Dionisis Margaris

TL;DR: This paper presents a query personalization algorithm, which employs collaborative filtering techniques and takes into account influence factors between social network users, leading to personalized results that are better-targeted to the user.

TL;DR: This paper enhances 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.

TL;DR: A fine-grained recommender system for social ecosystems, designed to recommend media content published by the user’s friends, which developed a proof-of-concept implementation for Facebook and explored the effectiveness of the underlying mechanisms for content analysis.

TL;DR: This paper introduces 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 and presents a framework which incorporates the above characteristics.

TL;DR: The features of textual reviews are examined, which affect the reliability of the review-to-rating conversion procedure, a confidence level is computed for each rating, which reflects the uncertainty level for each conversion process, and a novel rating prediction algorithm is presented.