This page has only limited features, please log in for full access.

Dr. Georgios Alexandridis
University of the Aegean

Basic Info


Research Keywords & Expertise

0 Artificial Intelligence
0 Data Mining
0 Deep Learning
0 Machine Learning
0 Recommender Systems

Fingerprints

Recommender Systems
Deep Learning

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 18 August 2021 in Information
Reads 0
Downloads 0

As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advances in text mining algorithms, deep learning techniques, and text representation models, the results in such tasks are very good for only a few high-density languages (e.g., English) that possess large training corpora and rich linguistic resources; nevertheless, there is still room for improvement for the other lower-density languages as well. In this direction, the current work employs various language models for representing social media texts and text classifiers in the Greek language, for detecting the polarity of opinions expressed on social media. The experimental results on a related dataset collected by the authors of the current work are promising, since various classifiers based on the language models (naive bayesian, random forests, support vector machines, logistic regression, deep feed-forward neural networks) outperform those of word or sentence-based embeddings (word2vec, GloVe), achieving a classification accuracy of more than 80%. Additionally, a new language model for Greek social media has also been trained on the aforementioned dataset, proving that language models based on domain specific corpora can improve the performance of generic language models by a margin of 2%. Finally, the resulting models are made freely available to the research community.

ACS Style

Georgios Alexandridis; Iraklis Varlamis; Konstantinos Korovesis; George Caridakis; Panagiotis Tsantilas. A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media. Information 2021, 12, 331 .

AMA Style

Georgios Alexandridis, Iraklis Varlamis, Konstantinos Korovesis, George Caridakis, Panagiotis Tsantilas. A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media. Information. 2021; 12 (8):331.

Chicago/Turabian Style

Georgios Alexandridis; Iraklis Varlamis; Konstantinos Korovesis; George Caridakis; Panagiotis Tsantilas. 2021. "A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media." Information 12, no. 8: 331.

Journal article
Published: 04 June 2020 in Big Data and Cognitive Computing
Reads 0
Downloads 0

Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, recommendation systems and personalization techniques have created a smart research field, augmenting cultural heritage visitor’s experience. In this work, a novel, hybrid recommender system for cultural places is proposed, that combines user preference with cultural tourist typologies. Starting with the McKercher typology as a user classification research base, which extracts five categories of heritage tourists out of two variables (cultural centrality and depth of user experience) and using a questionnaire, an enriched cultural tourist typology is developed, where three additional variables governing cultural visitor types are also proposed (frequency of visits, visiting knowledge and duration of the visit). The extracted categories per user are fused in a robust collaborative filtering, matrix factorization-based recommendation algorithm as extra user features. The obtained results on reference data collected from eight cities exhibit an improvement in system performance, thereby indicating the robustness of the presented approach.

ACS Style

Markos Konstantakis; Georgios Alexandridis; George Caridakis. A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies. Big Data and Cognitive Computing 2020, 4, 12 .

AMA Style

Markos Konstantakis, Georgios Alexandridis, George Caridakis. A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies. Big Data and Cognitive Computing. 2020; 4 (2):12.

Chicago/Turabian Style

Markos Konstantakis; Georgios Alexandridis; George Caridakis. 2020. "A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies." Big Data and Cognitive Computing 4, no. 2: 12.

Conference paper
Published: 29 May 2020 in Collaboration in a Hyperconnected World
Reads 0
Downloads 0

Sentiment analysis is a vigorous research area, with many application domains. In this work, aspect-based sentiment prediction is examined as a component of a larger architecture that crawls, indexes and stores documents from a wide variety of online sources, including the most popular social networks. The textual part of the collected information is processed by a hybrid bi-directional long short-term memory architecture, coupled with convolutional layers along with an attention mechanism. The extracted textual features are then combined with other characteristics, such as the number of repetitions, the type and frequency of emoji ideograms in a fully-connected, feed-forward artificial neural network that performs the final prediction task. The obtained results, especially for the negative sentiment class, which is of particular importance in certain cases, are encouraging, underlying the robustness of the proposed approach.

