Movie Recommendation Research Articles

Mean-reversion based hybrid movie recommender system using collaborative and content-based filtering methods

Mean-reversion based hybrid movie recommender system using collaborative and content-based filtering methods

Digital Content Recommendation System through Facial Emotion Recognition

Abstract: The use of facial emotion recognition technology in movie and music recommendation systems has become increasingly popular. In this paper, an all-encompassing evaluation of the present advancements in this field is provided, and the possible advantages and di culties of utilizing this technology are deliberated. The first part of the paper presents a brief overview of the di‰erent facial emotion recognition algorithms and how they work. This is followed by a review of the existing research on the use of these algorithms in movie and music recommendation systems. The second part of the paper discusses the potential benefits of using facial emotion recognition in movie and music recommendation systems. These include the ability to make more personalized recommendations based on the user's emotional state, as well as the potential for improving the overall user experience. The third part of the paper examines the challenges and limitations of using facial emotion recognition in movie and music recommendation systems. These include issues related to the accuracy and reliability of the algorithms, as well as concerns about privacy and ethical considerations

Movie Recommendation System Based on Traditional Recommendation Algorithm and CNN Model

As streaming services have expanded in recent years, an excellent recommendation algorithm, as one of the core technologies in movie service, brings huge benefits. Although the current application research of recommendation algorithms is mature, most systems or products usually rely on only one main algorithm. This makes it difficult for the system to overcome the shortcomings of various algorithms and cannot benefit from the combination of multiple recommendation algorithms. In addition, the widely used recommendation models are found to be unable to extract the finer features of users and movies, and the calculation time is very long. Meanwhile, the recommendation results are inaccurate, and they are not user-friendly. In this research, we design and construct a system to recommend movies which is consisted by a model based on a convolutional neural network consisting. By extracting the features of users and movies, we can calculate the direct similarity of different users' movies and then predict movie ratings to give recommendations. When extracting features of users and movies, we refer to traditional algorithms based on content and content. Through the "cosine similarity" and "user movie score matrix", the Item-Based Collaborative Filtering and User-Based Collaborative Filtering can be well implemented. To sum up, our movie recommendation system is based on Convolutional Neural Network (CNN) model and the performance of the system is improved by adopting multiple recommendation algorithms such as content-based recommendation and system filtering. We efficiently trained the neural network and finally built a movie recommendation system with a faster operation speed, although some parts are not perfect.

A Content-based Movie Recommendation System

In this paper, we describe a content-based movie recommendation system and provide an overview of the movie recommendation systems in today's market. Our findings show 1): Summary-Based and Feature-Based movie recommendation systems will provide different recommendation results. 2) Combined recommendation systems result is consistent with the Summary-Based recommendation system but different from the Feature-Based recommendation system. Based on our recommendation system, we also made some innovations and fusion and conducted several control tests to improve the quality of our recommendations.

Multimodal Movie Recommendation System Using Deep Learning

Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieLens datasets were selected to test the effectiveness of our new recommendation algorithm. With the input information, the hidden features of the movies and the users were mined using deep learning to build a deep-learning network algorithm model for training to further predict movie scores. With a learning rate of 0.001, the root mean squared error (RMSE) scores achieved 0.9908 and 0.9096 for test sets of MovieLens 100 K and 1 M datasets, respectively. The scoring prediction results show improved accuracy after incorporating the potential features and connections in multimodal data with deep-learning technology. Compared with the traditional collaborative filtering algorithms, such as user-based collaborative filtering (User-CF), item-based content-based filtering (Item-CF), and singular-value decomposition (SVD) approaches, the multimodal movie recommendation system using deep learning could provide better personalized recommendation results. Meanwhile, the sparse data problem was alleviated to a certain degree. We suggest that the recommendation system can be improved through the combination of the deep-learning technology and the multimodal data analysis.

