A Survey on Hybrid Recommendation System for Movie dataset

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.

In this paper, we are going to explain the recommender system for movies that recommend a movie for a specific multimedia device. Using movies feature rating, user demographic information and devices information we construct hybrid model and cluster the users offline, and then find the similar neighbor and predict the movie features and recommend the Top-N most similar movies

2018

Recommender systems represent a powerful method for enabling users to filter through wide verity of information. Research in the recommender system is moving in the direction of a richer understanding of how recommender technology may be embedded in specific domains. A recommendation system for movies is important in our social life to provide the enhanced entertainment .There are two major recommendation techniques –Collaborative and content based filtering but these filtering techniques are having some limitations thus an hybrid approach is often adopted. The proposed movie recommender system has an ability to recommend user specific movie by using their social networking site like Facebook data. This provides the generalized framework for personalized movie recommendation. https://journalnx.com/journal-article/20150657

The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 2023

Recommendation system relies on information of user preference and user behavior in order to recommend the useful information. The existing recommendation systems still have problems for new users and new items. This research proposes a new hybrid method to develop the conceptual framework of recommendation system that deals with new user and new movie data. The data used consists of a data from MovieLens and the internet movie database (IMDB). This work introduces a hybrid recommendation system which based on a combination of content-based filtering (CBF) and collaborative filtering (CF). Pre-filtering data is performed by finding an optimal number of clusters by calculating the total within cluster sum of square. In order to reduce the complexity of data and increase the relevance of the user-item ratings, the fuzzy c-mean (FCM) is employed. Then the similarity is calculated by using item-based method, the K-nearest neighbors and weight sum of the rating are applied. Finally, to recommend the movies, the research found that for new user data the precision is at 85% and mean absolute eror (MAE) value 2.1011. For new item data, the result of research obtains the precision at 87% and MAE value 2.0031. In conclusion, the new hybrid method developed can recommend movie efficiently.

2014

The growth of e-Commerce has given the birth to recommendation engine. There are many recommendation engines existing in the market to recommend different stuffs to the users. These recommendations are based on different aspects such as interest of users, history of users, location of users and many more. In all the above aspects, one thing is common and that is individuality. The engine recommends users on the basis of users' perspective; but there are things in market which are worth concerned and that a user is unaware of. These things must also recommended to the users by the engine; but due to the limitation of "individuality", these engines do not recommend things that are out of the box. The hybrid movie recommendation engine has overcome this limitation of individuality. The engine will recommend movies to the users as per their interest as well as it will recommend movies rated by other users who are similar to the user. In addition to that, there are web serv...

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019

Recommendation system helps people in decision making an item/person. Recommender systems are now pervasive and seek to make profit out of customers or successfully meet their needs. Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new item’s rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item. In this project, we attempt to under- stand the different kinds of recommendation systems and compare their performance on the Movie Lens dataset. Due to large size of data, recommendation system suffers from scalability problem. Hadoop is one of the solutions for this problem.

International Journal of Emerging Technology and Advanced Engineering

The primary aim of recommender system is to predict items which are of most interest to the users and today recommender systems play a vital role in boosting the sales in any e-commerce based platform. The present paper proposes an approach for recommending movies to the users on the basis on their choices. A novel technique for evaluation of collaborative filtering using SVD and hit ratio as a metric is taken in our proposed approach. We attempted to build a model-based Collaborative filtering technique. The proposed paper makes use of matrix factorization techniques like SVD & SVD++ for filtering movie recommendation system based on latent features. It makes better recommendations based on choice of user because it captures the underlying features driving the raw data. In this paper we are proposing a hybrid recommender system fusion of Content Based and SVD to get a new hybrid recommender system. Our proposed model gives the value of RMSE 0.87 for SVD model and RMSE 0.938 for SVD...

Journal of Intelligent Information Systems, 2021

With the dramatic rise of internet users in the last decade, there has been a massive rise in the number of daily web searches. This leads to a plethora of data available online, which is growing by the days. A recommendation engine leverages this massive amount of data by finding patterns of user behavior. Movie recommendation for users is one of the most prevalent implementations. Although it goes way back in the history of recommendation engines, collaborative filtering is still the most predominant method when it comes to the underlying technique implemented in recommendation engines. The main reasons behind that are its simplicity and flexibility. However, collaborative filtering has always suffered from the Cold-Start problem. When a new movie enters the rating platform, we do not have any user interaction for the movie. The foundation of collaborative filtering is based on the user-movie rating. In this paper, we have proposed a hybrid filtering to combat this problem using the genre labeled for a new movie. The proposed algorithm utilizes the nonlinear similarities among various movie genres and predicts the rating of a user for the new movie with the associated genres for the movie.

Recommender systems or recommendation systems are a subset of information filtering system that used to anticipate the 'evaluation' or 'preference' that user would feed to an item. In recent years E-commerce applications are widely using Recommender system. Generally the most popular Ecommerce sites are probably music, news, books, research articles, and products. Recommender systems are also available for business experts, jokes, restaurants, financial services, life insurance and twitter followers. Recommender systems have formulated in parallel with the web. Initially Recommender systems were based on demographic, content-based filtering and collaborative filtering. Currently, these systems are incorporating social information for enhancing a quality of recommendation process. For betterment of recommendation process in the future, Recommender systems will use personal, implicit and local information from the Internet. This paper provides an overview of recommender systems that include collaborative filtering, content-based filtering and hybrid approach of recommender system.