ACS Style

Georgios Alexandridis; Konstantinos Michalakis; John Aliprantis; Pavlos Polydoras; Panagiotis Tsantilas; George Caridakis. A Deep Learning Approach to Aspect-Based Sentiment Prediction. Collaboration in a Hyperconnected World 2020, 397 -408.

AMA Style

Georgios Alexandridis, Konstantinos Michalakis, John Aliprantis, Pavlos Polydoras, Panagiotis Tsantilas, George Caridakis. A Deep Learning Approach to Aspect-Based Sentiment Prediction. Collaboration in a Hyperconnected World. 2020; ():397-408.

Chicago/Turabian Style

Georgios Alexandridis; Konstantinos Michalakis; John Aliprantis; Pavlos Polydoras; Panagiotis Tsantilas; George Caridakis. 2020. "A Deep Learning Approach to Aspect-Based Sentiment Prediction." Collaboration in a Hyperconnected World , no. : 397-408.

Journal article
Published: 04 March 2020 in Algorithms
Reads 0
Downloads 0

Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods.

ACS Style

Georgios Alexandridis; Yorghos Voutos; Phivos Mylonas; George Caridakis. A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends. Algorithms 2020, 13, 59 .

AMA Style

Georgios Alexandridis, Yorghos Voutos, Phivos Mylonas, George Caridakis. A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends. Algorithms. 2020; 13 (3):59.

Chicago/Turabian Style

Georgios Alexandridis; Yorghos Voutos; Phivos Mylonas; George Caridakis. 2020. "A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends." Algorithms 13, no. 3: 59.

Conference paper
Published: 15 May 2019 in IFIP Advances in Information and Communication Technology
Reads 0
Downloads 0

This work constitutes a theoretically-informed empirical analysis of the spatial characteristics of the short-term rentals’ market and explores their linkage with shifts in the wider housing market within the context of a south-eastern EU metropolis. The same research objective has been pursued for a variety of international paradigms; however, to the best of our knowledge, there has not been a thorough and systematic study for Athens and its neighborhoods. With a theoretical framework that draws insight from the political-economic views of Critical Geography, this work departs from an assessment of Airbnb listings, and proceeds inquiring the expansion of the phenomenon with respect to the rates of long-term rent levels in the neighborhoods of Central Athens, utilizing relevant data. The geographical framework covers the City of Athens as a whole, an area undergoing profound transformations in recent years, stemming from diverse factors that render the city one of the most dynamic destinations of urban tourism and speculative land investment. The analysis reveals a prominent expansion of the short-term rental phenomenon across the urban fabric, especially taking ground in hitherto underexploited areas. This expansion is multifactorial, asynchronous and exhibits signs of positive relation with the long-term rentals shifts; Airbnb not only affects already gentrifying neighborhoods, but contributes to a housing market disruption in non-dynamic residential areas.

ACS Style

Konstantinos Gourzis; Georgios Alexandridis; Stelios Gialis; George Caridakis. Studying the Spatialities of Short-Term Rentals’ Sprawl in the Urban Fabric: The Case of Airbnb in Athens, Greece. IFIP Advances in Information and Communication Technology 2019, 196 -207.

AMA Style

Konstantinos Gourzis, Georgios Alexandridis, Stelios Gialis, George Caridakis. Studying the Spatialities of Short-Term Rentals’ Sprawl in the Urban Fabric: The Case of Airbnb in Athens, Greece. IFIP Advances in Information and Communication Technology. 2019; ():196-207.

Chicago/Turabian Style

Konstantinos Gourzis; Georgios Alexandridis; Stelios Gialis; George Caridakis. 2019. "Studying the Spatialities of Short-Term Rentals’ Sprawl in the Urban Fabric: The Case of Airbnb in Athens, Greece." IFIP Advances in Information and Communication Technology , no. : 196-207.