Multispace Evolutionary Search for Large-Scale Optimization With Applications to Recommender Systems

Large-scale optimization is vital in today’s artificial intelligence (AI) applications for extracting essential knowledge from huge volumes of data. In recent years, to improve the evolutionary algorithms used to solve optimization problems involving a large number of decision variables, many attempts have been made to simplify the problem solution space of a given problem for the evolutionary search. In the literature, the existing approaches can generally be categorized as decomposition-based methods and dimension-reduction-based methods. The former decomposes a large-scale problem into several smaller subproblems, while the latter transforms the original high-dimensional solution space into a low-dimensional space. However, it is worth noting that a given large-scale optimization problem may not always be decomposable, and it is also difficult to guarantee that the global optimum of the original problem is preserved in the reduced low-dimensional problem space. This article, thus, proposes a new search paradigm, namely the multispace evolutionary search, to enhance the existing evolutionary search methods for solving large-scale optimization problems. In contrast to existing approaches that perform an evolutionary search in a single search space, the proposed paradigm is designed to conduct a search in multiple solution spaces that are derived from the given problem, each possessing a unique landscape. The proposed paradigm makes no assumptions about the large-scale optimization problem of interest, such as that the problem is decomposable or that a certain relationship exists among the decision variables. To verify the efficacy of the proposed paradigm, comprehensive empirical studies in comparison to five state-of-the-art algorithms were conducted using the CEC2013 large-scale benchmark problems as well as an AI application in e-commerce, i.e., movie recommendation.

Movie recommender chatbot based on Dialogflow

<p><span lang="EN-US">Currently, the online movie streaming business is growing rapidly, such as Netflix, Disney+, Amazon Prime Video, HBO, and Apple TV. The recommender system helps customers in getting information about movies that are in accordance with their wishes. Meanwhile, the development of messaging platform technology has made it easier for many people to communicate instantly. Utilizing a messaging platform to build a recommender system for movies, provides special benefits because people often access the messaging platform all the time. In the Indonesian language, there are many slang terms that the system must recognize. In this study, we build a chatbot on a messaging platform which users can interact with the system in natural language (in Indonesian language) and get recommendations. We use rule-based and maximum likelihood as a method in natural language processing (NLP), and content-based filtering for the recommendation process. The recommender system interaction is built through a conversation mechanism that will form a conversational recommender system. The interaction is based on a chatbot which is built using Dialogflow and implemented on the telegram. We use the accuracy of recommendations and user satisfaction to evaluate the system performance. The results obtained from the user study indicate that the NLP approach provides a positive experience for users. In addition, the system also produces an accuracy value of 83%.</span></p>

A Systematic Literature Survey on Recommendation System

Abstract: For many applications, particularly in the academic environment and industry, the Recommendation System for Technical Paper Reviewers is very important. This study examines the research trends connecting the highly technical components of recommendation systems employed in various service fields to their commercial aspects. It is a technique that enables the user to identify the information that will be useful to him or her from the variety of facts accessible. In terms of the movie recommendation system, recommendations are made either based on user similarities in collaborative filtering or by considering the user's intended engagement with the content into account content-based filtering. A stronger recommendation system is produced by combining content-based and collaborative filtering, which overcomes the issues that collaborative and content-based filtering typically have. The similarity between users is also determined using a variety of similarity measures in order to make recommendations. We have reviewed cutting-edge approaches to collaborative filtering, content-based filtering, deep learning-based methods, and hybrid approaches in this study for movie recommendation. Additionally, we looked at other similarity measures. Numerous businesses, including Facebook, which suggests friends, LinkedIn, which suggests jobs, Pandora, which suggests music, Netflix, which suggests movies, and Amazon, which suggests purchases, among others, employ recommendation systems to boost their profits and help their clients. This essay primarily focuses on providing a succinct overview of the many approaches and techniques used for movie recommendation in order to investigate the field of recommendation systems research.

