How to combine visual features with tags to improve movie recommendation accuracy?

Previous works have shown the effectiveness of using stylistic visual features, indicative of the movie style, in content-based movie recommendation. However, they have mainly focused on a particular recommendation scenario, i.e., when a new movie is added to the catalogue and no information is available for that movie (New Item scenario). However, the stylistic visual features can be also used when other sources of information is available (Existing Item scenario). In this work, we address the second scenario and propose a hybrid technique that exploits not only the typical content available for the movies (e.g., tags), but also the stylistic visual content extracted form the movie files and fuse them by applying a fusion method called Canonical Correlation Analysis (CCA). Our experiments on a large catalogue of 13K movies have shown very promising results which indicates a considerable improvement of the recommendation quality by using a proper fusion of the stylistic visual features with other type of features

Exploring the Semantic Gap for Movie Recommendations

In the last years, there has been much attention given to the semantic gap problem in multimedia retrieval systems. Much effort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from multimedia content, as low-level features are not considered useful because they deal primarily with representing the perceived content rather than the semantics of it. In this paper, we explore a different point of view by leveraging the gap between low-level and high-level features. We experiment with a recent approach for movie recommendation that extract low-level Mise-en-Scéne features from multimedia content and combine it with high-level features provided by the wisdom of the crowd. To this end, we first performed an offline performance assessment by implementing a pure content-based recommender system with three different versions of the same algorithm, respectively based on (i) conventional movie attributes, (ii) mise-en-scene features, and (iii) a hybrid method that interleaves recommendations based on movie attributes and mise-en-scene features. In a second study, we designed an empirical study involving 100 subjects and collected data regarding the quality perceived by the users. Results from both studies show that the introduction of mise-en-scéne features in conjunction with traditional movie attributes improves both offline and online quality of recommendations

A Connotative Space for Supporting Movie Affective Recommendation

The problem of relating media content to users’affective responses is here addressed. Previous work suggests that a direct mapping of audio-visual properties into emotion categories elicited by films is rather difficult, due to the high variability of individual reactions. To reduce the gap between the objective level of video features and the subjective sphere of emotions, we propose to shift the representation towards the connotative properties of movies, in a space inter-subjectively shared among users. Consequently, the connotative space allows to define, relate and compare affective descriptions of film videos on equal footing. An extensive test involving a significant number of users watching famous movie scenes, suggests that the connotative space can be related to affective categories of a single user. We apply this finding to reach high performance in meeting user’s emotional preferences

Enhancing Children’s Experience with Recommendation Systems

Recommender Systems (RSs) offer a personalized support in exploring large amounts of information, assisting users in decision making about products matching their taste and preferences. Most of the research todate on recommender systems have focused on traditional users, i.e., adult individuals who are able to offer explicit feedback, write reviews, or purchase items themselves. However, children's patterns of attention and interaction are quite different from those of adults. This paper presents the first results of a research-in-progress that can be suited to bridge the barrier between children and a recom-mender system by providing a child-friendly interaction paradigm. Specifically, a web application is developed that employs real-time object recognition on movie thumbnails or DVD cover-photos in a real-time manner. The tangible object can be manipulated by the user and provide input to the system for the purpose of generating movie recommendations. We plan to extend this work to the scenario where the child could ask for a video content showing a related toy (e.g., a car, a plane, the doll of a character that she likes in a cartoon) and the system could generate the videos that matches these implicit preferences expressed by the chil

Movies and meaning: from low-level features to mind reading

When dealing with movies, closing the tremendous discontinuity between low-level features and the richness of semantics in the viewers' cognitive processes, requires a variety of approaches and different perspectives. For instance when attempting to relate movie content to users' affective responses, previous work suggests that a direct mapping of audio-visual properties into elicited emotions is difficult, due to the high variability of individual reactions. To reduce the gap between the objective level of features and the subjective sphere of emotions, we exploit the intermediate representation of the connotative properties of movies: the set of shooting and editing conventions that help in transmitting meaning to the audience. One of these stylistic feature, the shot scale, i.e. the distance of the camera from the subject, effectively regulates theory of mind, indicating that increasing spatial proximity to the character triggers higher occurrence of mental state references in viewers' story descriptions. Movies are also becoming an important stimuli employed in neural decoding, an ambitious line of research within contemporary neuroscience aiming at "mindreading". In this field we address the challenge of producing decoding models for the reconstruction of perceptual contents by combining fMRI data and deep features in a hybrid model able to predict specific video object classes

