An algorithm for movie classification and recommendation using genre correlation

The most classic algorithm in the recommender system domain is the collaborative filtering algorithm, but it often suffers from data sparsity and item cold-start problems. Moreover, most traditional collaborative filtering algorithms only consider the user's rating of the item, which greatly limits the accuracy of the recommendation. This paper takes movie recommendation as the research object, and proposes a collaborative filtering recommendation model based on LDA topic model and network embedding, which improves the dependability of the model. The model first uses the LDA topic model to extract the text features of users and movies from texts, then builds the association network through the association features between users, and learns the network features using node2vec and GraphGAN. Finally, the two parts are combined to compute the similarity between users to recommend movies using the collaborative filtering algorithm. Results of the experiment on the real dataset of the Douban website demonstrate that movie recommendation with the user characteristics mined by the LDA model and GraphGAN combined can achieve the best effect, which is much higher than the collaborative filtering based on user ratings or using only LDA theme. This proves that network embedding can promote the accuracy of recommendationation, while ensuring the dependability of the model. The method proposed in this paper is effective.

The necessity for sophisticated recommender systems in the movie recommendation sphere has become particularly pronounced, generating a more personalized movie recommendation due to people nowadays who like to watch movies online. Efficient recommender systems make use of advanced machine learning (ML) techniques in the pursuit of accurate and meaningful recommendations. This paper endeavours to give a comprehensive overview of technologies known as recommender systems, concentrating on ML methods found at the base. Different strategies have been applied in this work, which include collaborative filtering (CF), content-based filtering (CB), hybrid approaches, Generative AI and so on. The merits and demerits of each technique are listed and explained briefly. In addition, the actual application’s results are also presented in this paper. To evaluate the performance of the techniques, some of the important datasets that are used in evaluating recommender systems are also discussed along with measurement metrics to determine the effectiveness of technique. Example metrics used are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and so on. This paper synthesizes existing research to evaluate the advantages and limitations of diverse recommendation techniques, which aims to give suggestion on how to improve the design of movie recommendation systems so that the performance of the technique can be improved.

Evaluation Of Recommendation System On A Movie Dataset

Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.

Hybrid Movie Recommendation System using sentiment analysis with opinion mining, is a system that helps the user by giving recommendations about the movies, based on the user preference and ordinal ratings of the movies. It removes the need for the user to go through a big list of movies before deciding what should be watched. The arrangement exploits the consumer attitude and their ideas around the attributes to give recommendations and produce output. The script centred assessments from the consumer are very optimum in enlightening the accurateness and competence of a movie recommendation arrangement. Three methods must be castoff to abstract attributes as of the movies characteristics to be utilized as attributes for collaborative filtering. The essential physical interfaces are dissimilar for all the methods. The dataset that was cast-off for the assessment of attributes with CF included of nominal scores of numerous movies. An intricate technique of collaborative filtering is efficient for the drive of recommendation throughout assessment and to associate performance of the arrangement with the addition of attributes from user assessments and deprived of the attributes from user reviews. This technique performs overall improved methodology, which is merely built on nominal ratings and movie attributes like movie genres.

This paper presents a comprehensive review of movie recommendation systems and their applications in the entertainment industry. The paper highlights the different techniques used in movie recommendation systems such as collaborative filtering, content-based filtering, and hybrid filtering. The strengths and weaknesses of each technique are discussed, along with their respective algorithms. The paper also explores the challenges faced by movie recommendation systems such as data sparsity, cold start problem, and user bias. Furthermore, the paper examines the current state-of-the-art movie recommendation systems, their performance evaluation metrics, and their implementation in real world scenarios. The practical application of these systems by popular online streaming platforms is also discussed. The study concludes that movie recommendation systems are essential for enhancing user experience and engagement in the entertainment industry, and future research in this field is crucial to improving the accuracy and effectiveness of these systems. Key Words: Recommendation System, PyCharm, Jupiter Notebook, Artificial Intelligence & Machine Learning.

Recommender system is a tool to suggest items to the users from the extensive history of the user's feedback. Though, it is an emerging research area concerning academics and industries, where it suffers from sparsity, scalability, and cold start problems. This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. In this paper, an effective movie recommendation system is proposed by Classification and Regression Tree (CART) algorithm, enhanced Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm and truncation method. In this research paper, a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process, where the proposed algorithm is named as enhanced BIRCH. The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage. In this paper, the proposed model is tested on Movielens dataset, and the performance is evaluated by means of Mean Absolute Error (MAE), precision, recall and f-measure. The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models. The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets. Further, the proposed model obtained 0.83 of precision, 0.86 of recall and 0.86 of f-measure on Movielens 100k dataset, which are effective compared to the existing models in movie recommendation.

An effective collaborative movie recommender system with cuckoo search

Recently, collaborative filtering (CF) algorithms have played an important role in recommendation systems. However, the traditional CF algorithms cannot make full use of information (e.g., users' preference, social relationship, and so on) implied in check-ins of users in the recommendation process. In order to address this issue, in this paper, a recommendation framework is proposed by exploiting historical user behavior, the check-in information and user social relationships to improve the precision of recommendation, as follows. First, user reputation can be obtained by iteratively calculating the correlation of historical ratings of the user and intrinsic qualities of items. Second, we employ user reputation and rating similarity in the basic social recommender model. Finally, we use the social connections among users to obtain the social influence of their friends. To evaluate the performance of our method, a series of experiments conducted on Epinions and Ciao data sets show that our approach performs better than several state-of-the-art methods on metrics of RMSE, precision rate, recall rate and F-Measure.