Conference paper
Published: 13 May 2019 in Companion Proceedings of The 2019 World Wide Web Conference
Reads 0
Downloads 0
ACS Style

Georgios Alexandridis; Thanos Tagaris; Giorgos Siolas; Andreas Stafylopatis. From Free-text User Reviews to Product Recommendation using Paragraph Vectors and Matrix Factorization. Companion Proceedings of The 2019 World Wide Web Conference 2019, 335 -343.

AMA Style

Georgios Alexandridis, Thanos Tagaris, Giorgos Siolas, Andreas Stafylopatis. From Free-text User Reviews to Product Recommendation using Paragraph Vectors and Matrix Factorization. Companion Proceedings of The 2019 World Wide Web Conference. 2019; ():335-343.

Chicago/Turabian Style

Georgios Alexandridis; Thanos Tagaris; Giorgos Siolas; Andreas Stafylopatis. 2019. "From Free-text User Reviews to Product Recommendation using Paragraph Vectors and Matrix Factorization." Companion Proceedings of The 2019 World Wide Web Conference , no. : 335-343.

Conference paper
Published: 30 March 2019 in Computer Vision
Reads 0
Downloads 0

Genetic Algorithms (GAs) have been predominantly used in video games for finding the best possible sequence of actions that leads to a win condition. This work sets out to investigate an alternative application of GAs on action-adventure type video games. The main intuition is to encode actions depending on the state of the world of the game instead of the sequence of actions, like most of the other GA approaches do. Additionally, a methodology is being introduced which modifies a part of the agent’s logic and reuses it in another game. The proposed algorithm has been implemented in the GVG-AI competition’s framework and more specifically for the Zelda and Portals games. The obtained results, in terms of average score and win percentage, seem quite satisfactory and highlight the advantages of the suggested technique, especially when compared to a rolling horizon GA implementation of the aforementioned framework; firstly, the agent is efficient at various levels (different world topologies) after being trained in only one of them and secondly, the agent may be generalized to play more games of the same category.

ACS Style

Tasos Papagiannis; Georgios Alexandridis; Andreas Stafylopatis. GAMER: A Genetic Algorithm with Motion Encoding Reuse for Action-Adventure Video Games. Computer Vision 2019, 156 -171.

AMA Style

Tasos Papagiannis, Georgios Alexandridis, Andreas Stafylopatis. GAMER: A Genetic Algorithm with Motion Encoding Reuse for Action-Adventure Video Games. Computer Vision. 2019; ():156-171.

Chicago/Turabian Style

Tasos Papagiannis; Georgios Alexandridis; Andreas Stafylopatis. 2019. "GAMER: A Genetic Algorithm with Motion Encoding Reuse for Action-Adventure Video Games." Computer Vision , no. : 156-171.

Article
Published: 13 February 2019 in User Modeling and User-Adapted Interaction
Reads 0
Downloads 0

Although abundant research work has been published in the area of path recommendation and its applications on travel and routing topics, scarce work has been reported on context-aware route recommendation systems aimed to stimulate optimal cultural heritage experiences. This paper tries to address this issue, by proposing a personalized and content adaptive cultural heritage path recommendation system, where location is modeled using mean-shift clustering trained with actual user movement patters. Additionally, topic modeling is incorporated to formalize the implicit cultural heritage content, while first order Markov models address the movement as a temporal transition aspect of the problem. The overall architecture is applied on data collected from actual visits to the archaeological sites of Gournia and Çatalhöyük and extensive analysis on visitor movement patterns follows, especially in comparison to the curated paths in the aforementioned sites. Finally, the offline evaluation results of the proposed recommendation scheme are encouraging, validating its efficiency and setting a positive paradigm for cultural heritage route recommendations.