Affective Recommendation of Movies Based on Selected Connotative Features

The apparent difficulty in assessing emotions elicited by movies and the undeniable high variability in subjects emotional responses to filmic content have been recently tackled by exploring film connotative properties: the set of shooting and editing conventions that help in transmitting meaning to the audience. Connotation provides an intermediate representation which exploits the objectivity of audiovisual descriptors to predict the subjective emotional reaction of single users. This is done without the need of registering users physiological signals neither by employing other people highly variable emotional rates, but just relying on the inter-subjectivity of connotative concepts and on the knowledge of users reactions to similar stimuli. This work extends previous by extracting audiovisual and film grammar descriptors and, driven by users rates on connotative properties, creates a shared framework where movie scenes are placed, compared and recommended according to connotation. We evaluate the potential of the proposed system by asking users to assess the ability of connotation in suggesting filmic content able to target their affective requests

User interface patterns in recommendation-empowered content intensive multimedia applications

Design Patterns (DPs) are acknowledged as powerful conceptual tools to improve design quality and to reduce time and cost of the development process by effect of the reuse of “good” design solutions. In many fields (e.g., software engineering, web engineering, interface design) patterns are widely used by practitioners and are also investigated from a research perspective. Still, they have been seldom explored in the arena of Recommender Systems (RSs). RSs provide suggestions (“recommendations”) for items that are likely to be appropriate for the user profile, and are increasingly adopted in content-intensive multimedia applications to complement traditional forms of search in large information spaces. This paper explores RSs through the lens of User Interface (UI) Design Patterns. We have performed a systematic analysis of 54 recommendation-empowered content-intensive multimedia applications, in order to: (i) discover the occurrences of existing domain independent UI patterns; (ii) identify frequently adopted UI solutions that are not modelled by existing patterns, and define a set of new UI patterns, some of which are specific of the interfaces for recommendation features while others can be useful also in a broader context. The results of our inspection have been discussed with and evaluated by a team of experts, leading to a consolidated set of 14 new patterns that are reported in the paper. Reusing pattern-based design solutions instead of building new solutions from scratch enables novice and expert designers to build good UIs for Recommendation-empowered content intensive multimedia applications more effectively, and ultimately can improve the UX experience in this class of systems. From a broader perspective, our work can stimulate future research bridging Recommender Systems, Web Engineering and Interface Design by means of Design Patterns, and highlights new research directions also discussed in the paper

Lady in Red: Framing the Representation of Women through Mise-En-Scène

Films are popular medium that can reflect and contribute to changes in cultural norms and values.The films represent reality by combining film elements with stories, expression of emotions, and creation of the expression.This study focuses on enabling audiences to view women in films through the aspects of mise-en-scène in a frame.In this study, the researchers applied close textual analysis on scenes of the main female characters in four Thai films of Bhandevanov Devakula, a Thai film director.The four selected films, namely, The Eternity, The Outrage, Jan Dara the Beginning, and Jan Dara the Finale, are samples of the study.Findings suggest that the aspects of mise-en-scène in the films constantly use the red colour in the scenes of the main female characters to show the confidence, non-innocence, or high sexual attractiveness of women. Moreover, red colour is also used to reflect the sexual desire of men.This study is significant because it facilitates understanding of the comprehensive use of each aspect of mise-en-scène. This study also provides an understanding of how mise-en-scène can frame the expression of women in films. This study also demonstrates that the textual analysis approach of a film offers a close reading to facilitate a deep understanding of the meaning of a film through the interaction of all elements.This study has implications for research on film studies, analysis of mise-en-scène, women in the media, and the film industry of Southeast Asia

Exploring Movie Construction & Production: What’s So Exciting about Movies?

Exploring Movie Construction & Production contains eight chapters of the major areas of film construction and production. The discussion covers theme, genre, narrative structure, character portrayal, story, plot, directing style, cinematography, and editing. Important terminology is defined and types of analysis are discussed and demonstrated. An extended example of how a movie description reflects the setting, narrative structure, or directing style is used throughout the book to illustrate building blocks of each theme. This approach to film instruction and analysis has proved beneficial to increasing students’ learning, while enhancing the creativity and critical thinking of the student.https://knightscholar.geneseo.edu/oer-ost/1001/thumbnail.jp