Interactive systems that explain data, or support decision making often emphasize what is present while overlooking what is expected but missing. This presence bias limits users’ ability to form complete mental models of a dataset or situation. Detecting absence depends on expectations about what should be there, yet interfaces rarely help users form such expectations. We present an experimental study examining how reference framing and prompting influence people’s ability to recognize expected but missing categories in datasets. Participants compared distributions across three domains (energy, wealth, and regime) under two reference conditions: Global, presenting a unified population baseline, and Partial, showing several concrete exemplars. Results indicate that absence detection was higher with Partial reference than with Global reference, suggesting that partial, samples-based framing can support expectation formation and absence detection. When participants were prompted to look for what was missing, absence detection rose sharply. We discuss implications for interactive user interfaces and expectation-based visualization design, while considering cognitive trade-offs of reference structures and guided attention.

Intelligent interfaces increasingly use large language models to summarize user-generated content, yet these summaries emphasize what is mentioned while overlooking what is missing. This presence bias can mislead users who rely on summaries to make decisions. We present Domain Informed Summarization through Contrast (DiSCo), an expectation-based computational approach that makes absences visible by comparing each entity’s content with domain topical expectations captured in reference distributions of aspects typically discussed in comparable accommodations. This comparison identifies aspects that are either unusually emphasized or missing relative to domain norms and integrates them into the generated text. In a user study across three accommodation domains, namely ski, beach, and city center, DiSCo summaries were rated as more detailed and useful for decision making than baseline large language model summaries, although slightly harder to read. The findings show that modeling expectations reduces presence bias and improves both transparency and decision support in intelligent summarization interfaces.

The integration of Generative AI (GenAI) into creative communities, like fanfiction, is reshaping how stories are created, shared, and valued. This study investigates the perceptions of 157 active fanfiction members, both readers and writers, regarding AI-generated content in fanfiction. Our research explores the impact of GenAI on community dynamics, examining how AI affects the participatory and collaborative nature of these spaces. The findings reveal responses ranging from cautious acceptance of AI’s potential for creative enhancement to concerns about authenticity, ethical issues, and the erosion of human-centered values. Participants emphasized the importance of transparency and expressed worries about losing social connections. Our study highlights the need for thoughtful AI integration in creative platforms using design interventions that enable ethical practices, promote transparency, increase engagement and connection, and preserve the community’s core values.

The rapid integration of artificial intelligence (AI) into communication systems has significantly altered how users interact with digital tools and collaborate with AI agents. This study investigates the dynamics of politeness in human-AI interactions through a controlled experiment with 1,684 participants, each completing sequential text-based tasks with a conversational AI system. Participants were randomly assigned to one of several conditions that varied in the AI's visual identity (no icon, robot icon, or human face), allowing us to examine the role of perceived anthropomorphism through a minimal visual cue. Politeness was measured using linguistic markers and analyzed using statistical models that account for task sequence and individual differences. Our findings show that politeness toward AI declines over time, with a temporary increase at the start of a second task. Compared to human-human interactions in a benchmark dataset, politeness in human-AI interactions eroded more quickly. Younger participants were less polite overall, and although frequent AI users also appeared less polite descriptively, adjusted models showed a small positive association with daily AI use. Anthropomorphic visual cues, especially human-like avatars, led to more sustained polite behavior. These results offer insight into how users adapt social norms in AI-mediated collaboration and suggest design strategies for fostering respectful and effective human-AI communication.

Diversifying movie recommendations is an effective way to address choice overload, a phenomenon where recommenders generate lists with highly similar recommendations that are difficult to choose from. However, existing diversification algorithms often rely on latent features, which limits their interpretability and makes it less clear why a particular set of movies is recommended. Given that movies are designed to elicit emotional responses, researchers have suggested leveraging these responses to enhance recommender system performance. This study introduces a novel “emotion diversification” approach, which diversifies movie recommendations based on emotional signals extracted from audience reviews. We evaluate this method against latent and non-diversified baselines in a controlled user study (N = 115), finding that it significantly improves perceived taste coverage and system satisfaction without compromising recommendation quality. Going beyond the traditional rating- and/or interaction data used by traditional recommender systems, our work demonstrates the user experience benefits of extracting emotional data from rich, qualitative user feedback and using it to give users a more emotionally diverse set of recommendations.