Content Based Movie Recommendation System

Abstract: Recommender System is a tool which helps users find the required content and overcome information overload. It predicts interests of users by using Machine Learning algorithms and makes recommendation according to the interest of users. The primary content-based recommender system is the continuation and development of collaborative filtering, which does not need the user’s appraisal for items. Instead, the similarity is calculated based on the data of items that are selected by users, and then make the recommendation appropriately. With the augmentation of machine learning, the current content-based recommender system can build profile for users and products respectively. Building or renewing the profile according to the perusal of items that are bought or seen by users. The system can differentiate the user and the profile of items and then recommend the most resembling products. So, this recommender method that compel user and product directly can’t be brought into collaborative filtering model. The groundwork of content-based algorithm is acquisition and quantitative analysis of the content. The research of acquisition and filtering of text information are fully fledged, many current modified content-based recommender systems make recommendations according to the analysis of text data. This paper introduces content-based recommendation system for the movie websites. There are a lot of factors extracted from the movie, they are diverse and unique, which is also different from other recommender systems. We use these aspects to construct movie model and calculate similarity. We introduce a new outlook for setting weight of features, which improvises the representation of movie recommendations. Finally, we evaluate the approach to illustrate the improvement

Context-Based Patterns in Machine Learning Bias and Fairness Metrics: A Sensitive Attributes-Based Approach

The majority of current approaches for bias and fairness identification or mitigation in machine learning models are applications for a particular issue that fails to account for the connection between the application context and its associated sensitive attributes, which contributes to the recognition of consistent patterns in the application of bias and fairness metrics. This can be used to drive the development of future models, with the sensitive attribute acting as a connecting element to these metrics. Hence, this study aims to analyze patterns in several metrics for identifying bias and fairness, applying the gender-sensitive attribute as a case study, for three different areas of applications in machine learning models: computer vision, natural language processing, and recommendation systems. The gender attribute case study has been used in computer vision, natural language processing, and recommendation systems. The method entailed creating use cases for facial recognition in the FairFace dataset, message toxicity in the Jigsaw dataset, and movie recommendations in the MovieLens100K dataset, then developing models based on the VGG19, BERT, and Wide Deep architectures and evaluating them using the accuracy, precision, recall, and F1-score classification metrics, as well as assessing their outcomes using fourteen fairness metrics. Certain metrics disclosed bias and fairness, while others did not, revealing a consistent pattern for the same sensitive attribute across different application domains, and similarities for the statistical parity, PPR disparity, and error disparity metrics across domains, indicating fairness related to the studied sensitive attribute. Some attributes, on the other hand, did not follow this pattern. As a result, we conclude that the sensitive attribute may play a crucial role in defining the fairness metrics for a specific context.

Decision Support System for Movie Recommendations Based on Multi User Preferences Using the Simple Additive Weighting Method

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 parameters, namely the main genre of a movie,subgenre, movie rating, movie duration, and year of making.

Movie Recommendation System

Movie recommendation system is important Because of its strength to the provide the enhanced entertainment in suggesting the recommended movies. This system suggests bunch of movies to the users based on their likes or the popularities of the movie. In this paper the main goal for a movie recommendation system is to provide a recommended movie to the new user as well as other. It needs movie database to collect all the prime information, like name of movies, their cast names who have work in that movie and their information, reviews of that movies etc.

A multi-objective artificial bee colony approach for profit-aware recommender systems

Movie recommender systems are increasingly present in our daily lives, offering content of interest from streaming providers. Objectives in addition to the liking probability can be proposed to provide movie recommendations. However, there is a lack of recommenders that are aware of the benefit and that address the multi-objective nature of the problem. A profit-aware recommender system based on swarm intelligence in a multi-objective environment (multi-objective artificial bee colony, MOABC) has been designed, implemented, and applied. The proposed approach incorporates new intelligent operators that try to improve both objectives (liking probability and profit) simultaneously in each iteration instead of exclusively using randomness. This increases the quality of the recommendations with respect to the state-of-the-art algorithms. This new proposal has been evaluated using MovieLens datasets, covering different sizes (large, medium, and small). The experiments show that the MOABC performs better than collaborative filtering (CF, a standard in recommender systems) and Non-dominated Sorting Genetic Algorithm II (NSGA-II, the only multi-objective proposal in scientific literature that is profit aware) in terms of accuracy and global profit. Furthermore, statistical analysis shows that the proposed approach generates better and more robust results, also showing that the multi-objective nature of the problem must be exploited.

