Movies Recommenders Systems: Automation of the Information and Evaluation Phases in a Multi-criteria Decision-Making Process (original) (raw)
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RRSS - Rating Reviews Support System Purpose Built for Movies Recommendation
Advances in Soft Computing, 2007
This paper describes the part of a recommendation system designed for the recognition of film reviews (RRSS). Such a system allows the automatic collection, evaluation and rating of reviews and opinions of the movies. First the system searches and retrieves texts supposed to be movie reviews from the Internet. Subsequently the system carries out an evaluation and rating of the movie reviews. Finally, the system automatically associates a digital assessment with each review. The goal of the system is to give the score of reviews associated with the user who wrote them. All of this data is the input to the cognitive engine. Data from our base allows the making of correspondences, which are required for cognitive algorithms to improve, advanced recommending functionalities for e-business and e-purchase websites. In this paper we will describe the different methods on automatically identifying opinions using natural language knowledge and techniques of classification.
Recommendation Systems or Engines are found in many applications. These systems or Engines offer the user or service subscriber with a list of suggestions or recommendations that they might choose based on the user's already known preferences. In this paper, the focus is on combining a content-based algorithm, a User-based collaborative filtering algorithm, and review based text mining algorithm in the application of a tailored movie recommendation system. Here movies are recommended based on ratings explicitly provided by the user and according to the ratings and reviews of movies provided by other users as well. Here the propose is to generate polarity ratings to Characteristics of a movie instead of generating a wholesome rating to an available text based review to gain better insights about preferences of users, thus refining Movie recommendation systems further.
JEECS (Journal of Electrical Engineering and Computer Sciences)
At this time advances in technology and information have experienced rapid progress, one of which is in the field ofentertainment, both audio and visual. And one of the entertainments is movies. With the increasing number of movies,there are several classifications of movie genres to assist users in finding and selecting movies to watch, but the genreclassification itself is still very general. Due to the above factors, especially in genre, subgenre, rating, movie durationwhich always develops over time according to a certain pattern and also audiences who have different moviepreferences, the researcher sees that there is a need for an application that can recommend movies with preferencesthat can be set according to the wishes of movie lovers. From the problems that arise, this research was built using theSimple Additive Weighting (SAW) method which aims to make it easier for users to determine which movie to choose.This system produces a web-based information system using several ...
Multi-Criteria Review-Based Recommender System–The State of the Art
IEEE Access, 2019
In recent times, the recommender systems (RSs) have considerable importance in academia, commercial activities, and industry. They are widely used in various domains such as shopping (Amazon), music (Pandora), movies (Netflix), travel (TripAdvisor), restaurant (Yelp), people (Facebook), and articles (TED). Most of the RSs approaches rely on a single-criterion rating (overall rating) as a primary source for the recommendation process. However, the overall rating is not enough to gain high accuracy of recommendations because the overall rating cannot express fine-grained analysis behind the user's behavior. To solve this problem, multi-criteria recommender systems (MCRSs) have been developed to improve the accuracy of the RS performance. Additionally, a new source of information represented by the user-generated reviews is incorporated in the recommendation process because of the rich and numerous information included (i.e. review elements) related to the whole item or to a certain feature of the item or the user's preferences. The valuable review elements are extracted using either text mining or sentiment analysis. MCRSs benefit from the review elements of the user-generated reviews in building their criteria forming multi-criteria review based recommender systems. The review elements improve the accuracy of the RS performance and mitigate most of the RS's problems such as the cold start and sparsity. In this review, we focused on the multi-criteria review-based recommender system and explained the user reviews elements in detail and how these can be integrated into the RSs to help develop their criteria to enhance the RSs performance. Finally, based on the survey, we presented four future trends based on this type of RSs to support researchers who wish to pursue studies in this area. INDEX TERMS Recommender system, multi-criteria recommender system, user-generated reviews, review elements, sentiment analysis, text mining, multi-criteria review-based recommender system, recommender system accuracy.
