@conference {874,
	title = {A Multimodal Pipeline for Personalized Recommendations from Greek YouTube Content: Integrating Automatic Speech Recognition and Aspect-Based Sentiment Analysis},
	booktitle = {Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications},
	year = {2026},
	publisher = {IEEE Xplore},
	organization = {IEEE Xplore},
	abstract = {<p>Audio/video social media and video sharing sites like YouTube allow users to create their own community where they post audio content. This type of content includes product reviews which consumers use to assist others in making buying decisions, and this content can be processed using NLP and automated Aspect-Based Sentiment Analysis (ABSA) to produce input for recommender systems. However, developing these systems for low resource languages (such as Modern Greek) lags behind significantly. To address this gap, in this paper we introduce a complete ABSA framework, which transforms Greek multimedia reviews into structured and accurate data, to be used for personalized product recommendation. The proposed framework uses a multistage pipeline to address the complexities of spoken text, such as the highly inflected morphology of the Greek language and slang utilized in reviewing. The first stage of the pipeline transforms raw audio data into a form suitable for processing, while the second stage applies linguistic processing. Two alternatives were evaluated for this stage, where the first utilizes a native Greek Sentiment Lexicon and the second firstly applies Greek-to-English automated translation, followed by English Language Sentiment Analysis. The efficacy of the proposed framework has been assessed through an experiment in which each of the pipeline outcomes is compared against independent human assessments. The assessment shows that the translation-based approach achieves high accuracy and alignment with expert human raters. The proposed work also takes into account the effect of {\textquotedblleft}noise{\textquotedblright} owing to Automatic Speech Recognition (ASR) errors and studies the propagation of these errors across 12 distinct product attributes. The proposed pipeline thus constitutes an effective solution for exploiting audiovisual content in recommender systems to generate personalized recommendations.</p>
},
	keywords = {Aspect-Based Sentiment Analysis (ABSA), Automatic Speech Recognition (ASR), Modern Greek NLP, Multimedia Data Mining, Personalized Recommender Systems, User Preference Modeling},
	author = {Singh, Manvir and Roumeliotis, Konstantinos I. and Vassilakis, Costas and Margaris, Dionisis and Mpardis, Giorgos and Spiliotopoulos, Dimitris}
}
@conference {876,
	title = {Unveiling Structural Distributional Bias in Collaborative Filtering for Ultra-Sparse Datasets},
	booktitle = {Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications},
	year = {2026},
	publisher = {IEEE Xplore},
	organization = {IEEE Xplore},
	abstract = {<p>Collaborative Filtering is one of the most widely used rating prediction methods for recommender systems in recent years. It is based on a solid and verified rationale, it supports explainability and usually delivers satisfactory results. However, its performance declines when applied to ultra-sparse datasets. Despite extensive research, the root cause of its failure in such environments still requires further investigation. This work systematically investigates structural distribution biases in CF ultra-sparse datasets, by examining 11 datasets in this category. The findings of this study are threefold. First, this study reveals that the rating distribution within ultra-sparse datasets, exhibits a strong skew toward boundary ratings, thus creating a non-normal rating structure that challenges the traditional Collaborative Filtering algorithms. Secondly, through extensive experiments, employing four different similarity metrics and using three nearest neighbor selection thresholds, it asserts that ratings with values that are underrepresented in the dataset tend to produce significantly higher error margins {\textemdash} up to four times larger than predictions corresponding to the most frequent rating values. The third and most important finding of this study is the phenomenon of mean rating attraction. Specifically, it reveals that the rating predictions are consistently anchored to user and item averages, a process which essentially computationally smooths the rating predictions, instead of recognizing true and unique non-conforming preferences. The above conclusions were found to be independent of both the selection of user similarity metric and the neighbor selection threshold; rather, they are grounded on the inherent structural bias of the input data. This work offers a new perspective for Collaborative Filtering algorithm design and implementation, by identifying structural bias as an important dimension that needs to be handled by rating prediction algorithms, along with the dimension of similarity metrics between users and items.</p>
},
	keywords = {collaborative filtering, Performance evaluation, Recommender Systems, similarity metrics, Structural Distributional Bias, Ultra-Sparse datasets},
	author = {Nanos, John and Margaris, Dionisis and Mpardis, Giorgos and Roumeliotis, Konstantinos I. and Vassilakis, Costas and Spiliotopoulos, Dimitris}
}