Behavioral recommendation engine driven by only non-identifiable user data

Most recommendation systems utilize personal data to device personalized recommendations for users. Even though it seems favorable, security risks like data breaches are inevitable. This research proposes a novel reinforcement learning ‘approach’ to recommend users without collecting identifiable data. With only user activity on a session, our proposed method can model and track user behavior and formulate a recommendation system. We conclude that our algorithms demonstrate positive results in capturing user behavior without collecting private data of any kind from the user. The research is two folds. On one hand, we experiment using traditional reinforcement learning techniques (MDP, Q-learning), and on the other hand, we use deep reinforcement learning algorithms (DQN, DDQN, and D3QN) on a movie recommendation scenario. Interestingly, we observe that MDP and D3QN works comparatively better on movie recommendations.

Hybrid Movie Recommendation System Using Machine Learning

This research suggests a hybrid movie recommendation system and optimization approach based on weighted classification and user collaborative filtering algorithm to address the issue that the single model of the standard recommendation system cannot adequately reflect user preferences. The top-N personalized movie recommendations are made by fusing the weighted classification model with the local recommendation model, which is trained based on user clustering, and the sparse linear model, which serves as the fundamental recommendation model. The scoring matrix is transformed into a low-dimensional, dense item category preference matrix based on item category preference, multiple cluster centers are obtained, the distance between each cluster center and the target user is calculated, and the target user is categorized into the closest cluster. Finally, a suggestion list is created using the collaborative filtering algorithm to forecast the scores for the target user's unrated items. The highdimensional rating matrix is transformed into a lowdimensional item category preference matrix, which further reduces the sparsity of the data. The items are then grouped based on item category preference. The recommendation algorithm suggested in this article addresses some of the limitations of a single algorithm model and enhances the suggestion effect, according to experiments using the MovieLens movie dataset.

AI-RADS: Successes and challenges of a novel artificial intelligence curriculum for radiologists across different delivery formats.

Artificial intelligence and data-driven predictive modeling have become increasingly common tools integrated in clinical practice, heralding a new chapter of medicine in the digital era. While these techniques are poised to affect nearly all aspects of medicine, medical education as an institution has languished behind; this has raised concerns that the current training infrastructure is not adequately preparing future physicians for this changing clinical landscape. Our institution attempted to ameliorate this by implementing a novel artificial intelligence in radiology curriculum, "AI-RADS," in two different educational formats: a 7-month lecture series and a one-day workshop intensive. The curriculum was structured around foundational algorithms within artificial intelligence. As most residents have little computer science training, algorithms were initially presented as a series of simple observations around a relatable problem (e.g., fraud detection, movie recommendations, etc.). These observations were later re-framed to illustrate how a machine could apply the underlying concepts to perform clinically relevant tasks in the practice of radiology. Secondary lessons in basic computing, such as data representation/abstraction, were integrated as well. The lessons were ordered such that these algorithms were logical extensions of each other. The 7-month curriculum consisted of seven lectures paired with seven journal clubs, resulting in an AI-focused session every two weeks. The workshop consisted of six hours of content modified for the condensed format, with a final integrative activity. Both formats of the AI-RADS curriculum were well received by learners, with the 7-month version and workshop garnering 9.8/10 and 4.3/5 ratings, respectively, for overall satisfaction. In both, there were increases in perceived understanding of artificial intelligence. In the 7-lecture course, 6/7 lectures achieved statistically significant (P < 0.02) differences, with the final lecture approaching significance (P = 0.07). In the one-day workshop, there was a significant increase in perceived understanding (P = 0.03). As artificial intelligence becomes further enmeshed in clinical practice, it will become critical for physicians to have a basic understanding of how these tools work. Our AI-RADS curriculum demonstrates that it is successful in increasing learner perceived understanding in both an extended and condensed format.