The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process – the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice.

Online customers opinions about products and services, in the form of reviews, are a major part of today's web culture. However, customers, when looking for a product or service, do not have the time or the desire to read even a small part of the available product reviews (which themselves may be lengthy and not easy to read). Moreover, they often would like to examine reviews of similar products, and get a comprehensive picture of how different aspects of these products compare. In this work, by introducing a generic framework for analyzing and presenting a visual summary based on comparative sentences extracted from customer reviews, we offer the user an easy and intuitive understanding of the differences between a set of products. The contribution of this study is twofold: First, it focuses on reviews of intangible services (using the restaurant domain as a case study), unlike most of the related studies that consider physical products. Second, it combines state-of-the-art text analysis techniques with an intuitive visualization into an easy to use prototype to visualize summarized service comparisons to the users.

Predicting whether a session is a buying session (e.g. will end with buying an item) is an ongoing research task. Drawing from recent experience in Web search and movie recommenders, we explore the effect of temporal trends and characteristics on the ability to predict buying sessions. We suggest a new approach, based on items' temporal dynamics, together with sessions' temporal aspects for predicting whether a session is going to end up with a purchase. We suggest a model for estimating the probability of a session to end with a purchase, according to the purchase history of items clicked on during the session over the past few days. The predictions can be used by recommender systems, enabling them to take relevant actions, thus improving shoppers experience as well as increasing sales for e-commerce companies. Our findings shed light on the importance of considering temporal dynamics in items recommendations in e-commerce sites. Empirical results on imbalanced e-commerce dataset with more than nine million sessions demonstrate that we achieve high Precision, Recall and ROC in predicting whether session ends up with a purchase or not.

Traffic patterns in the Internet are changing, with video and user generated content (UGC) taking an increasing share of the volume, and P2P traffic decreases. The widespread appearing of content providers and content peering has been shown to decrease profit for ISPs. To reduce expenses, the use of P2P caches for UGC has been suggested. In this work, we look at the problem of UGC content sharing between peering ISPs. We show a method for testing whether sharing is beneficial for the ISPs. We then give a method for total objects placement such that the optimal demand is maximized, under the following constraints: (1) The local demand is known at each ISP; (2) ISPs share only if they can satisfy at least the same demand as before the sharing. We further simulate our method with different workloads distributions that exhibit either UGC or P2P characteristics.

A plethora of tools exist for extracting and visualizing key sentiment information from a corpus of text documents. Often, however, there is a need for quickly assessing the sentiment and feelings that arise from an individual document. We describe an interactive tool that visualizes the sentiment of a specific document such as an online opinion, blog, or transcript, by visually highlighting the sentiment features while leaving the document text intact.

This work is concerned with the problem of efficient and intelligent message forwarding in wireless networks. This problem arises in many diverse scenarios within ad-hoc networks and especially networks formed during and in the aftermath of a disaster in which infrastructure-based communication systems have been damaged or completely destroyed. Within this setting, mobile devices need to support critical message exchanges in order to offer user reassurance and aid first responders' search-and-rescue operations. Notably, the dissemination of alert messages has to be done in a way that achieves sufficient dissemination while ensuring network longevity. Under the proposed explore and exploit (EnE) framework, this paper derives innovative networking heuristics that capitalizes on locally-calculated metrics (including the Local Connectivity (LC) centrality metric) to make message forwarding/replication decisions. The proposed heuristics exhibit excellent features with regards to the aforementioned performance objectives and are shown to greatly outperform current popular alternative solutions.