Abstract—Recommender systems are often used to provide useful recommendations for users. They use previous history of the users-items interactions, e.g. purchase history and/or users rating on items, to provide a suitable recommendation list for any target user. They may also use contextual information available about items and users. Collaborative filtering algorithm and its variants are the most successful recommendation algorithms that have been applied to many applications. Collaborative filtering method works by first finding the most similar users (or items) for a target user (or items), and then building the recommendation lists. There is no unique evaluation metric to assess the performance of recommendations systems, and one often choose the one most appropriate for the application in hand. This paper compares the performance of a number of well-known collaborative filtering algorithms on movie recommendation. To this end, a number of performance criteria are used to test the algorithms. The algorithms are ranked for each evaluation metric and a rank aggregation method is used to determine the wining algorithm. Our experiments show that the probabilistic matrix factorization has the top performance in this dataset, followed by item-based and user-based collaborative filtering. Non-negative matrix factorization and Slope 1 has the worst performance among the considered algorithms.
 Keywords—Social networks analysis and mining, big data, recommender systems, collaborative filtering.

In view of the sparsity of user-resource interaction data, information asymmetry caused by cold start, and weak personalized resource allocation in the current integration of enterprises with vocational schools and universities, this article adopts the Collaborative Filtering (CF) algorithm to mine the user’s behavior patterns and preferences, establish a more intelligent resource recommendation model, and enhance the relevance and accuracy of resource recommendation. The intelligent resource recommendation system plays a key role in the construction of smart cities, fully optimizing the interaction between educational institutions and enterprises, and providing strong support for promoting the development of smart cities. First, an embedded feature expression for users and resources is established, and the deep embedding learning method is used to extract the association between high-dimensional and nonlinear features. Secondly, based on the traditional CF algorithm, the neural network architecture is combined to achieve accurate matching between users and resources. Finally, through experiments in user cold start and resource cold start scenarios, the performance improvement of the recommendation system is verified and compared with the widely used Matrix Factorization (MF) and Neural Collaborative Filtering (NCF) for analysis. According to the experimental results, the average precision of the proposed method is 0.804, which is 7.34% and 5.51% higher than MF and NCF respectively. The conclusion shows that the CF algorithm helps to enhance the resource recommendation effect and provides a new perspective for enhancing service personalization.

Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distributed systems. The combination guarantees the safety and scalability of the recommender system. We aim to create an authorized secure exchange device using blockchain-enabled multiparty computation by adding smart contracts to the core blockchain protocol. The recommendation structure and Blockchain technology make online shopping more convenient and private. We propose a blockchain-related recommender system using the “movielens” data. The case study includes a smart contract model that recommends movies to buyers. Initially, we tested the model on a small “movielens dataset” and extended it to a 3M movielens dataset. We developed a classifier model for movielens and proposed a Dual light graph convolutional network for movielens data classification. Our results, including ablation analysis, show that blockchain strategies and Dual light graph convolutional networks can effectively improve recommender systems’ privacy. Furthermore, the suggested blockchain technique can be stretched by similar procedures.

With the rapid growth of the Internet and the consequent surge in data, the current era is characterized by information overload. As the domain of data processing and storage expands, recommendation systems have become pivotal tools in navigating this deluge, assisting users in filtering through vast information landscapes. A notable segment of this is movie recommendation systems. As living standards rise, so does the demand for cinematic experiences. Enhancing and refining the methodologies of these recommendation systems is, therefore, of significant value. However, a consistent challenge is the cold start problem encountered when new users join. Without prior viewing records or preferences, these users pose a dilemma for the system: how to offer relevant recommendations without historical data? Addressing this challenge, this paper proposes a unique method grounded in the N-armed bandit model, introducing an enhanced Epsilon-greedy algorithm specifically designed for movie recommendations for such users. By adjusting dynamically based on real-time user feedback, the algorithm aims to continuously hone its recommendation quality, ensuring a consistently better user experience.

Movie collaborative filtering with multiplex implicit feedbacks

A key challenge of the collaborative filtering (CF) information filtering is how to obtain the reliable and accurate results with the help of peers' recommendation. Since the similarities from small-degree users to large-degree users would be larger than the ones in opposite direction, the large-degree users' selections are recommended extensively by the traditional second-order CF algorithms. By considering the users' similarity direction and the second-order correlations to depress the influence of mainstream preferences, we present the directed second-order CF (HDCF) algorithm specifically to address the challenge of accuracy and diversity of the CF algorithm. The numerical results for two benchmark data sets, MovieLens and Netflix, show that the accuracy of the new algorithm outperforms the state-of-the-art CF algorithms. Comparing with the CF algorithm based on random walks proposed by Liu et al. (Int. J. Mod. Phys. C, 20 (2009) 285) the average ranking score could reach 0.0767 and 0.0402, which is enhanced by 27.3% and 19.1% for MovieLens and Netflix, respectively. In addition, the diversity, precision and recall are also enhanced greatly. Without relying on any context-specific information, tuning the similarity direction of CF algorithms could obtain accurate and diverse recommendations. This work suggests that the user similarity direction is an important factor to improve the personalized recommendation performance.