ACS Style

Georgios Alexandridis; Angeliki Chrysanthi; George E. Tsekouras; George Caridakis. Personalized and content adaptive cultural heritage path recommendation: an application to the Gournia and Çatalhöyük archaeological sites. User Modeling and User-Adapted Interaction 2019, 29, 201 -238.

AMA Style

Georgios Alexandridis, Angeliki Chrysanthi, George E. Tsekouras, George Caridakis. Personalized and content adaptive cultural heritage path recommendation: an application to the Gournia and Çatalhöyük archaeological sites. User Modeling and User-Adapted Interaction. 2019; 29 (1):201-238.

Chicago/Turabian Style

Georgios Alexandridis; Angeliki Chrysanthi; George E. Tsekouras; George Caridakis. 2019. "Personalized and content adaptive cultural heritage path recommendation: an application to the Gournia and Çatalhöyük archaeological sites." User Modeling and User-Adapted Interaction 29, no. 1: 201-238.

Conference paper
Published: 01 November 2018 in 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)
Reads 0
Downloads 0

Integrated spiral inductors are a fundamental part of Radio-Frequency (RF) circuits. In certain scenarios, a solution to the inverse spiral inductor design problem is required; given the desired properties of an inductor, locate the most suitable geometric characteristics. This problem does not have a unique solution and current approaches approximate it through a number of differential equations and the subsequent application of optimization techniques that narrow down the set of feasible solutions. In this work, the Neural Network Specialists model is outlined; a preliminary approach to solving the aforementioned problem using fully connected neural network models. The obtained results on a first round of experiments are encouraging, especially in terms of the reduction in time complexity.

ACS Style

Nikolaos Dervenis; Georgios Alexandridis; Andreas Stafylopatis. Neural Network Specialists for Inverse Spiral Inductor Design. 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) 2018, 60 -64.

AMA Style

Nikolaos Dervenis, Georgios Alexandridis, Andreas Stafylopatis. Neural Network Specialists for Inverse Spiral Inductor Design. 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI). 2018; ():60-64.

Chicago/Turabian Style

Nikolaos Dervenis; Georgios Alexandridis; Andreas Stafylopatis. 2018. "Neural Network Specialists for Inverse Spiral Inductor Design." 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) , no. : 60-64.

Original paper
Published: 22 May 2018 in Evolving Systems
Reads 0
Downloads 0

Package recommendation systems have gained in popularity especially in the tourism domain, where they propose combinations of different types of attractions that can be visited by someone during a city tour. These systems can also be applied in suggesting home entertainment, proper nutrition or academic courses. Such systems must optimize multiple user criteria in tandem, such as preference score, package cost or duration. This work proposes a flexible framework for recommending packages that best fit users’ preferences while satisfying several constraints on the set of the valid packages. This is achieved by modeling the relation between the items and the categories these items belong to, aiming at recommending to each user the top-k packages that cover their preferred categories and the restriction of a maximum package cost. Our contribution includes an optimal and a greedy algorithm, that both outperform a state-of-the-art system and a popularity-based baseline solution. The novelty of the optimal algorithm is that it combines the collaborative filtering predictions with a graph-based model to produce package recommendations. The problem is expressed through a minimum cost flow network and is solved by integer linear programming. The greedy algorithm has a low computational complexity and provides recommendations which are close to the optimal one. An extensive evaluation of the proposed framework has been carried out on six popular recommendation datasets. The results obtained using a set of widely accepted metrics show promising performance. Finally, the formulation of the problem for specific domains has also been addressed.

ACS Style

Panagiotis Kouris; Iraklis Varlamis; Georgios Alexandridis; Andreas Stafylopatis. A versatile package recommendation framework aiming at preference score maximization. Evolving Systems 2018, 11, 423 -441.

AMA Style

Panagiotis Kouris, Iraklis Varlamis, Georgios Alexandridis, Andreas Stafylopatis. A versatile package recommendation framework aiming at preference score maximization. Evolving Systems. 2018; 11 (3):423-441.

