171 research outputs found
MMTF-14K: A Multifaceted Movie Trailer Dataset for Recommendation and Retrieval
No full text<p>The MMTF-14K dataset provides a stable and extensive source for devising and evaluating movie recommender systems. MMTF-14K contains <strong><a href="https://mmprj.github.io/mtrm_dataset/datasets">audio and visual descriptors</a></strong> in addition to ratings and metadata for 13,623 Hollywood-type movie trailers. The dataset therefore facilitates research on content-based recommender systems, where content refers not only to metadata, but specifically to visual and auditory characteristics of movies. The data comes also with several baselines <a href="https://mmprj.github.io/mtrm_dataset/benchmark">benchmarking results</a> for uni-modal and multi-modal recommendation systems. The dataset therefore facilitates research on movie recommendation. In addition, the rich data supports the exploration of other multimedia tasks such as popularity prediction, genre classification, or auto-tagging (aka tag prediction).</p>
<p>The MMTF-14K dataset has been created as a joint research work by <a href="http://www.ir.disco.unimib.it/yashar-deldjoo/">Yashar Deldjoo </a>(Politecnico di Milano, Italy), <a href="http://www.campus.pub.ro/lab7/gconstantin/">Mihai Gabriel Constantin </a>and <a href="http://campus.pub.ro/lab7/bionescu/">Bogdan Ionescu </a>(University Politehnica of Bucharest, Romania), <a href="http://www.cp.jku.at/people/schedl/">Markus Schedl </a>(Johannes Kepler University Linz, Austria), and <a href="https://scholar.google.it/citations?hl=en&user=dTSOPCMAAAAJ&view_op=list_works&sortby=pubdate">Paolo Cremonesi </a>(Politecnico di Milano, Italy).</p>
<p>We would like to acknowledge MovieLens here for providing a stable benchmark dataset of movies containing individual user ratings and metadata which is an enabler for doing research on movie recommendation. Please consider the <a href="http://files.grouplens.org/datasets/movielens/ml-20m-README.html">MovieLens-20M web page</a> for more details on the ratings and tags datasets.</p>
<p>For acknowledgments please use our paper:</p>
<p>@inproceedings{deldjooMMTF14K, <br>
title={MMTF-14K: A Multifaceted Movie Trailer Feature Dataset for Recommendation and Retrieval}, <br>
author={Deldjoo, Yashar and Constantin, Mihai Gabriel and Schedl, Markus and Ionescu, Bogdan and Cremonesi, Paolo}, <br>
booktitle={Proceedings of the 9th ACM Multimedia Systems Conference}, <br>
year={2018}, <br>
organization={ACM}}</p>
<p>For further inquiries you are free to contact Yashar Deldjoo through his email: <a href="mailto:[email protected]">[email protected] </a>.</p>The link to the dataset can be also found in: https://mmprj.github.io/mtrm_dataset/inde
Enhancing Video Recommendation Using Multimedia Content
Full text linkVideo recordings are complex media types. When we watch a movie, we can effortlessly register a lot of details conveyed to us (by the author) through different multimedia channels, in particular, the audio and visual modalities. To date, majority of movie recommender systems use collaborative filtering (CF) models or content-based filtering (CBF) relying on metadata (e.g., editorial such as genre or wisdom of the crowd such as user-generated tags) at their core since they are human-generated and are assumed to cover the ‘content semantics’ of movies by a great degree. The information obtained from multimedia content and learning from muli-modal sources (e.g., audio, visual and metadata) on the other hand, offers the possibility of uncovering relationships between modalities and obtaining an in-depth understanding of natural phenomena occurring in a video. These discerning characteristics of heterogeneous feature sets meet users’ differing information needs. In the context of this Ph.D. thesis [9], which is briefly summarized in the current extended abstract, approaches to automated extraction of multimedia information from videos and their integration with video recommender systems have been elaborated, implemented, and analyzed. Variety of tasks related to movie recommendation using multimedia content have been studied. The results of this thesis can motivate the fact that recommender system research can benefit from knowledge in multimedia signal processing and machine learning established over the last decades for solving various recommendation tasks
A low-cost infrared-optical head tracking solution for virtual 3D audio environment using the Nintendo Wii-remote
