scholarly journals A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yibo Wang ◽  
Mingming Wang ◽  
Wei Xu
Keyword(s):  
Sentiment Analysis ◽  
Recommender System ◽  
Evaluation Criteria ◽  
Big Data Analytics ◽  
Mobile Environment ◽  
Movie Recommendation ◽  
Hybrid Recommender System ◽  
Preliminary Recommendation ◽  
Hybrid Recommender ◽  
Hybrid Recommendation

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.

A Hybrid Recommender System for Rating Prediction of Arabic Reviews

2020 ◽  
pp. 2050010
Author(s):  
Fouzi Harrag ◽  
Abdulmalik Salman Al-Salman ◽  
Alaa Alquahtani
Keyword(s):  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
Opinion Mining ◽  
Good Accuracy ◽  
Hybrid Approach ◽  
Data Sparsity ◽  
Hybrid Recommender System ◽  
Rating Prediction ◽  
Hybrid Recommender

Recommender systems nowadays are playing an important role in the delivery of services and information to users. Sentiment analysis (also known as opinion mining) is the process of determining the attitude of textual opinions, whether they are positive, negative or neutral. Data sparsity is representing a big issue for recommender systems because of the insufficiency of user rating or absence of data about users or items. This research proposed a hybrid approach combining sentiment analysis and recommender systems to tackle the problem of data sparsity problems by predicting the rating of products from users’ reviews using text mining and NLP techniques. This research focuses especially on Arabic reviews, where the model is evaluated using Opinion Corpus for Arabic (OCA) dataset. Our system was efficient, and it showed a good accuracy of nearly 85% in predicting the rating from reviews.

scholarly journals Developing a Contextually Personalized Hybrid Recommender System

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Aysun Bozanta ◽  
Birgul Kutlu
Keyword(s):  
Recommender System ◽  
Real World ◽  
Experimental Evaluation ◽  
User Study ◽  
Contextual Information ◽  
Threshold Values ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Hybrid Recommendation ◽  
Content Based Filtering

It is hard to choose places to go from an endless number of options for some specific circumstances. Recommender systems are supposed to help us deal with these issues and make decisions that are more appropriate. The aim of this study is to recommend new venues to users according to their preferences. For this purpose, a hybrid recommendation model is proposed to integrate user-based and item-based collaborative filtering, content-based filtering together with contextual information in order to get rid of the disadvantages of each approach. Besides that, in which specific circumstances the user will like a specific venue is predicted for each user-venue pair. Moreover, threshold values determining the user’s liking toward a venue are determined separately for each user. Results are evaluated with both offline experiments (precision, recall, F-1 score) and a user study. Both the experimental evaluation with a real-world dataset and a user study of the proposed system showed improvement upon the baseline approaches.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

Intelligent Techniques in Recommender Systems and Contextual Advertising

2013 ◽  
pp. 105-128 ◽  
Author(s):  
Giuliano Armano ◽  
Alessandro Giuliani ◽  
Eloisa Vargiu
Keyword(s):  
Recommender System ◽  
Information Filtering ◽  
Relevant Information ◽  
Web Page ◽  
Contextual Advertising ◽  
Generic Hybrid ◽  
Unified View ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
The Web

Information Filtering deals with the problem of selecting relevant information for a given user, according to her/his preferences and interests. In this chapter, the authors consider two ways of performing information filtering: recommendation and contextual advertising. In particular, they study and analyze them according to a unified view. In fact, the task of suggesting an advertisement to a Web page can be viewed as the task of recommending an item (the advertisement) to a user (the Web page), and vice versa. Starting from this insight, the authors propose a content-based recommender system based on a generic solution for contextual advertising and a hybrid contextual advertising system based on a generic hybrid recommender system. Relevant case studies have been considered (i.e., a photo recommender and a Web advertiser) with the goal of highlighting how the proposed approach works in practice. In both cases, results confirm the effectiveness of the proposed solutions.

A hybrid recommender system for the mining of consumer preferences from their reviews

2019 ◽  
Vol 46 (5) ◽  
pp. 664-682
Author(s):  
Li Chen Cheng ◽  
Ming-Chan Lin
Keyword(s):  
Opinion Mining ◽  
Information Overload ◽  
Consumer Preferences ◽  
Online Reviews ◽  
User Generated Content ◽  
Mining Technique ◽  
Movie Recommendation ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Filtering Approach

Product review sites are widespread on the Internet and are rapidly gaining in popularity among consumers. This already large volume of user-generated content is dramatically growing every day, making it hard for consumers to filter out the worthwhile information which appears on the various review sites. There commendation system plays a significant role in solving the problem of information overload. This study proposes a framework which integrates a collaborative filtering approach and an opinion mining technique for movie recommendation. Within the proposed framework, sentiment analysis is first applied to the users’ reviews to detect consumer opinions about the movie they have watched and to explore the individual’s preference profile. Traditional recommendation models are overly dependent on preference ratings and often suffer from the problem of ‘data sparsity’. Experimental results obtained from real online reviews show that our proposed method is effective in dealing with insufficient data and is more accurate and efficient than existing traditional methods.

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