Chicago/Turabian Style

Panagiotis Kouris; Iraklis Varlamis; Georgios Alexandridis; Andreas Stafylopatis. 2018. "A versatile package recommendation framework aiming at preference score maximization." Evolving Systems 11, no. 3: 423-441.

Conference paper
Published: 02 August 2017 in Programmieren für Ingenieure und Naturwissenschaftler
Reads 0
Downloads 0

The popularity of recommendation systems has made them a substantial component of many applications and projects. This work proposes a framework for package recommendations that try to meet users’ preferences as much as possible through the satisfaction of several criteria. This is achieved by modeling the relation between the items and the categories these items belong to aiming to recommend to each user the top-k packages which cover their preferred categories and the restriction of a maximum package cost. Our contribution includes an optimal and a greedy solution. The novelty of the optimal solution is that it combines the collaborative filtering predictions with a graph based model to produce recommendations. The problem is expressed through a minimum cost flow network and is solved by integer linear programming. The greedy solution performs with a low computational complexity and provides recommendations which are close to the optimal solution. We have evaluated and compared our framework with a baseline method by using two popular recommendation datasets and we have obtained promising results on a set of widely accepted evaluation metrics.

ACS Style

Panagiotis Kouris; Iraklis Varlamis; Georgios Alexandridis. A Package Recommendation Framework Based on Collaborative Filtering and Preference Score Maximization. Programmieren für Ingenieure und Naturwissenschaftler 2017, 744, 477 -489.

AMA Style

Panagiotis Kouris, Iraklis Varlamis, Georgios Alexandridis. A Package Recommendation Framework Based on Collaborative Filtering and Preference Score Maximization. Programmieren für Ingenieure und Naturwissenschaftler. 2017; 744 ():477-489.

Chicago/Turabian Style

Panagiotis Kouris; Iraklis Varlamis; Georgios Alexandridis. 2017. "A Package Recommendation Framework Based on Collaborative Filtering and Preference Score Maximization." Programmieren für Ingenieure und Naturwissenschaftler 744, no. : 477-489.

Preprint
Published: 23 June 2017
Reads 0
Downloads 0

Review-based recommender systems have gained noticeable ground in recent years. In addition to the rating scores, those systems are enriched with textual evaluations of items by the users. Neural language processing models, on the other hand, have already found application in recommender systems, mainly as a means of encoding user preference data, with the actual textual description of items serving only as side information. In this paper, a novel approach to incorporating the aforementioned models into the recommendation process is presented. Initially, a neural language processing model and more specifically the paragraph vector model is used to encode textual user reviews of variable length into feature vectors of fixed length. Subsequently this information is fused along with the rating scores in a probabilistic matrix factorization algorithm, based on maximum a-posteriori estimation. The resulting system, ParVecMF, is compared to a ratings' matrix factorization approach on a reference dataset. The obtained preliminary results on a set of two metrics are encouraging and may stimulate further research in this area.

ACS Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. ParVecMF: A Paragraph Vector-based Matrix Factorization Recommender System. 2017, 1 .

AMA Style

Georgios Alexandridis, Georgios Siolas, Andreas Stafylopatis. ParVecMF: A Paragraph Vector-based Matrix Factorization Recommender System. . 2017; ():1.

Chicago/Turabian Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. 2017. "ParVecMF: A Paragraph Vector-based Matrix Factorization Recommender System." , no. : 1.

Article
Published: 31 March 2017 in Data Mining and Knowledge Discovery
Reads 0
Downloads 0

Social collaborative filtering recommender systems extend the traditional user-to-item interaction with explicit user-to-user relationships, thereby allowing for a wider exploration of correlations among users and items, that potentially lead to better recommendations. A number of methods have been proposed in the direction of exploring the social network, either locally (i.e. the vicinity of each user) or globally. In this paper, we propose a novel methodology for collaborative filtering social recommendation that tries to combine the merits of both the aforementioned approaches, based on the soft-clustering of the Friend-of-a-Friend (FoaF) network of each user. This task is accomplished by the non-negative factorization of the adjacency matrix of the FoaF graph, while the edge-centric logic of the factorization algorithm is ameliorated by incorporating more general structural properties of the graph, such as the number of edges and stars, through the introduction of the exponential random graph models. The preliminary results obtained reveal the potential of this idea.

