In the rapidly evolving field of news recommendation, where user preferences are highly dynamic and content quickly becomes obsolete, providing timely and relevant recommendations presents a significant challenge. Traditional recommender systems typically rely on complex collaborative filtering models that depend on extensive user histories. In the news domain, however, such histories are often scarce due to the high prevalence of anonymous users. To address these challenges, we introduce a novel session-based recommendation method that leverages cohesive sequential pattern mining. Rather than relying on traditional frequency-based pattern utility metrics, our approach prioritizes pattern cohesiveness, which captures the temporal proximity of item interactions within a pattern, resulting in recommendations that align more closely with the user’s ongoing session.We conduct a comprehensive empirical evaluation of our approach using four large-scale real-world news datasets. The results demonstrate that our method, SeQcsp, significantly outperforms state-of-the-art session-based recommendation algorithms in terms of accuracy, ranking quality, as well as diversity. Furthermore, SeQcsp provides recommendations faster than most existing methods and is effective for both short and long user sessions, highlighting its robustness, adaptability, and efficiency.

Time series semantic segmentation is the task of extracting time intervals from the time series data that share a similar meaning within the application domain in an unsupervised manner. State-of-the-art algorithms typically treat this problem as change point detection, resulting in discrete state transitions. However, in real-world applications, states often transition gradually. This leads to a novel, more challenging variation of the traditional time series segmentation task, for which we present PaTSS, a novel, domain-agnostic algorithm to uncover those gradual state transitions. PaTSS learns a distribution over the semantic segments based on an embedding space derived from mined sequential patterns. An extensive experimental evaluation on 107 benchmark time series shows that PaTSS is capable of detecting gradual state transitions, a task current methods are unable to perform.

The discovery of repeated structures in time series, known as motifs, is an important data mining task. Various techniques exist to mine motifs in either a database of time series or within one or two individual time series, either for a user-defined motif length or a range of lengths. However, mining frequent motifs of variable length in large time series databases remains an unsolved task that is computationally expensive. We propose FRM-Miner, an efficient algorithm for discovering more informative patterns in time series data, i.e., motifs of different length that are non-overlapping, occur frequently and where the euclidean distance between the motif and its various occurrences is minimal. Unlike current state-of-the-art approaches, FRM-Miner can efficiently find variable motif lengths in large time series databases. Through extensive experimentation, we show desirable properties of FRM-Miner, such as robustness to noise and expressive power, thereby discovering motifs that remain undetected using state-of-the-art methods. Additionally, our method is highly scalable, taking only 2.95 hours to discover informative sets of motifs on all 128 time series data sets of the UCR Time Series Archive, where related state-of-the-art algorithms such as Ostinato require several days.

Finding patterns in long event sequences is an important data mining task. Two decades ago research focused on finding all frequent patterns, where the anti-monotonic property of support was used to design efficient algorithms. Recent research focuses on producing a smaller output containing only the most interesting patterns. To achieve this goal, we introduce a new interestingness measure by computing the proportion of the occurrences of a pattern that are cohesive. This measure is robust to outliers, and is applicable to sequential patterns. We implement an efficient algorithm based on constrained prefix-projected pattern growth and pruning based on an upper bound to uncover the set of top-k quantile-based cohesive sequential patterns. We run experiments to compare our method with existing state-of-the-art methods for sequential pattern mining and show that our algorithm is efficient and produces qualitatively interesting patterns on large event sequences.

Multi-label classification problems occur naturally in different domains. For example, within text categorization the goal is to predict a set of topics for a document, and within image scene classification the goal is to assign labels to different objects in an image. In this work we propose a combination of two variations of k nearest neighborhoods (kNN) where the first neighborhood is computed instance (or row) based and the second neighborhood is feature (or column) based. Instance based kNN is inspired by user-based collaborative filtering, while feature kNN is inspired by item-based collaborative filtering. Finally we apply a linear combination of instance and feature neighbors scores and apply a single threshold to predict the set of labels. Experiments on various multi-label datasets show that our algorithm outperforms other state-of-the-art methods such as ML-kNN, IBLR and Binary Relevance with SVM, on different evaluation metrics. Finally our algorithm uses an inverted index during neighborhood search and scales to extreme datasets that have millions of instances, features and labels.