Movie Selection Research Articles

MOVIEMATE: MOVIE RECOMMENDATION SYSTEM

Abstract— Nowadays, recommendation systems have revolutionized the way we search for things of interest. These systems employ an information filtering approach to anticipate user preferences. Commonly applied in areas such as books, news, articles, music, videos, and movies, recommendation systems play a pivotal role. In this study, we introduce MOVIEMATE, a movie recommendation system. Using content-based approach, MOVIEMATE examines information provided by users to recommend movies customized to their individual preferences. MOVIEMATE streamlines the movie selection process, leveraging the collective movie experiences of other users to efficiently deliver personalized recommendations. Developed in Python using Jupyter Notebook and Streamlit, this system harnesses various types of user data, item information, and past transactions stored in dedicated databases to generate recommendations. Navigating through these recommendations, users can effortlessly discover movies that align with their preferences. Keywords – recommendation system, recommender system, movies, Content-based filtering

Hierarchical representation learning for next basket recommendation

The task of next basket recommendation is pivotal for recommender systems. It involves predicting user actions, such as the next product purchase or movie selection, by exploring sequential purchase behavior and integrating users’ general preferences. These elements may converge and influence users’ subsequent choices. The challenge intensifies with the presence of varied user purchase sequences in the training set, as indiscriminate incorporation of these sequences can introduce superfluous noise. In response to these challenges, we propose an innovative approach: the Selective Hierarchical Representation Model (SHRM). This model effectively integrates transactional data and user profiles to discern both sequential purchase transactions and general user preferences. The SHRM’s adaptability, particularly in employing nonlinear aggregation operations on user representations, enables it to model complex interactions among various influencing factors. Notably, the SHRM employs a Recurrent Neural Network (RNN) to capture extended dependencies in recent purchasing activities. Moreover, it incorporates an innovative sequence similarity task, grounded in a k-plet sampling strategy. This strategy clusters similar sequences, significantly mitigating the learning process’s noise impact. Through empirical validation on three diverse real-world datasets, we demonstrate that our model consistently surpasses leading benchmarks across various evaluation metrics, establishing a new standard in next-basket recommendation.

StreamBoostE: A Hybrid Boosting-Collaborative Filter Scheme for Adaptive User-Item Recommender for Streaming Services

With home entertainment, selecting the perfect movie is a pervasive challenge, amplified by many streaming platforms like Netflix and Amazon Prime. This study advances a movie recommender system with collaborative filtering approach as implemented in Python titled StreamBoostE. We used the user-based and item-based similarity schemes on feature embedding to aid faster model construction and training for the tree-based gradient boosting ensemble. Employing both user- and item-based collaborative filtering with cosine similarity to ease feature embedding, the system assesses movies inter-relations via personalized user interest and preferences as submitted user titles with a focus on movie genre classification. Results shows the ensemble yields a recommender prediction accuracy of 0.9984 with F1 of 0.996. The major contribution of StreamBoostE is in its capability to expedite the movie selection process when integrated using flask API and streamlit for cross-channel integration in web-based platforms. It presents users with a list of top-10 recommended movies by genre similarity. The XGBoost ensemble performed best with the user-/item-based collaborative filtering scheme fused with feature embedding approach as a sampling method. Keywords: Random Forest, SMOTE, credit card fraud detection, feature selection, imbalanced dataset Aims Research Journal Reference Format: Atuduhor, R.R., Okpor, M.D., Yoro, R.E., Odiakaose, C.C., Emordi, F.U., Ojugo, A.A., Ako, R.E., Geteloma, V.O., Ejeh, P.O., Abere, R.A., Ifioko, A.M., & Brizimor, S.E. (2024): StreamBoostE: A Hybrid Boosting-Collaborative Filter Scheme for Adaptive User-Item Recommender for Streaming Services. Advances in Multidisciplinary and Scientific Research Journal Vol. 10. No. 2. Pp 89-106. www.isteams.net/aimsjournal. dx.doi.org/10.22624/AIMS/V10N2P8