ACS Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models. Data Mining and Knowledge Discovery 2017, 31, 1031 -1059.

AMA Style

Georgios Alexandridis, Georgios Siolas, Andreas Stafylopatis. Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models. Data Mining and Knowledge Discovery. 2017; 31 (4):1031-1059.

Chicago/Turabian Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. 2017. "Enhancing social collaborative filtering through the application of non-negative matrix factorization and exponential random graph models." Data Mining and Knowledge Discovery 31, no. 4: 1031-1059.

Conference paper
Published: 01 December 2016 in 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Reads 0
Downloads 0

In this paper a new algorithm for session identification in Web logs is outlined, based on the fuzzy c-means clustering of the available data. The novelty of the proposed methodology lies in the initialization of the partition matrix using subtractive clustering, the examination of the effect a variety of distance metrics have on the clustering process (in addition to the widely-used Euclidean distance), the determination of the number of user sessions based on candidate sessions and the representation of the session data. The experimental results show that the proposed methodology is effective in the reconstruction of user sessions and can distinguish individual sessions more accurately than baseline time-heuristic methods proposed in literature.

ACS Style

Dimitrios Koutsoukos; Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. A new approach to session identification by applying fuzzy c-means clustering on web logs. 2016 IEEE Symposium Series on Computational Intelligence (SSCI) 2016, 1 -8.

AMA Style

Dimitrios Koutsoukos, Georgios Alexandridis, Georgios Siolas, Andreas Stafylopatis. A new approach to session identification by applying fuzzy c-means clustering on web logs. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). 2016; ():1-8.

Chicago/Turabian Style

Dimitrios Koutsoukos; Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. 2016. "A new approach to session identification by applying fuzzy c-means clustering on web logs." 2016 IEEE Symposium Series on Computational Intelligence (SSCI) , no. : 1-8.

Book chapter
Published: 13 February 2015 in Lecture Notes in Social Networks
Reads 0
Downloads 0

In this chapter, we focus on recommender systems that are enhanced with social information in the form of trust statements between their users. The trust information may be processed in a number of ways, including the random walks in the social graph, where every step in the walk is chosen almost uniformly at random from the available choices. Although this strategy yields satisfactory results in terms of the novelty and the diversity of the produced recommendations, it exhibits poor accuracy because it does not fully exploit the similarity information among users and items. Our work tries to model user-to-user and user-to-item relation as a probability distribution using a novel approach based on Rejection Sampling in order to decide its next step (biased random walk). Some initial results on reference datasets indicate that a satisfying trade-off among accuracy, novelty, and diversity is achieved.

ACS Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. Accuracy Versus Novelty and Diversity in Recommender Systems: A Nonuniform Random Walk Approach. Lecture Notes in Social Networks 2015, 41 -57.

AMA Style

Georgios Alexandridis, Georgios Siolas, Andreas Stafylopatis. Accuracy Versus Novelty and Diversity in Recommender Systems: A Nonuniform Random Walk Approach. Lecture Notes in Social Networks. 2015; ():41-57.

Chicago/Turabian Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. 2015. "Accuracy Versus Novelty and Diversity in Recommender Systems: A Nonuniform Random Walk Approach." Lecture Notes in Social Networks , no. : 41-57.

Conference paper
Published: 25 August 2013 in Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Reads 0
Downloads 0
ACS Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. A biased random walk recommender based on rejection sampling. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2013, 648 -652.

AMA Style

Georgios Alexandridis, Georgios Siolas, Andreas Stafylopatis. A biased random walk recommender based on rejection sampling. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 2013; ():648-652.