DCF-MLSTM: a deep security content-based filtering scheme using multiplicative BiLSTM for movie recommendation system

Recently, the demand for online and offline recommendation systems has increased drastically. These systems are widely used in tourism, music, and video or movie recommendations. Currently, online movie streaming applications have gained huge attention. Providing better recommendations to the user is a challenging task for these applications. The content-based filtering (CBF) recommender system is a promising technique for these systems. However, traditional systems suffer from challenges such as cold-start problems, sparsity, and scalability. Consequently, we strengthen content-based recommendation algorithms by enriching the user-related and relevant product models with effective tendencies. The majority of previous work on classifiers has been in recommendation systems. To overcome these issues, we present a deep learning model that uses a deep neural network mechanism and a multiplicative BiLSTM model. This scheme uses embedding, weight updating, and preference learning processes to improve the recommendation system's performance. The performance of the proposed approach is measured in terms of MAE, MAP, Precision, Recall, and F-measure. The comparative performance shows that the proposed approach achieves better performance when compared with state-of-art movie recommendation techniques.

Sequence Recommendation based on Deep Learning

Sequence recommendation systems have become increasingly popular in various fields such as movies and social media. These systems aim to predict a user's preferences and interests based on their past behavior and provide them with personalized recommendations. Deep learning, particularly Recurrent Neural Networks (RNNs), have emerged as a powerful tool for sequence recommendation. In this research, we explore the effectiveness of RNNs in movie and Instagram recommendation systems. We investigate and compare the performance of different types of RNNs, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in recommending movies and Instagram posts to users based on their browsing history. Additionally, we study the impact of incorporating additional information such as user's demographics and Instagram hashtags on the performance of the recommendation system. We also evaluate the performance of RNN-based movie and Instagram recommendation systems in comparison to traditional approaches, such as collaborative filtering and content-based filtering, in terms of accuracy and personalization. The findings of this research provide insights into the effectiveness of RNNs in movie and Instagram recommendation systems and contribute to the development of more accurate and personalized recommendations for users.

Plot-aware transformer for recommender systems

&lt;abstract&gt; &lt;p&gt;Plot text is very valuable supporting information in movie recommendations. It has several characteristics: 1) It is rich in content. Each movie often has a document of more than 200 words to describe it, which can give the movie a rich semantic meaning. 2) Objectivity. Plot texts are different from review information. A movie may have thousands of reviews with mixed and conflicting opinions. However, a film has only one plot text, which is fair in tone and does not take a position. Despite its appealing properties and potential for accurate movie portrayal, the lack of a building block for effectively mining plot semantics has led to the marginalization of plot text in the design of movie recommendation algorithms. Therefore, in this paper, we explore the application of the Transformer, currently the best natural language processing module, to learning movie plot texts to help achieve more accurate rating prediction. We propose the "Plot-Aware Transformer" model (PAT) to model the process of "user-movie" rating interaction. We test the PAT model on several movie datasets and demonstrated that the model is competitive. In all tasks, PAT achieves state-of-the-art performance compared to baseline experiments.&lt;/p&gt; &lt;/abstract&gt;

Point Process based time sensitive personalised recommendation

We frequently rely on suggestions in an online context, including those from search engine results, e-commerce product recommendations, movie recommendations, and so forth. These suggestions are made in response to our actions, such as when we search for products, songs, or movies, or at the very least, when we visit the website in order to receive a recommendation. These recommendations do not take into account time-sensitive factors, such as when we often utilise these services during the day or the week. We prefer to repeat some activities, like listening to music at a specific time of day. We presented a methodology in this paper that addresses two critical issues in personalised recommendation. 1) recommending the right items to the right people at the right time, and 2) estimating when a user will return to a service or product after performing repeated actions. The scholarly community has not yet examined this work in personalised recommendations. To do this, we offered the Hawkes process, in which we employed a customised initial intensity based on Hierarchical Poisson Factorization, and for a dynamic activity, we considered a sinusoidal function coupled with exponential effect decay. This is consistent with user activity cycles, such as music listening during a specific time of day. This is the first framework we've used, which is based on a probabilistic matrix factorization