A recommender system for the TV on the web: integrating unrated reviews and movie ratings
Multimedia Systems, 2013
The activity of Social-TV viewers has grown considerably in the last few years-viewers are no longer passive elements. The Web has socially empowered the viewers in many new different ways, for example, viewers can now rate TV programs, comment them, and suggest TV shows to friends through Web sites. Some innovations have been exploring these new activities of viewers but we are still far from realizing the full potential of this new setting. For example, social interactions on the Web, such as comments and ratings in online forums, create valuable feedback about the targeted TV entertainment shows. In this paper, we address this last setting: a media recommendation algorithm that suggests recommendations based on users' ratings and unrated comments. In contrast to similar approaches that are only ratings-based, we propose the inclusion of sentiment knowledge in recommendations. This approach computes new media recommendations by merging media ratings and comments written by users about specific entertainment shows. This contrasts with existing recommendation methods that explore ratings and metadata but do not analyze what users have to say about particular media programs. In this paper, we argue that text comments are excellent indicators of user satisfaction. Sentiment analysis algorithms offer an analysis of the users' preferences in which the comments may not be associated with an explicit rating. Thus, this analysis will also have an impact on the popularity of a given media show. Thus, the recommendation algorithm-based on matrix factorization by Singular Value Decompositionwill consider both explicit ratings and the output of sentiment analysis algorithms to compute new recommendations. The implemented recommendation framework can be integrated on a Web TV system where users can view and comment entertainment media from a video-ondemand service. The recommendation framework was evaluated on two datasets from IMDb with 53,112 reviews (50 % unrated) and Amazon entertainment media with 698,210 reviews (26 % unrated). Recommendation results with ratings and the inferred preferences-based on the sentiment analysis algorithms-exhibited an improvement over the ratings only based recommendations. This result illustrates the potential of sentiment analysis of user comments in recommendation systems.
This paper proposes an original recommender system (RS) 1 based upon an automatic extraction of trends from opinions and a multicriteria multi actors assessment model. Our RS tries to optimize the use of the available information on the web to reduce as much as possible the complex and fastidious steps for multicriteria assessing and for identifying users' preference models. It may be applied as soon as i) overall assessments of competing entities are provided by trade magazines and ii) web users' critics in natural languages and related to some characteristics of the assessed entities are available. Recommendation is then based on the capacity of the RS to associate a web user with a trade magazine that conveys the same values as the user and thus represents a reliable personalized source of information. Possibility theory is used to take account subjectivity of critics. Finally a case study concerning movie recommendations is presented.
In today's digital world, it has become an irksome task to find the content of one's liking in an endless variety of content that are being consumed like books, videos, articles, movies, etc.On the other hand there has been an emerging growth among the digital content providers who want to engage as many users on their service as possible for the maximum time. This gave birth to the recommender system comes wherein the content providers recommend users the content according to the users' taste and liking. In this paper we have implemented a movie recommendation system. A movie recommendation is important in our social life as it helps the user in finding the popular movies and also based on the users interest and taste among a set of movies.In this paper we are proposing a movie recommendation system that has the ability to recommend movies to a new user as well as the other existing users.It acquires movie databases by collecting all the important particulars including popularity and attractiveness, which are essential for recommendation. A hybrid filtering is used to construct our recommendation system which includes both content based filtering and collaborative filtering.
2020
Nowadays, thousands of commercial and non-commercial sites provide a large amount of different products to users on the Internet. Recommender systems play an important role in helping online users find relevant information by suggesting information of potential interest to them. For this purpose, Amazon and Netflix by using these systems, they help their customers to fined right products. Recommender systems have become significant tools in e-commerce that try to find the items such as books or movies that match best with users' preferences. One of the types of Recommender Systems is the movie recommendation Different users have different preferences in different genres of the film. In order to predict new film to users in user-base approach first, the similarity between users must be calculated, then, according to the calculated similarity, the rating of the new movies is predicted for the target user. In this study, to calculate the similarity between users, at the first, the ...
A Movie Recommender System Using Hybrid Approach: A Review
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The topic of this paper is movie suggestions. Because of its ability to provide improved entertainment, a movie recommendation is vital in our social lives. Users can be recommended a set of movies depending on their interests or admiration for the films by such a system. A recommendation system is used to make suggestions for things to buy or see. They employ a big collection of information to steer consumers to the things that will best match their needs. A recommender system, also known as a recommendation system, is a type of material filtering system that attempts to forecast a user's "rating" or "preference" for an item. They're mostly employed for commercial purposes. MOVREC also assists users in efficiently and effectively locating movies of their choice based on the movie experiences of other users, without wasting time in pointless searching.
Development of a recommendation system with multiple subjective evaluation process models
2003
We have observed how each consumer judges his likes and dislikes on objects viewing them. We have modeled each consumer's evaluation process by relationships among physical features of objects, each consumer's subjective interpretations and preferences. Thus, based on the models, our system can estimate users' subjective evaluations and preferences from physical features of objects to perform a suitable recommendation.