No full textA virtual audio system needs to track both the translation and rotation of an observer to simulate a realistic sound environment. Current existing virtual audio systems either do not fully account for rotation or require the user to carry a controller at all times. This paper presents a three-dimensional (3D) virtual audio system with a head tracking unit that fully accounts for both translation and rotation of a user without the need of a controller. The system consists of four infrared light-emitting diodes on the user's headset together with a Wii-remote to track their movement through a graphical user interface. The system was tested with a simulation that used a pinhole camera model to map the 3D-coordinates of each diode onto the two-dimensional (2D) camera plane. This simulation of 3D head movement yields 2D coordinate data that were put into the tracking algorithm and to reproduced the 3D motion. The results from a prototype system, assembled to track the 3D movements of a rigid object were also consistent with the simulation results. The tracking system has been integrated into an Ericsson 3D-audio system and its effectiveness has been verified in a headtracked virtual 3D-audio system with real-time animating graphical outputs
Retrieving Relevant and Diverse Movie Clips Using the MFVCD-7K Multifaceted Video Clip Dataset
No full textMultimedia search is an emerging area in information retrieval (IR) and recommender systems (RS) research. However, there is a lack of standardized audiovisual datasets that include rich content descriptors, which are a necessity in content-based IR and RS. The contributions of this paper are twofold: First, we present a new multimedia dataset of movie clips, named MFVCD-7K Multifaceted Video Clip Dataset, that comes with low-level and semantic multimodal descriptions of their content (textual, audio, and visual). In addition, we showcase the use of this dataset for a novel content-based video clip retrieval and result diversification task we introduce. We investigate baseline algorithms for retrieval and diversification, and provide experimental results according to relevance and diversity measures. We believe that both dataset and baseline results constitute an important asset for the IR, RS, and multimedia communities
Towards Multi-Modal Conversational Information Seeking
No full textRecent research on conversational information seeking (CIS) mostly focuses on uni-modal interactions and information items. This perspective paper highlights the importance of moving towards developing and evaluating multi-modal conversational information seeking (MMCIS) systems as they enable us to leverage richer context, overcome errors, and increase accessibility. We bridge the gap between the multi-modal and CIS research and provide a formal definition for MMCIS. We discuss potential opportunities and research challenges in designing, implementing, and evaluating MMCIS systems. Based on this research, we propose and implement a practical open-source framework for facilitating MMCIS research
PyCPFair: A framework for consumer and producer fairness in recommender systems
No full textFairness is a critical problem not only in scientific research but also in many real-life applications. Recent work in recommender systems mainly focuses on fairness in recommendations as an important aspect of measuring recommendations quality. This paper presents PyCPFair, a Python-based framework for consumer and producer fairness in recommender systems. The ease-of-use and flexibility of the presented framework have allowed reducing the development time and increased evaluation strategies of fairness models for recommender systems. The PyCPFair is written mainly in Python and the optimization solution is provided using MIP interface and Gurobi solver
ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPT
No full textA Novel Fuzzy-Based Smoke Detection System Using Dynamic and Static Smoke Features
No full textAutomatic fire surveillance is an important task for providing emergency response in the event of unexpected fire hazards. Early detection of fire can substantially mitigate the ecological or economical costs associated with a fire disaster. In this regard, as smoke usually always precedes fire, an intelligent smoke detection system is proposed that exploits a Fuzzy Inference System (FIS) in order to aggregate the features of smoke. In addition, robust smoke feature detection algorithms are implemented that take into account both dynamic and static characteristics of smoke. The smoke features include motion, motion orientation (estimated by using the accumulation of motion) for the former and texture for the latter. Experimental results on different video frames show that the proposed smoke detection system has robust performance on detecting the existence of smoke which shows the effectiveness of the proposed smoke detection system
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