@inbook {808,
	title = {Identifying Reliable Recommenders in Users{\textquoteright} Collaborating Filtering and Social Neighbourhoods},
	booktitle = {Lecture Notes in Social Networks},
	year = {2021},
	pages = {51{\textendash}76},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {<p>Recommender systems increasingly use information sourced from social networks to improve the quality of their recommendations. However, both recommender systems and social networks exhibit phenomena under which information for certain users or items is limited, such as the cold start and the grey sheep phenomena in collaborative filtering systems and the isolated users in social networks. In the context of a social network-aware collaborative filtering, where the collaborating filtering- and social network{\textendash}based neighbourhoods are of varying density and utility for recommendation formulation, the ability to identify the most reliable recommenders from each neighbourhood for each user and appropriately combine the information associated with them in the recommendation computation process can significantly improve the quality and accuracy of the recommendations offered. In this chapter, we report on our extensions on earlier works in this area which comprise of (1) the development of an algorithm for discovering the most reliable recommenders of a social network recommender system and (2) the development and evaluation of a new collaborative filtering algorithm that synthesizes the opinions of a user{\textquoteright}s identified recommenders to generate successful recommendations for the particular user. The proposed algorithm introduces significant gains in rating prediction accuracy (4.9\% on average, in terms of prediction MAE reduction and 4.2\% on average, in terms of prediction RMSE reduction) and outperforms other algorithms. The proposed algorithm, by design, utilizes only basic information from the collaborative filtering domain (user{\textendash}item ratings) and the social network domain (user relationships); therefore, it can be easily applied to any social network recommender system dataset.</p>
},
	keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Rating prediction, Recommender Systems, Social Networks},
	doi = {10.1007/978-3-030-67044-3_3},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis}
}
@article {723,
	title = {A Fine-grained Social Network Recommender System},
	journal = {Social Network Analysis and Mining},
	volume = {10},
	year = {2020},
	abstract = {Recommender systems have greatly evolved in recent years and have become an integral part of the Web. From e-commerce sites to mobile apps, our daily routine revolves around a series of {\textquotedblleft}small{\textquotedblright} decisions that are influenced by such recommendations. In a similar manner, online social networks recommend only a subset of the massive amount of content published by a user{\textquoteright}s friends. However, the prevalent approach for the content selection process in such systems is driven by the amount of interaction between the user and the friend who published the content. As a result, content of interest is often lost due to weak social ties. In this paper, we present a fine-grained recommender system for social ecosystems, designed to recommend media content (e.g., music videos, online clips) published by the user{\textquoteright}s friends. The system design was driven by the findings of our qualitative user study that explored the value and requirements of a recommendation component within a social network. The core idea behind the proposed approach was to leverage the abundance of preexisting information in each user{\textquoteright}s account for creating interest profiles, to calculate similarity scores at a fine-grained level for each friend. The intuition behind the proposed method was to find consistent ways to obtain information representations that can identify overlapping interests in very specific sub-categories (e.g., two users{\textquoteright} music preferences may only coincide on hard rock). While the system is intended as a component of the social networking service, we developed a proof-of-concept implementation for Facebook and explored the effectiveness of our underlying mechanisms for content analysis. Our experimental evaluation demonstrates the effectiveness of our approach, as the recommended content of interest was both overlooked by the existing Facebook engine and not contained in the users{\textquoteright} Facebook News Feed. We also conducted a user study for exploring the usability aspects of the prototype and found that it offers functionality that could significantly improve user experience in popular services.},
	keywords = {Content Analysis, Interest Profiles, Recommender Systems, Social Networks, Usability},
	issn = {1869-5469},
	doi = {10.1007/s13278-019-0621-7},
	author = {Markos Aivazoglou and Antonios Roussos and Dionisis Margaris and Costas Vassilakis and Sotiris Ioannidis and Jason Polakis and Dimitris Spiliotopoulos}
}
@conference {Margaris_2019_asonam,
	title = {Social Relations versus Near Neighbours: Reliable Recommenders in Limited Information Social Network Collaborative Filtering for Online Advertising},
	booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)},
	year = {2019},
	month = {aug},
	abstract = {Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users{\textquoteright} aspects are taken into consid-eration. However, social network information may be unavaila-ble since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited col-laborative filtering information, comprised only of users{\textquoteright} ratings on items, with limited social network information, com-prised only of users{\textquoteright} social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.},
	keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Online Advertising, Pearson Correlation Coefficient, Social Networks},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis}
}
@article {margaris2018a,
	title = {Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems},
	journal = {Informatics},
	volume = {5},
	number = {2},
	year = {2018},
	month = {apr},
	pages = {21 pages},
	abstract = {One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by, social network users may change preferences and likings: they may like different types of clothes, listen to different singers or even different genres of music and so on. This phenomenon has been termed as concept drift. In this paper: (1) we establish that when a social network user abstains from rating submission for a long time, it is a strong indication that concept drift has occurred and (2) we present a technique that exploits the abstention interval concept, to drop from the database ratings that do not reflect the current social network user{\textquoteright}s interests, thus improving prediction quality.},
	keywords = {collaborative filtering, concept drift, Evaluation, Recommender Systems, shift of interest, Social Networks},
	doi = {10.3390/informatics5020021},
	url = {http://www.mdpi.com/2227-9709/5/2/21},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@article {margaris-tldks,
