The rise of social media has fundamentally transformed how people engage in public discourse and form opinions. While these platforms offer unprecedented opportunities for democratic engagement, they have been implicated in increasing social polarization and the formation of ideological echo chambers. Previous research has primarily relied on observational studies of social media data or theoretical modeling approaches, leaving a significant gap in our understanding of how individuals respond to and are influenced by polarized online environments. Here we present a novel experimental framework for investigating polarization dynamics that allows human users to interact with LLM-based artificial agents in a controlled social network simulation. Through a user study with 122 participants, we demonstrate that this approach can successfully reproduce key characteristics of polarized online discourse while enabling precise manipulation of environmental factors. Our results provide empirical validation of theoretical predictions about online polarization, showing that polarized environments significantly increase perceived emotionality and group identity salience while reducing expressed uncertainty. These findings extend previous observational and theoretical work by providing causal evidence for how specific features of online environments influence user perceptions and behaviors. More broadly, this research introduces a powerful new methodology for studying social media dynamics, offering researchers unprecedented control over experimental conditions while maintaining ecological validity.

We propose the concept of meta-intents which represent high-level user preferences related to the interaction and decision-making in conversational recommender systems (CRS) and present a questionnaire instrument for measuring meta-intents. We conducted a two-stage user study, an exploratory study with 212 participants on Prolific, and a confirmatory study with 394 participants on Prolific. We obtained a reliable and stable meta-intents questionnaire with 22 question items, corresponding to seven latent factors (concepts). These seven factors cover important interaction preferences and are closely related to users’ decision-making process. For example, the factor dialog-initiative reflects whether users prefer to follow the system’s guidance or ask their own questions in a CRS. We conducted statistical analyses of meta-intents in two domains (smartphones and hotels), and a general chatbot scenario. We also investigated the influence of additional factors (demography, decision-making style) on meta-intents through Structural Equation Modeling (SEM). Our results provide preliminary evidence that the proposed meta-intents are domain and demography (gender, age) independent. They can be linked to the general decision-making style and can thus be instrumental in translating general decision-making factors into more concrete design guidance for CRS and their potential personalization. Meta-intents also provide a basis for future analyses of interaction behavior in CRS and the development of a cognitively founded theoretical framework.

Multi-list interfaces are widely used in recommender systems, especially in industry, showing collections of recommendations, one below the other, with items that have certain commonalities. The composition and order of these “carousels” are usually optimized by simulating user interaction based on probabilistic models learned from item click data. Research that actually involves users is rare, with only few studies investigating general user experience in comparison to conventional recommendation lists. Hence, it is largely unknown how specific design aspects such as carousel type and length influence the individual perception and usage of carousel-based interfaces. This paper seeks to fill this gap through an exploratory user study. The results confirm previous assumptions about user behavior and provide first insights into the differences in decision making in the presence of multiple recommendation carousels.

Conversational recommender systems (CRS) are increasingly designed to offer mixed-initiative dialogs in which the user and the system can take turns in starting a communicative exchange, for example, by asking questions or stating preferences. However, whether and when users make use of the mixed-initiative capabilities in a CRS and which factors influence their behavior is as yet not well understood. We report an online study investigating user interaction behavior, especially the transfer of initiative between user and system in a real-time online CRS. We assessed the impact of dialog initiative at system start as well as of several psychological user characteristics that may influence their preference for either initiative mode. To collect interaction data, we implemented a chatbot in the domain of smartphones. Two groups of participants on Prolific (total n=143) used the system which started either with a system-initiated or user-initiated dialog. In addition to interaction data, we measured several psychological factors as well as users’ subjective assessment of the system through questionnaires. We found that: 1. Most users tended to take over the initiative from the system or stay in user-initiated mode when this mode was offered initially. 2. Starting the dialog in user-initiated mode CRS led to fewer interactions needed for selecting a product than in system-initiated mode. 3. The user’s initiative transfer was mainly affected by their personal interaction preferences (especially initiative preference). 4. The initial mode of the mixed-initiative CRS did not affect the user experience, but the occurrence of initiative transfers in the dialog negatively affected the degree of user interest and excitement. The results can inform the design and potential personalization of CRS.

