Abstract

Abstract

Abstract

Abstract
Online incremental models for recommendation are nowadays pervasive in both the industry and the academia. However, there is not yet a standard evaluation methodology for the algorithms that maintain such models. Moreover, online evaluation methodologies available in the literature generally fall short on the statistical validation of results, since this validation is not trivially applicable to stream-based algorithms. We propose a k-fold validation framework for the pairwise comparison of recommendation algorithms that learn from user feedback streams, using prequential evaluation. Our proposal enables continuous statistical testing on adaptive-size sliding windows over the outcome of the prequential process, allowing practitioners and researchers to make decisions in real time based on solid statistical evidence. We present a set of experiments to gain insights on the sensitivity and robustness of two statistical tests-McNemar's and Wilcoxon signed rank-in a streaming data environment. Our results show that besides allowing a real-time, fine-grained online assessment, the online versions of the statistical tests are at least as robust as the batch versions, and definitely more robust than a simple prequential single-fold approach.

Abstract
Although widely used, the majority of current music recommender systems still focus on recommendations' accuracy, user preferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations' quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address "similar concepts" rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items' discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs' similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.

Abstract
Over the past few years, the amount of information generated, consumed and stored on the Web has grown exponentially, making it impossible for users to keep up to date. Temporal data representation can help in this process by giving documents a sense of organization. Timelines are a natural way to showcase this data, giving users the chance to get familiar with a topic in a shorter amount of time. Despite their importance, little is known about their use in the context of single documents. In this paper, we present Time-Matters, a novel system to automatically explore arbitrary texts through temporal narratives in an interactive fashion that allows users to get insights into the relevant temporal happenings of a story through multiple components, including temporal annotation, storylines or temporal clustering. In contrast to classical timeline multi-document summarization tasks, we focus on performing text summaries of single documents with a temporal lens. This approach may be of interest to a number of providers such as media outlets, for which automatically building a condensed overview of a text is an important issue. © 2021, Springer Nature Switzerland AG.