Movie Attributes Research Articles

Optimizing User Satisfaction in Movie Recommendations Using Variable Learning Rates and Dynamic Neighborhood Functions in SOMs

Customized movie recommendations are crucial in elevating user satisfaction and engagement in the era of vast online entertainment options. This study presents an innovative approach utilizing Enhanced Self-Organizing Maps (SOMs) for movie categorization. SOMs, as unsupervised neural networks, are highly effective in recommendation systems due to their ability to identify intricate data patterns accurately. The proposed method involves collecting user-movie interaction data, such as user ratings and movie attributes. Data standardization is performed to ensure consistency before training the refined SOM. By integrating variable learning rates and dynamic Neighborhood functions, the advanced SOM can uncover complex patterns within datasets, thus enhancing the accuracy of personalized movie recommendations by identifying meaningful connections between users and films. To further improve recommendation quality, hybrid filtering techniques are employed, combining content-based filtering, which considers movie characteristics like genre and description, with collaborative filtering algorithms that analyze user-item interactions to expand the range of recommended films. This integrated approach allows for the generation of user-movie matrices by employing SVD collaborative filtering to give precedence to movie recommendations. The hybrid technique demonstrates superior performance compared to earlier models, attaining an RMSE of 0.410, MAE of 0.211, precision of 92.09%, recall of 93.12%, and an F1-score of 92.15%, consequently offering very accurate movie recommendations. Subsequent studies could concentrate on improving personalised recommendations by integrating supplementary contextual data.

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.

COMPARATIVE ANALYSIS OF DATA MINING TECHNIQUES FOR MOVIE PREDICTION

The rate at which movies are being produced is increasing at exponential rates and it has become pertinent to ascertain success rate since the investment that goes into these movie creation runs in millions of dollars. A number of data mining-based methods, ranging from Support Vector Machine (SVM) to logistic regression, have been proposed with a varying level of success with SVM showing the most promising results. This paper aims to carry out a comparative analysis of the performance of Gradient Boosting and SVM algorithms in optimizing the prediction of movie success. The study developed a framework for the research methodology; the dataset used contained 33 movie attributes and 838 entries. The dataset was cleaned with six attributes; features were identified and selected from the datasets using four methods. These methods include: Analysis of Variance (ANOVA), Lasso Regularization, Combination of Lasso Regularization and Random Forest (RF). Model Formulation were done using Support Vector Machine (SVM) and Gradient Boosting Algorithm and the performance evaluation of the developed predictive models was done using accuracy, precision and recall values. The results shows that the accuracy of the Gradient Boosting algorithm is around 100%, SVM-Linear is 86 %, SVM-Poly is 88%, SVM-RBF is 88% and SVM-Sigmoid is 72%. The study concluded that Gradient Boosting algorithm is more robust in predicting movie success. Also recommended that comparison should be done with different machine learning techniques.

How viewing experience drives moviegoers on over the top platform: Investigating the patronized intention

The emerging market of over the top (OTT) streaming platform has evolved the movie industry to a new challenge. Providing moviegoers with a good viewing experience is critical for success in the highly competitive OTT market. This study aimed to examine the antecedent of the viewing experience in the context of the OTT platform and the mediating role of viewing experience toward behavioral intentions in Indonesia. In conjunction with the viewing experience, this study also includes social media exposure and fear of missing out (FOMO) to predict behavioral intentions. PLS-SEM method was used to analyze the empirical data gathered through purposive sampling. A total of 438 eligible samples were obtained through a questionnaire distributed online. The results indicate that viewing experience is mainly influenced by movie attributes. Viewing experience also mediates behavioral intentions with an R-squared value of 0.629, indicating a strong effect. The theoretical and managerial implications of the study results are discussed. The study also aimed to ascertain how sound viewing experience, social media exposure, and fear of missing out predicts the intention to recommend movies, intention to recommend platforms, and intention to continue subscribing. Lastly, this study assessed which antecedent of viewing experience has the most significant impact on forming a good viewing experience.

Research on Online Reservation Preference of Hotel Consumers Based on Joint Analysis Method

With the rapid development of e-commerce, the online booking of hotel websites has become a very common way of booking. With a large number of tourists booking hotels online, with huge turnover, many online travel agencies pay more and more attention to the development, operation. and maintenance of the websites. For those online booking websites which provide hotel reservation information services, it is very important for their survival and development that they can satisfy customers, reduce their risk perception, and enhance their repeated purchase behavior in the process of hotel booking. Therefore, it is necessary to study the quality and operation efficiency of online hotel booking website. Based on the theory of consumer behavior, consumer preference, and product attributes, this article determines that the consumer preference for online hotel booking can be studied through consumer preferences for movie attributes. The basic theory of joint analysis method is studied, and the online reservation consumer preference theory is analyzed. Finally, a case study is carried out, and the results show that preferences of a consumer online reservation hotel can provide a favorable basis for making proper measurements.