Echo chambers are social phenomena that amplify agreement and suppress opposing views in social media which may lead to fragmentation and polarization of the user population. In prior research, echo chambers have mainly been modeled as a result of social information diffusion. While most scientific work has framed echo chambers as a result of epistemic imbalances between polarized communities, we argue that members of echo chambers often actively discredit outside sources to maintain coherent world views. We therefore argue that two different types of echo chambers occur in social media contexts: Epistemic echo chambers create information gaps mainly through their structure whereas ideological echo chambers systematically exclude counter-attitudinal information. Diversifying recommendations by simply widening the scope of topics and viewpoints covered to counteract the echo chamber effect may be ineffective in such contexts. To investigate the characteristics of this dual echo chamber view and to assess the depolarizing effects of diversified recommendations, we apply an agent-based modeling approach. We rely on knowledge graph embedding techniques not only to generate recommendations, but also to show how to utilize logical graph queries in embedding spaces to diversify recommendations aimed at challenging polarization in online discussions. The results of our evaluation indicate that counteracting the two different types of echo chambers requires fundamentally different diversification strategies.

We present an approach combining the AR-based presentation of product attributes in a physical retail store with recommendations for items only available online. The system supports users’ decision-making process by offering functions for comparing product features between items, both physical and online, and by providing recommendations based on selecting in-store products. The physical products may thus serve as anchors for forming the user’s preferences, also offering a richer and more engaging experience when exploring the products hands-on. Both objective product attributes as well as the visual appearance of a physical product are employed for generating recommendations from the online space. In this way, the advantages of online and in-store shopping can be combined, creating novel multi-channel opportunities for businesses. An empirical evaluation showed that the comparison and recommendation functions were appreciated by users, and hinted some possible benefits of a hybrid physical-online shopping support system. Despite the limitations of the study, there is sufficient evidence to consider this a viable approach worth to be further explored.

Numerous attempts have been made for increasing the interactivity in recommender systems, but the features actually available in today's systems are in most cases limited to rating or re-rating single items. We present a demonstrator that showcases how model-based collaborative filtering recommenders may be enhanced with advanced interaction and preference elicitation mechanisms in a holistic manner. Hereby, we underline that by employing methods we have proposed in the past it becomes possible to easily extend any matrix factorization recommender into a fully interactive, user-controlled system. By presenting and deploying our demonstrator, we aim at gathering further insights, both into how the different mechanisms may be intertwined even more closely, and how interaction behavior and resulting user experience are influenced when users can choose from these mechanisms at their own discretion.

Recommender systems (RS) often use implicit user preferences extracted from behavioral and contextual data, in addition to traditional rating-based preference elicitation, to increase the quality and accuracy of personalized recommendations. However, these approaches may harm user experience by causing mixed emotions, such as fear, anxiety, surprise, discomfort, or creepiness. RS should consider users' feelings, expectations, and reactions that result from being shown personalized recommendations. This paper investigates the creepiness of recommendations using an online experiment in three domains: movies, hotels, and health. We define the feeling of creepiness caused by recommendations and find out that it is already known to users of RS. We further find out that the perception of creepiness varies across domains and depends on recommendation features, like causal ambiguity and accuracy. By uncovering possible consequences of creepy recommendations, we also learn that creepiness can have a negative influence on brand and platform attitudes, purchase or consumption intention, user experience, and users' expectations of---and their trust in---RS.

In user studies of recommender systems, participants typically cannot consume the recommended items. Still, they are asked to assess recommendation quality and other aspects related to user experience by means of questionnaires. Without having listened to recommended songs or watched suggested movies, however, this might be an error-prone task, possibly limiting validity of results obtained in these studies. In this paper, we investigate the effect of actually consuming the recommended items. We present two user studies conducted in different domains showing that in some cases, differences in the assessment of recommendations and in questionnaire results occur. Apparently, it is not always possible to adequately measure user experience without allowing users to consume items. On the other hand, depending on domain and provided information, participants sometimes seem to approximate the actual value of recommendations reasonably well.

Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network.

Collaborative work is characterized by frequently changing situations and corresponding demands for tool support and interaction behavior provided by the collaboration environment. Current approaches to address these changing demands include manual tailoring by end-users and automatic adaptation of single user tools or for individual users. Few systems use context as a basis for adapting collaborative work environments, mostly focusing on document recommendation and awareness provision. In this paper, we present, firstly, a generic four layer framework for modeling and exploiting context. Secondly, a generic adaptation process translating user activity into state, deriving context for a given focus, and executing adaptation rules on this context. Thirdly, a collaboration domain model for describing collaboration environments and collaborative situations. Fourthly, examples of exploiting our approach to support context-based adaptation in four typical collaboration situations: co-location, co-access, co-recommendation, and co-dependency.