Content-based Recommender System Using Cosine Similarity

Abstract: In the quest for a more tailored cinematic experience, this paper presents a Movie Recommender System that harnesses the power of Cosine Similarity within a machine learning framework. The system’s cornerstone is its ability to discern nuanced user preferences and suggest films that resonate on a personal level. Employing Python, the research delineates a methodology that encompasses data collection, preprocessing, and feature extraction from a comprehensive dataset of movies. The crux of the system lies in its application of the cosine similarity algorithm, which calculates the affinity between movies and users based on shared characteristics such as genre, director, plot, title and cast. This approach is rigorously evaluated using metrics like precision, recall, and accuracy, ensuring the recommender’s reliability. The experimental design bifurcates the dataset into training and testing subsets, fortifying the system’s robustness. The paper’s findings illuminate the system’s efficacy in delivering precise and individualized recommendations, thereby augmenting the user’s movie selection process. It underscores the potential of the system to revolutionize content discovery and consumption in the entertainment domain. The conclusion encapsulates the project’s ambition to refine the recommender system further by integrating diverse machine learning algorithms and scaling its capabilities.

Sentiment Analysis for Movie Recommendation

Our research proposes a movie recommendation system integrating sentiment analysis with LSTM neural networks. By extracting sentiments from user reviews, LSTM captures nuanced preferences, enhancing recommendation accuracy. Leveraging real-world movie datasets, our approach outperforms existing methods, offering personalized recommendations aligned with user sentiments. Through this fusion of NLP and deep learning, we strive to streamline movie selection, providing users with a more tailored and satisfying experience. Keywords—Sentiment Analysis, Natural Language Processing,, Long Short-Term Memory, Recurrent Neural Network

SOM and Hybrid Filtering: Pioneering Next-Gen Movie Recommendations in the Entertainment Industry

In an age where digital connectivity is increasingly shaping entertainment content, personalized movie recommendations play a pivotal role in enhancing user satisfaction and engagement. This research introduces an innovative approach utilizing Enhanced Self-Organizing Maps (SOM) to streamline movie selection processes. Self-Organizing Maps (SOMs), a type of unsupervised neural network architecture, are particularly adept at discerning intricate data patterns, making them valuable assets in recommendation systems. The methodology outlined in this paper commences with gathering user-movie interaction data, including user feedback and movie characteristics, which is standardized to ensure consistency before model training. Leveraging its adaptable learning rate and neighborhood function, the Enhanced SOM effectively identifies subtle data nuances. Personalized movie suggestions are then generated by exploiting the Enhanced SOM's capacity to identify similar users and films. Integration of hybrid filtering techniques enriches recommendation quality, blending collaborative filtering algorithms, which leverage user-item interactions, with content-based filtering, which utilizes movie attributes such as genres and descriptions. This amalgamation results in suggestions that harmoniously combine diverse filtering methodologies. The proposed solution's efficacy is rigorously evaluated by comparing suggestion accuracy and user satisfaction against predefined benchmarks. Extensive real-world dataset testing corroborates the effectiveness of the Enhanced SOM-based movie recommendation approach. Furthermore, the system offers flexibility through options for parameter adjustment, grid size variations, and neighborhood function modifications to further refine recommendation accuracy. Collectively, these elements underscore the efficacy of the proposed method in furnishing tailored movie recommendations. When coupled with hybrid filtering techniques, the implementation of Enhanced SOMs emerges as a reliable model for content platforms seeking to enhance user experiences by delivering precise movie recommendations, coupled with scalability and adaptability.

