A personalized and context-aware news offer for mobile devices

For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Mind over machine? The clash of agency in social media environments

Includes bibliographical references.2022 Fall.Underlying many social media platforms are choice recommendation "nudging" architectures designed to give users instant content and social recommendations to keep them engaged. Powered by complex algorithms, these architectures flush people's feeds and an array of other features with fresh content and create a highly individualized experience tailored to their interests. In a critical realist qualitative study, this research examines how individual agency manifests when users encounter these tools and the suggestions they provide. In interviews and focus groups, 45 participants offered their experiences where they reflected on how they perceived the engines, e.g., their Facebook feed, influenced their actions and behaviors, as well as how the participants felt they controlled it to achieve personal aims. Based on these and other experiences, this study posits the Social Cognitive Machine Agency Dynamic (SCMAD) model, which provides an empirically supported explanatory framework to explain how individual agency can manifest and progress in response to these tools. The model integrates Albert Bandura's social cognitive theory concepts and emergent findings. It demonstrates how users react to the engines through agentic expressions not dissimilar to the real-world, including enacting self-regulatory, self-reflective and intentionality processes, as well as other acts not captured by Bandura's theory. Ultimately, the research and model propose a psycho-environmental explanation of the swerves of agency experienced by users in reaction to the unique conditions and affordances of these algorithmically driven environments. The study is the first known extension of social cognitive theory to this technology context. Implications of the findings are discussed and recommendations for future research provided. The study recommends that future research and media discourse aim for an individual-level psychological evaluation of these powerful technologies. This stance will afford a greater understanding of the technology's impacts and implications on individuals, particularly as it is anticipated to significantly evolve in the coming years

User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration

Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks. Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion. Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data

Rethinking Lurking: Invisible Leading and Following in a Knowledge Transfer Ecosystem

The term lurker connotes a low-value role in online communities. Despite making up the majority of members, these invisible individuals are often cast as peripheral players who should be encouraged to participate more fully. We argue that the lurker concept is problematic and that online communities, and the roles associated with them, need to be reconceptualized. We report on a study of online communities in a New Zealand professional development program. We found that two knowledge broker types played key roles in transferring knowledge: connector-leaders, who had a strong online presence, and follower-feeders, who communicated largely invisibly, via side-channels. Despite their different online profiles, both brokers used “lurking” purposively to perform two sets of invisible online activities: managing the knowledge agenda, and mentoring/being mentored. These activities supported their roles as leaders and followers, and sustained a symbiotic relationship. Decisions to “lurk” arose from the need for these brokers to negotiate diverse boundaries: the boundaries of micro-culture associated with communication contexts, the theory-practice boundary, role boundaries, and the online-offline boundary. Combining the concept of polycontextuality with boundary spanning theory, we propose an alternative way of understanding both lurking and online communities: the three-tier knowledge transfer ecosystem (KTE), a system of engagement spaces comprising diverse online and offline contexts in which individuals make continual decisions to cross between less- or more-visible settings. The study illustrates how key phenomena may remain invisible without a shift in level of analysis, and how using an anachronistic concept to frame a study can unintentionally constrain its value