Abstract

Abstract

Abstract
Recommendation systems can play an important role in today's digital content platforms by supporting the suggestion of relevant content in a personalised manner for each customer. Such content customisation has not been consistent across most media domains, and particularly on radio streaming and gaming aggregators, which are the two real-world application domains focused in this work. The challenges faced in these application areas are the dynamic nature of user preferences and the difficulty of generating recommendations for less popular content, due to the overwhelming choice and polarisation of available top content. We present the design and implementation of a Reinforcement Learning-based Recommendation System (RLRS) for web applications, using a Deep Deterministic Policy Gradient (DDPG) agent and, as a reward function, a weighted sum of the user Click Distribution (CD) across the recommended items and the Dwell Time (DT), a measure of the time users spend interacting with those items. Our system has been deployed in real production scenarios with preliminary but promising results. Several metrics are used to track the effectiveness of our approach, such as content coverage, category diversity, and intra-list similarity. In both scenarios tested, the system shows consistent improvement and adaptability over time, reinforcing its applicability.

Abstract
Developers of data-intensive georeplicated applications face a difficult decision when selecting a database system. As captured by the CAP theorem, CP systems such as Spanner provide strong consistency that greatly simplifies application development. AP systems such as AntidoteDB providing Transactional Causal Consistency (TCC), ensure availability in face of network partitions and isolate performance from wide-area round-trip times, but avoid lost-update anomalies only when values can be merged. Ideally, an application should be able to adapt to current data and network conditions by selecting which transactional consistency to use for each transaction. In this paper, we test the hypothesis that a georeplicated database system can be built at its core providing only TCC, hence, being AP, but allow an application to execute some transactions under Snapshot Isolation (SI), hence CP. Our main result is showing that this can be achieved even when all the interaction happens through the TCC database system, without additional communication channels between the participants. A preliminary experimental evaluation with a proof-of-concept implementation using AntidoteDB shows that this approach is feasible.

Abstract

Abstract
Modelling complex information systems often entails the need for dealing with scenarios of inconsistency in which several requirements either reinforce or contradict each other. This lecture summarises recent joint work with Juliana Cunha, Alexandre Madeira and Ana Cruz on a variant of transition systems endowed with positive and negative accessibility relations, and a metric space over the lattice of truth values. Such structures are called paraconsistent transition systems, the qualifier stressing a connection to paraconsistent logic, a logic taking inconsistent information as potentially informative. A coalgebraic perspective on this family of structures is also discussed.