Impact of Promotional Strategies on Viewers of Kollywood Movies

Indian Movie Industry had so many ups and downs since 19th century. Industry had movies with great success along with high box office collection records, while some were not even noticed by the viewers. Tempting all of them to movie theatre need well planned strategies. In order to determine the impact of promotional strategy the researcher has created some constructs namely TV advertisements, movie trailers, posters and print advertisements, social networking sites, in-house movie theatre advertisements, word-of-mouth/e- wom, movie attributes, road shows and flash mobs, you tube viral videos, video games, mobile apps, product placements, reality shows, audio launch, making videos. It is concluded from the present study that promotional strategies being followed in Kollywood movies creating great impact on movie watchers. Out of the major promotional strategies considered for this study, the most preferred promotional factors that influence the respondents to watch movies are movie attribute, movie trailers and social networking sites, where movie watchers are most aware on the creative aspects of product placements, you tube viral videos, and reality shows.

Consumption of Movie Experience: Cognitive and Affective Approaches

ABSTRACTThis study examines the structural relationships among film quality, value, and satisfaction on the perspective of moviegoers. The experience economy concept is employed as the theoretical framework, where moviegoers’ cognitive and affective evaluations are projected in fulfilling their perceived value. Moviegoers’ perceived movie attributes and experiences are assessed to evaluate the moviegoers’ satisfactions through functional and emotional values. In particular, this article investigates how four dimensions of experience mediate the effect of movie attributes on perceived value and satisfaction. A total of 565 samples were analyzed with structural equation modeling to investigate the relationship of ‘quality—value—satisfaction.’ The findings show that two movie attributes (core and peripheral) affect three dimensions of experience (entertainment, educational, and escapist), which in turn influence functional and emotional values, and moviegoers’ satisfaction. The results also confirm that emotional value is a more powerful predictor of moviegoers’ satisfaction than functional value.

When Kerry Met Sally: Politics and Perceptions in the Demand for Movies

Movie producers and exhibitors make various decisions requiring an understanding of moviegoer's preferences at the local level. Two examples of such decisions are exhibitors' allocation of screens to movies and producers' allocation of advertising across different regions of the country. This study presents a predictive model of local demand for movies with two unique features. First, arguing that consumers' political tendencies have an unutilized predictive power for marketing models, we allow consumers' heterogeneity to depend on their voting tendencies. Second, instead of relying on the commonly used genre classifications to characterize movies, we estimate latent movie attributes. These attributes are not determined a priori by industry professionals but rather reflect consumers' perceptions, as revealed by their moviegoing behavior. Box-office data over five years from 25 counties in the U.S. Midwest provide support for this model. First, consumers' preferences are related to their political tendencies. For example, we find that counties that voted for congressional Republicans prefer movies starring young, Caucasian, female actors over those starring African American, male actors. Second, perceived attributes provide new insights into consumers' preferences. For example, one of these attributes is the movie's degree of seriousness. Finally, and most importantly, the two improvements proposed here have a meaningful impact on forecasting error, decreasing it by 12.6%. This paper was accepted by Pradeep Chintagunta, marketing.

Modeling Multichannel Home Video Demand in the U.S. Motion Picture Industry

The U.S. motion picture industry has become increasingly reliant on posttheatrical channel profits. Two often-cited drivers of these profits are cross-channel substitution among posttheatrical channels and seasonality in consumer preferences for any movie. The authors use a differentiated products version of the multiplicative competitive interaction model to investigate these two phenomena. They estimate the model using data from 2000 and 2001 on two posttheatrical channels in the U.S. market: purchase and rental home viewing channels. Contrary to expectations based on business press commentary, after controlling for seasonality and movie attributes, the authors find low cross-channel price and availability elasticity for both channels. To measure the extent of cross-channel cannibalization, they simulate a 28-day window of sequential release with either purchase or rental channel going first. They find that windowing reduces the sum of revenues across both channels, because more consumers choose to not purchase or rent when faced with older movies in their favored channel rather than to switch to the alternative channel with newer movies.

Understanding preferences for motion pictures

Of interest in the consumer behavior field are the drivers of experiential and aesthetic consumption. This paper considers these questions in the context of the motion picture industry. Most motion picture industry studies use secondary data to elucidate a relationship between movie attributes (genre, star power, critical reviews, distribution strategy, etc.) and box-office revenues. The study gathers primary data from 225 survey respondents in New Zealand to further understand the factors influencing the purchase decision-making process of movie-going consumers. The study uses a factor-analytic approach to map the different genres in attribute space, and to understand the drivers of choice. Overall, the results show genre, movies based on true stories, critical reviews, word-of-mouth, country of origin, pricing strategy as well as star and director power significantly impact consumers' movie choices.

Bayesian belief network for box-office performance: A case study on Korean movies

Due to their definition as experience goods with short product lifetime cycles, it is difficult to forecast the demand for motion pictures. Nevertheless, producers and distributors of new movies need to forecast box-office results in an attempt to reduce the uncertainty in the motion picture business. Previous research demonstrated the ability of certain movie attributes such as early box-office data and release season to forecast box-office revenues. However, no previous research has focused on the causal relationship among various movie attributes, which have the potential to increase the accuracy of box-office predictions. In this paper a Bayesian belief network (BBN), which is known as a causal belief network, is constructed to investigate the causal relationship among various movie attributes in the performance prediction of box-office success. Subsequently, sensitivity analysis is conducted to determine those attributes most critically related to box-office performance. Finally, the probability of a movie's box-office success is computed using the BBN model based on the domain knowledge from the value chain of theoretical motion pictures. The results confirm the improved forecasting accuracy of the BBN model compared to artificial neural network and decision tree.