In this paper, we present PACK (Predictive ACKs), a novel end-to-end traffic redundancy elimination (TRE) system, designed for cloud computing customers. Cloud-based TRE needs to apply a judicious use of cloud resources so that the bandwidth cost reduction combined with the additional cost of TRE computation and storage would be optimized. PACK's main advantage is its capability of offloading the cloud-server TRE effort to end-clients, thus minimizing the processing costs induced by the TRE algorithm. Unlike previous solutions, PACK does not require the server to continuously maintain clients' status. This makes PACK very suitable for pervasive computation environments that combine client mobility and server migration to maintain cloud elasticity. PACK is based on a novel TRE technique, which allows the client to use newly received chunks to identify previously received chunk chains, which in turn can be used as reliable predictors to future transmitted chunks. We present a fully functional PACK implementation, transparent to all TCP-based applications and network devices. Finally, we analyze PACK benefits for cloud users, using traffic traces from various sources.

Online hotel searching is a daunting task due to the wealth of online information. Reviews written by other travelers replace the word-of-mouth, yet turn the search into a time consuming task. Users do not rate enough hotels to enable a collaborative filtering based recommendation. Thus, a cold start recommender system is needed. In this work we design a cold start hotel recommender system, which uses the text of the reviews as its main data. We define context groups based on reviews extracted from TripAdvisor.com and Venere.com. We introduce a novel weighted algorithm for text mining. Our algorithm imitates a user that favors reviews written with the same trip intent and from people of similar background (nationality) and with similar preferences for hotel aspects, which are our defined context groups. Our approach combines numerous elements, including unsupervised clustering to build a vocabulary for hotel aspects, semantic analysis to understand sentiment towards hotel features, and the profiling of intent and nationality groups.

Online hotel searching is a daunting task due to the wealth of online information. Reviews written by other travelers replace the word-of-mouth, yet turn the search into a time consuming task. Users do not rate enough hotels to enable a collaborative filtering based recommendation. Thus, a cold start recommender system is needed. This demo describes briefly our cold start hotel recommender system, which uses the text of the reviews as its main data. We define context groups based on reviews extracted from TripAdvisor.com and Venere.com. We introduce a novel weighted algorithm for text mining.

In this paper we present PACK (Predictive ACKs), a novel end-to-end Traffic Redundancy Elimination (TRE) system, designed for cloud computing customers.

No abstract available.

This work presents a thorough investigation of the structure of multicast trees cut from the Internet and power-law topologies. Based on both generated topologies and real Internet data, we characterize the structure of such trees and show that they obey the rank-degree power law; that most high degree tree nodes are concentrated in a low diameter neighborhood; and that the sub-tree size also obeys a power law. Our most surprising empirical finding suggests that there is a linear ratio between the number of high-degree network nodes, namely nodes whose tree degree is higher than some constant, and the number of leaf nodes in the multicast tree (clients). We also derive this ratio analytically. Based on this finding, we develop the fast algorithm, that estimates the number of clients, and show that it converges faster than one round trip delay from the root to a randomly selected client.

The competition on clients attention requires sites to update their content frequently. As a result, a large percentage of Web pages are semi-dynamic, i.e., change quite often and stay static between changes. The cost of maintaining consistency for such pages discourages caching solutions. We suggest here an integrated architecture for the scalable delivery of frequently changing hot pages. Our scheme enables sites to dynamically select whether to cyclically multicast a hot page or to unicast it, and to switch between multicast and unicast mechanisms in a transparent way. Our scheme defines a new protocol, called h.t.t.p.m. In addition, it uses currently deployed protocols, and dynamically directs browsers seeking for a URL to multicast channels, while using existing DNS mechanisms. Thus, we enable sites to deliver content to a growing number of users at less cost and during denial of service attacks, while reducing load on core links. We report simulation results that demonstrate the advantages of the integrated architecture, and its significant impact on server and network load, as well as clients delay.