Research on the Impact of Parental Movie Choices on Children in China

In the context of growing global awareness regarding the influence of media content on young audiences, this paper focuses on the significant subject of film rating systems. Specifically, it explores the intricate landscape of these systems, their implementation across various countries, and the unique absence of such a system in China. Through an extensive analysis of existing rating systems, including those in the United States, the UK, and India, the study illuminates their influence on global cinema and cultural perceptions. Focusing on China, the paper employs a detailed survey of parents experiences and opinions regarding movie selection for children in the absence of a formal rating system. Findings reveal that a majority of parents acknowledge that movies have an impact on children. Notably, parents express a strong desire for the implementation of a movie rating system in China to guide their choices and prevent the exposure of their children to adult or violent content. However, the study also uncovers potential challenges and complexities in designing a system that balances protection, choice, and cultural relevance. The paper concludes by advocating for a well-considered film rating system in China that aligns with international norms yet is sensitive to local values, aiming to foster a responsible and diverse cinematic culture.

An Emotionally Intuitive Fuzzy TODIM Methodology for Decision Making Based on Online Reviews: Insights from Movie Rankings

With the burgeoning growth of the internet, online evaluation systems have become increasingly pivotal in shaping consumer decision making. In this context, this study introduces an intuitionistic fuzzy TODIM (an acronym in Portuguese for interactive and multicriteria decision making) methodology to rank products based on online reviews. Our approach aims to enhance user decision making efficiency and address the prevalent issue of information overload. Initially, we devised a product attribute emotion quantification framework within the confines of the intuitionistic fuzzy paradigm. This allows for the transformation of online reviews into exact functional outputs via our advanced intuitionistic fuzzy scoring mechanism and its associated precise function. Following this, we take into account the inherent correlation among product attributes, leading to the development of an attribute-associated intuitionistic fuzzy model. This model further ascertains the dominance degree of alternative products. Moreover, by integrating the risk aversion factor, we can derive a hierarchical structure for alternative products, aiding in the prioritization process. Finally, this paper validates the proposed method using movie sequencing as a case study. The results show that the proposed method, which takes into account the emotional tendencies of different attributes in a movie and the different preferences of viewers in the attribute weighting and movie selection process, is more reasonable than methods proposed in previous studies.

Movie Recommendation Based System Using Time Series Data

Finding the right movie from a wide selection can be difficult, leading to frustration and wasted time. Recommendation systems offer a solution by providing personalized movie recommendations based on users' interests and preferences. These systems use data analytics, machine learning algorithms and temporal analysis techniques to understand user behavior and provide accurate recommendations. Collaborative filtering algorithms identify similarities between users or movies, while content-based filtering separates movie features based on user preferences. Time series analysis methods collect temporal patterns for dynamic recommendations. The results of the literature review support the effectiveness of movie recommendation systems based on time series data, showing their ability to provide accurate recommendations despite changing information and changing preferences. Real-time data collection improves system efficiency. Overall, the proposed solution aims to improve the movie selection process, save users time and effort, and at the same time improve the movie viewing experience.

Content-Based Movie Recommendation System: An Enhanced Approach to Personalized Movie Recommendations

With the exponential growth of digital media platforms and the vast amount of available movie content, users are often overwhelmed when selecting movies that match their preferences. Recommender systems have emerged as an effective solution to assist users in discovering relevant and enjoyable movies. Among these systems, content-based recommendation approaches have gained popularity due to their ability to recommend items based on the content characteristics of movies, such as genres, actors, directors, and plot summaries. The first stage of our system involves the collection and preprocessing of movie metadata from various sources, including genres, actors, directors, and plot summaries. Feature extraction techniques are applied to transform the textual information into meaningful representations that capture the essential characteristics of each movie. Next, a content-based filtering algorithm is employed to compute similarity scores between the user's movie preferences and the extracted features of the available movies. The proposed approach contributes to the advancement of movie recommendation systems and has the potential to enhance user engagement and satisfaction in movie selection.