Chicago/Turabian Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. 2013. "A biased random walk recommender based on rejection sampling." Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining , no. : 648-652.

Journal article
Published: 01 February 2012 in International Journal on Artificial Intelligence Tools
Reads 0
Downloads 0

Most recommender systems have too many items to propose to too many users based on limited information. This problem is formally known as the sparsity of the ratings' matrix, because this is the structure that holds user preferences. This paper outlines a Collaborative Filtering Recommender System that tries to amend this situation. After applying Singular Value Decomposition to reduce the dimensionality of the data, our system makes use of a dynamic Artificial Neural Network architecture with boosted learning to predict user ratings. Furthermore we use the concept of k-separability to deal with the resulting noisy data, a methodology not yet tested in Recommender Systems. The combination of these techniques applied to the MovieLens datasets seems to yield promising results.

ACS Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. APPLYING k-SEPARABILITY TO COLLABORATIVE RECOMMENDER SYSTEMS. International Journal on Artificial Intelligence Tools 2012, 21, 1 .

AMA Style

Georgios Alexandridis, Georgios Siolas, Andreas Stafylopatis. APPLYING k-SEPARABILITY TO COLLABORATIVE RECOMMENDER SYSTEMS. International Journal on Artificial Intelligence Tools. 2012; 21 (1):1.

Chicago/Turabian Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. 2012. "APPLYING k-SEPARABILITY TO COLLABORATIVE RECOMMENDER SYSTEMS." International Journal on Artificial Intelligence Tools 21, no. 1: 1.

Conference paper
Published: 01 January 2010 in Computer Vision
Reads 0
Downloads 0

Most recommender systems usually have too many items to recommend to too many users using limited information. This problem is formally known as the sparsity of the ratings’ matrix, because this is the structure that holds user preferences. This article outlines a collaborative recommender system, that tries to amend this situation. The system is built around the notion of k-separability combined with a constructive neural network algorithm.

ACS Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. An Efficient Collaborative Recommender System Based on k-Separability. Computer Vision 2010, 6354, 198 -207.

AMA Style

Georgios Alexandridis, Georgios Siolas, Andreas Stafylopatis. An Efficient Collaborative Recommender System Based on k-Separability. Computer Vision. 2010; 6354 ():198-207.

Chicago/Turabian Style

Georgios Alexandridis; Georgios Siolas; Andreas Stafylopatis. 2010. "An Efficient Collaborative Recommender System Based on k-Separability." Computer Vision 6354, no. : 198-207.

Conference paper
Published: 01 January 2005 in Computer Vision
Reads 0
Downloads 0

PDAs and other handheld devices are commonly used for processing private or otherwise secret information. Their increased usage along with their networking capabilities raises security considerations for the protection of the sensitive information they contain and their communications. We present CryptoPalm, an extensible cryptographic library for the PalmOS. The library integrates a large set of cryptographic algorithms and is compatible with the IEEE P1363 standard. Furthermore, the library offers performance comparable with that of independent, application-centric implementations of the cryptographic algorithms. CryptoPalm is beneficial for PalmOS software developers, since it provides established cryptographic algorithms as an infrastructure for meeting their applications’ security requirements.

ACS Style

Georgios C. Alexandridis; Artemios G. Voyiatzis; Dimitrios N. Serpanos. CryptoPalm: A Cryptographic Library for PalmOS. Computer Vision 2005, 651 -660.

AMA Style

Georgios C. Alexandridis, Artemios G. Voyiatzis, Dimitrios N. Serpanos. CryptoPalm: A Cryptographic Library for PalmOS. Computer Vision. 2005; ():651-660.

Chicago/Turabian Style

Georgios C. Alexandridis; Artemios G. Voyiatzis; Dimitrios N. Serpanos. 2005. "CryptoPalm: A Cryptographic Library for PalmOS." Computer Vision , no. : 651-660.