similar research interest or topic. The determination of the final project supervisor is an important factor in the work of the student's final project. However, the lack of information about the supervisor can hamper students in making the determination of the supervisor. Thus, a system is needed that can facilitate students in determining the final project or thesis advisors in accordance with the research topic. This problem is the basis of this research. The study is conducted by developing a web-based system and applying the TF-IDF word weighting and cosine similarity method. TF-IDF method is a way to give the weight of the relationship of a word to the document. The cosine similarity is a method for calculating the similarity between two objects expressed in two vectors by using keywords from a document as a measure. The results of the advisor recommendation system can provide recommendations to students regarding the final assignment advisor who has conducted research in accordance with the topic of the student's final assignment written in Indonesian. In 20 testing, the accuracy of the comparison of the results of the system recommendations with the actual data obtained an average of 75% by comparing system recommendation with actual assigned supervisor.

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“…Content-based filtering approaches recommend items by matching the features of an item with users" profile [7]. They don"t consider the opinion of users while making recommendations, instead of that, the properties of item are taken into account.…”

Section: B Content-based Filtering (Cb)mentioning

confidence: 99%

Selecting Appropriate Metrics for Evaluation of Recommender Systems

Rawat¹,

Dwivedi²

2019

IJITCS

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The abundance of information on the web makes it difficult for users to find items that meet their information need effectively. To deal with this issue, a large number of recommender systems based on different recommender approaches were developed which have been used successfully in a wide variety of domains such as e-commerce, e-learning, e-resources, and egovernment among others. Moreover, in order for a recommender system to generate good quality of recommendations, it is essential for a researcher to find the most suitable evaluation metric which best matches a given recommender algorithm and a recommender's task. However, with the availability of several recommender tasks, recommender algorithms, and evaluation metrics, it is often difficult for a researcher to find their best combination. This paper aims to discuss various evaluation metrics in order to help researchers to select the most appropriate metric which matches a given task and an algorithm so as to provide good quality of recommendations.

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

Cited by 68 publications

References 47 publications

A Neural Network-Inspired Approach for Improved and True Movie Recommendations

A Neural Network-Inspired Approach for Improved and True Movie Recommendations

Research Supervisor Recommendation System Based on Topic Conformity

Selecting Appropriate Metrics for Evaluation of Recommender Systems

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