This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations. Initially, a theoretical model for semantic-based text generalization is introduced and used in conjunction with a deep encoder-decoder architecture in order to produce a summary in generalized form. Subsequently, a methodology is proposed which transforms the aforementioned generalized summary into human-readable form, retaining at the same time important informational aspects of the original text and addressing the problem of out-of-vocabulary or rare words. The overall approach is evaluated on two popular datasets with encouraging results.

Recent theoretical and practical advances have led to the emergence of review-based recommender systems, where user preference data is encoded in at least two dimensions; the traditional rating scores in a predefined discrete scale and the user-generated reviews in the form of free-text. The main contribution of this work is the presentation of a new technique of incorporating those reviews into collaborative filtering matrix factorization algorithms. The text of each review, of arbitrary length, is mapped to a continuous feature space of fixed length, using neural language models and more specifically, the Paragraph Vector model. Subsequently, the resulting feature vectors (the neural embeddings) are used in combination with the rating scores in a hybrid probabilistic matrix factorization algorithm, based on maximum a-posteriori estimation. The proposed methodology is then compared to three other similar approaches on six datasets in order to assess its performance. The obtained results demonstrate that the new formulation outperforms the other systems on a set of two metrics, thereby indicating the robustness of the idea.

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 {\c{C}}atalhöyük 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.

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.

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.