In recent decades, Recommender Systems (RS) have undergone significant advancements, particularly in popular domains like movies, music, and product recommendations. Yet, progress has been notably slower in leveraging these systems for personal information management and knowledge assistance. In addition to challenges that complicate the adoption of RS in this domain (such as privacy concerns, heterogeneous recommendation items, and frequent context switching), a significant barrier to progress in this area has been the absence of a standardized benchmark for researchers to evaluate their approaches. In response to this gap, this paper presents a benchmark built upon a publicly available dataset of Real-Life Knowledge Work in Context (RLKWiC). This benchmark focuses on evaluating context-based entity recommendation, a use case for leveraging RS to support knowledge workers in their daily digital tasks. By providing this benchmark, it is aimed to facilitate and accelerate research efforts in enhancing personal knowledge assistance through RS.

A growing number of applications users daily interact with have to operate in (near) real-time: chatbots, digital companions, knowledge work support systems - just to name a few. To perform the services desired by the user, these systems have to analyze user activity logs or explicit user input extremely fast. In particular, text content (e.g. in form of text snippets) needs to be processed in an information extraction task. Regarding the aforementioned temporal requirements, this has to be accomplished in just a few milliseconds, which limits the number of methods that can be applied. Practically, only very fast methods remain, which on the other hand deliver worse results than slower but more sophisticated Natural Language Processing (NLP) pipelines. In this paper, we investigate and propose methods for real-time capable Named Entity Recognition (NER). As a first improvement step, we address word variations induced by inflection, for example present in the German language. Our approach is ontology-based and makes use of several language information sources like Wiktionary. We evaluated it using the German Wikipedia (about 9.4B characters), for which the whole NER process took considerably less than an hour. Since precision and recall are higher than with comparably fast methods, we conclude that the quality gap between high speed methods and sophisticated NLP pipelines can be narrowed a bit more without losing real-time capable runtime performance.

With the growing amount of content stored in personal and organizational information spaces, finding and re-finding documents becomes both more crucial and challenging. In this work, we propose an approach to reduce information overload in navigation by automatically focusing on important documents, adaptively to the tasks at hand. Based on the idea of managed forgetting, we present a ranking method, which unifies activity logs and semantic information about documents into a common framework to identify important documents to the user's current tasks. Our experiments on two real-world datasets, both collected from knowledge work activities in professional scenarios, show that our ranking approach outperforms the baseline methods for both subsequent access prediction and the effectiveness in ranking important documents. Furthermore, we implemented and demonstrated a system for decluttering information spaces as a proof of concept of our managed forgetting approach.

The World Wide Web is arguably the most important source of digital documents nowadays. These documents mainly consist of unstructured and semi-structured data comprising a wealth of information at the disposal of the DAR (Document Analysis and Recognition) community. Contextualization plays an important role in understanding the content of those documents. In this paper, we present an approach to early contextualization of textual data in HTML documents. It combines automatic as well as semiautomatic annotation of named entities with user interaction to support contextualization of the content of digital documents as early as in the authoring stage of their life cycle. We also present the results of an online experimental evaluation involving 120 human test subjects. They show that our approach successfully managed to produce semantically annotated versions of unstructured textual content, which contain reliable contextual information, thus facilitating the task of later document analysis stages.