Sistem Rekomendasi Film Dengan Pendekatan Ontologi

Movie is one of the easiest and cheapest entertainment human can experience. Nevertheless, there is an abundant amount of movies to watch. In the US and Canada alone, there are 403 movies produced in 2021. That is a huge amount of movies one person can watch. Most of people usually confused in determining which movies to watch, especially after watching a movie that truly suits their taste. Determining a decision in choosing a movie to watch requires a recommendation system. The recommendation system will provide decisions with good accuracy if it is collaborated with the Semantic Web using ontologies. In this study, researchers aims to build a movie ontology design which will later be used as a processing database in a movie selection recommendation system. In building the ontology, researcher requires the Methontology method. The methontology stages are performing the stages of specification, knowledge acquisition, conseptualizationo, integration, implementation, evaluation, and documentation.

Development of Online Movie Recommendation System based on Neighborhood-based Collaborative Filtering

The recommendation system integrated in movie streaming provides relevant information to viewers predicted by viewers’ past behaviors. There are basically two methods, Content-Based Filtering and Collaborative Filtering. In this article, our focus is on the second method which is based on memory, namely Neighborhood-based Collaborative Filtering (NBCF), to make movie recommendations to users given users’ information. Simultaneously, we have built an online movie website and integrated the movie recommendation system based on NBCF to assist users in movie selection. In the process of building the website, apart from building diagram of movie recommendation system’s functions, class diagram of movie recommendation function, sequence diagram of movie recommendation function, we also build a user-recommended movie model based on the Movies Lens[9] dataset for a fairly high accuracy, which is 99%.

A study on university learners’ movie selection criteria in self-directed English learning using films

A study on university learners’ movie selection criteria in self-directed English learning using films

Towards Personalized Movie Selection for Wellness: Investigating Event-Inspired Movies

ABSTRACT Movie recommender systems help users to navigate the magnitude and diversity of movies that people have easy access to nowadays, especially with online streaming services. However, current systems personalize recommendations primarily based on what they think a specific user would like or prefer using information about how similar the users are to other users. Prior work has suggested that this could lead to a ‘filter bubble’ whereby users only watch movies that they are comfortable with. This paper presents the novel concept of event-inspired movies that draw on users’ highly specific everyday life experiences to recommend movies that are meaningful to them. To conduct the study, we developed a systematic process to align movies with viewer variables. Meaningful movies have been said to have therapeutic potential to possibly affect one’s sense of overall well-being. We conducted a study with 24 participants to explore how users engage with event-inspired movies, whether event-inspired movies are perceived as more relevant, and their potential to affect users’ sense of life satisfaction. In so doing, we also contribute a process and insights on how to operationalize event-inspired movies that can inform the implementation of a movie recommender system situated within the proposed paradigm.

국제 영화선택속성이 축제 이미지, 관여도, 만족도에 미치는 영향 : 전주국제영화제 중심으로

This study collected 209 copies of Jeonju International Film Festival in Jeonju, Jeollabuk-do, Korea. In the empirical analysis of data on the influence of the selection attributes of the International Film Festival on the festival image, involvement and satisfaction, frequency analysis, validity, reliability analysis, and multiple regression analysis were conducted using the social science statistical program SPSS 20.0. First, Jeonju's international movie selection attribute was analyzed to have a significant influence on convenience and kindness in the festival image (emotional), but not attractive. The Jeonju international film selection attribute was analyzed to have a significant influence on the festival image (cognitive). Second, Jeonju international movie selection attribute was found to have a significant influence on kindness and convenience. Third, it was analyzed that Jeonju international movie selection attribute was significant for satisfaction. Fourth, the festival image was analyzed to be significant both involvement and satisfaction. Finally, involvement was analyzed to be significant for satisfaction. This study expects that the selection attribute and festival image of Jeonju International Film will be established, thereby increasing tourists' involvement and satisfaction in film festivals, and suggesting the revitalization of regional economy will be the basis for further research.