	title = {Enhancing User Rating Database Consistency Through Pruning},
	journal = {Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIV},
	volume = {LNCS, volume 10620},
	number = {Special issue on Consistency and Inconsistency in Data-centric Applications},
	year = {2017},
	pages = {33{\textendash}64},
	publisher = {Springer Berlin Heidelberg},
	abstract = {Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by the interests and likings of people may change: people listen to different singers or even different types of music, watch different types of movies, read different types of books and so on. Due to this type of changes, an amount of inconsistency is introduced in the database since a portion of it does not reflect the current preferences of the user, which is its intended purpose. In this paper, we present a pruning technique that removes old aged user behavior data from the ratings database, which are bound to correspond to invalidated preferences of the user. Through pruning (1) inconsistencies are removed and data quality is upgraded, (2) better rating prediction generation times are achieved and (3) the ratings database size is reduced. We also propose an algorithm for determining the amount of pruning that should be performed, allowing the tuning and operation of the pruning algorithm in an unsupervised fashion. The proposed technique is evaluated and compared against seven aging algorithms, which reduce the importance of aged ratings, and a state-of-the-art pruning algorithm, using datasets with varying characteristics. It is also validated using two distinct rating prediction computation strategies, namely collaborative filtering and matrix factorization. The proposed technique needs no extra information concerning the items{\textquoteright} characteristics (e.g. categories that they belong to or attributes{\textquoteright} values), can be used in all rating databases that include a timestamp and has been proved to be effective in any size of users-items database and under two rating prediction computation strategies. Note: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author{\textquoteright}s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.},
	keywords = {Collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services},
	doi = {10.1007/978-3-662-55947-5_3},
	url = {https://link.springer.com/article/10.1007\%2Fs11761-017-0216-y},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@article {margaris-soca-17,
	title = {Exploiting Internet of Things information to enhance venues{\textquoteright} recommendation accuracy},
	journal = {Service Oriented Computing and Applications},
	volume = {todb},
	year = {2017},
	month = {aug},
	pages = {393{\textendash}409},
	publisher = {Springer Nature},
	abstract = {In this paper, we introduce a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations. The proposed algorithm provides added value for the web services offered by the Internet of Things and enhances the state-of-the-art in this algorithm category by taking into account (a) web of things data regarding the contexts of the user and the context of the venues to be recommended (restaurants, movie theatres, etc.), such as the user{\textquoteright}s geographical position, road traffic and weather conditions, (b) qualitative aspects of the venues, such as price, atmosphere or service, (c) the semantic similarity of venues and (d) the influencing factors between social network users, derived from user participation in social networks. The combination of these features leads to more accurate and better user-targeted recommendations. We also present a framework which incorporates the above characteristics, and we evaluate the presented algorithm, both in terms of performance and recommendation quality.},
	keywords = {collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services},
	doi = {10.1007/s11761-017-0216-y},
	url = {https://link.springer.com/article/10.1007/s11761-017-0216-y},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@inbook {marg16d,
	title = {Knowledge-Based Leisure Time Recommendations in Social Networks},
	booktitle = {Current Trends on Knowledge-Based Systems},
	series = {Intelligent Systems Reference Library},
	year = {2017},
	pages = {23{\textendash}48},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	chapter = {Knowledge-Based Leisure Time Recommendations in Social Networks},
	abstract = {We introduce a novel knowledge-based recommendation algorithm for leisure time information to be used in social networks, which enhances the state-of-the-art in this algorithm category by taking into account (a) qualitative aspects of the recommended places (restaurants, museums, tourist attractions etc.), such as price, service and atmosphere, (b) influencing factors between social network users, (c) the semantic and geographical distance between locations and (d) the semantic categorization of the places to be recommended. The combination of these features leads to more accurate and better user-targeted leisure time recommendations.},
	keywords = {Attribute constraints, collaborative filtering, Knowledge-based Recommender Systems, Semantic Information, Social Networks},
	doi = {10.1007/978-3-319-51905-0_2},
	author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis},
	editor = {Giner Alor Hernandez and Rafael Valencia Garcia}
}
@article {Margaris_2016,
	title = {Recommendation information diffusion in social networks considering user influence and semantics},
	journal = {Social Network Analysis and Mining},
	volume = {6},
	number = {1},
	year = {2016},
	month = {nov},
	pages = {108},
	publisher = {Springer Nature},
	abstract = {One of the major problems in the domain of social networks is the handling and diffusion of the vast, dynamic and disparate information created by its users. In this context, the information contributed by users can be exploited to generate recommendations for other users. Relevant recommender systems take into account static data from users{\textquoteright} profiles, such as location, age or gender, complemented with dynamic aspects stemming from the user behavior and/or social network state such as user preferences, items{\textquoteright} general acceptance and influence from social friends. In this paper, we enhance recommendation algorithms used in social networks by taking into account qualitative aspects of the recommended items, such as price and reliability, the influencing factors between social network users, the social network user behavior regarding their purchases in different item categories and the semantic categorization of the products to be recommended. The inclusion of these aspects leads to more accurate recommendations and diffusion of better user-targeted information. This allows for better exploitation of the limited recommendation space, and therefore online advertisement efficiency is raised.},
	keywords = {Collaborative filtering, Information diffusion, Quality of service, Semantic Information, Social Networks},
	doi = {10.1007/s13278-016-0416-z},
	url = {http://rdcu.be/mZ7e},
	author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis}
}