CrowdWatcher: an open-source platform to catch the eye of the crowd

This paper presents the open-source eye tracking platform CrowdWatcher. It enables researchers to measure gaze location and user engagement in a crowdsourcing context through traditional RGB webcams. The proposed platform particularly advances the field of Quality of Experience (QoE) research, as it allows the experimenter to collect remotely and with very limited effort novel information from crowds of participants, such as their commitment towards a task, attention and decision-making processes. Two different experiments are described that were conducted to demonstrate the platform’s potential. The first experiment addresses the measurement of participants’ behavior while performing a movie selection task. Results show that the platform provides complementary information to traditional self-reported data by taking gaze analysis into account. This is of particular relevance, since in a crowdsourcing context decision processes and attention are difficult to assess, and there is often limited control over the engagement of the test user with the task. A second experiment is conducted in the scenario of a multimedia QoE test. Prediction accuracy is compared to a professional infrared eye tracker. While CrowdWatcher performs less well than the professional eye tracker, it is still able to collect valuable gaze information in the far more challenging environment of crowdsourcing. As an outlook to further application domains, the usage of the platform to measure user engagement allows participants who do not pay attention to the task to be identified.

A Soft Set Approach for Handling Conflict Situation on Movie Selection

Conflict analysis plays an important role in the fields of politics, military operations, economics, business management, games, urban planning, management negotiations and etcetera. Computational intelligence model such as rough set theory has been used in managing conflict situations which have the ability to handle uncertainties. However, there is a great concern in the computational time of the rough set approach in determining strength, certainty and coverage of conflicts. Motivated by the fact that every rough set approach can be represented using soft set theory, we derived an alternative method based on the concept of co-occurrence from multi-soft sets to handle conflict situations. We first used an illustrative example of a movie selection problem to demonstrate the proposed approach and provide an extensive elaboration using a publicly available dataset. Our motivation is to provide a new measure based on support, strength, certainty and coverage of soft set on movie selection problem. Our findings have revealed that the proposed approach achieved less computational time when compared with the rough set-based approach of up to 8.05%. One potential application of the proposed approach is the domain of recommendation systems. The proposed approach can be used to easily identify users/items nearest neighbours based on support, strength, certainty and coverage, which is crucial for the success of recommendation systems.

Cross-cultural Differences in Movie Selection. Decision-making of German, U.S., and Singaporean Media Users for Video-on-Demand Movies

ABSTRACTThis article investigates the process of individual decision-making for movies on video-on-demand platforms from a cross-cultural perspective. Models of decision-making and movie selection act as theoretical underpinning. The article focuses on the information search phase and examines media consumers' information use during movie selection. Empirically the article relies on an online survey among N = 694 German, the U.S., and Singaporean students. Results indicate that participants considered only a limited amount of information before carrying out their choice. Participants shared a preference for product-related cues over crowd-related cues signaling previous user-experiences. With respect to cross-cultural differences, results point to a higher amount of crowd-related information used by Singaporean participants.

Dissecting movie performance across multiple distribution channels: An elastic justification theory perspective

The recent emergence of multiple channels such as Internet protocol television (IPTV) and online video-on-demand (VOD) has transformed sequential movie distribution structures into simultaneous ones. We analyze the drivers of movie performance success in terms of box office, IPTV, and online VOD services amid these changed market conditions. We use elastic justification theory and consider diverse consumer risks to explain how movie consumers exhibit different behaviors for different movie channels when facing different degrees and types of risk. We classify the antecedents of movie selection into justifiable (risk-hedging) factors such as viewer rating, scenario familiarity, star power, and country of origin and unjustifiable (tempting and appealing) factors, including expert rating and genre. By considering various types and degrees of risk in each channel based on elastic justification theory, this study offers insights that should lead to more effective movie distribution across channels. This study is a first step toward paving the way for advanced movie distribution strategies for producers and distributors.

How people utilise tweets on movie selection? The reverse effects of e-WoM valence on movie sales

How people utilise tweets on movie selection? The reverse effects of e-WoM valence on movie sales