Cultural Heritage (CH) is a challenging domain of application for novel Information and Communication Technologies (ICT), where visualization plays a major role in enhancing the visitors’ experience, whether onsite or online. Technology-supported natural human-computer interaction is a key factor in enabling access to CH assets. Following the successful workshops at AVI 2016, AVI 2018, AVI 2020, AVI 2022, and AVI 2024, the goal of the AVI-CH 2026 workshop is to bring together researchers and practitioners interested in presenting and discussing the potential of state-of-the-art advanced visual interfaces in enhancing our daily cultural heritage experience. This year, the workshop theme is Hybrid Reasoning Methods behind Interactive Cultural Heritage Applications. Seven papers were accepted, covering topics ranging from augmented and virtual reality for heritage sites, knowledge graph exploration, conversational AI architectures, digital preservation of multimedia art, computer vision for historical audio recordings, cross-device visitor behaviour in virtual museum tours, and LLM-driven adaptive content in VR museums.

Recent research suggests that users of a recommender system may like to receive useful unexpected suggestions that provide a pleasant surprise. This concept, called serendipity, is one of the aspects that have been proposed to meet user expectations for the recommendations they receive. Introducing serendipity means going beyond the “more of the same” aspect that past recommender systems are criticized for. A new approach has recently been proposed to create user models from their previous ratings. In this paper, we show how this user modelling approach can be used to investigate the relationship between users’ preferences, their previous ratings and their judgments related to serendipity. Experiments in the movie domain show that the more relevant an item is to a user, the more willing the user is to discover attributes that are unfamiliar to him, as long as these attributes do not play an important role in his ratings.

The digital era has made millions of manuscript images in Hebrew available to all. However, despite major advancements in handwritten text recognition over the past decade, an efficient pipeline for large scale and accurate conversion of these manuscripts into useful machine-readable form is still sorely lacking.We propose a pipeline that significantly improves recognition models for automatic transcription of Hebrew manuscripts. Transfer learning is used to fine-tune pretrained models. For post-recognition correction, it leverages text reuse, a common phenomenon in medieval manuscripts, and state-of-the-art large language models for medieval Hebrew.The framework successfully handles noisy transcriptions and consistently suggests alternate, better readings. Initial results show that word level accuracy increased by 10% for new readings proposed by text-reuse detection. Moreover, the character level accuracy improved by 18% by fine-tuning models on the first few pages of each manuscript.

This research explores how Interactive Multimodal Tangible Interface (IMTIs) exploiting advanced technologies such as 3D printing and microcontrollers to enhance museum experiences for blind and low vision (BLV) visitors. It investigates the potential for these technologies to create more inclusive and engaging museum environments. Four IMTIs were developed in collaboration with two blind volunteers, with each IMTI using a different interaction technique (autoplay, pushbuttons, and scanning sensors) developed in the pilot phase and having a different shape to align with the museum installation being presented. After refining the concepts, three of the four IMTIs were redesigned and developed into high-quality IMTIs and evaluated by BLV visitors (n=30). The results showed a clear preference among BLV visitors for using pushbuttons to operate the IMTI. Additionally, the research identified key areas for improvement, including 3D printing techniques for producing replicas, audio guide content, and design decisions that can enhance users’ sense of control over the IMTI. These findings offer valuable insights for the future development of tactile replicas that promote contextual understanding, while contributing to a more inclusive and engaging museum experience.

Cultural Heritage (CH) is a challenging domain of application for novel Information and Communication Technologies (ICT), where visualization plays a major role in enhancing the visitors’ experience, whether onsite or online. Technology-supported natural human-computer interaction is a key factor in enabling access, both on-site and online, to CH assets. Recent advances in ICT enhance visitors’ access to online collections as well as their CH experience onsite, bringing even wider audiences than those who visit the physical museums. The range of visualization devices – from tiny smartwatch screens, through large situated public displays, to the latest generation of immersive Head-Mounted Displays – together with the increasing availability of real-time 3D rendering technologies for online and mobile devices and, recently, Internet of Things (IoT) approaches, Social Robotics, requires exploring how they can be applied successfully in CH. Following the successful workshops at AVI 2016, AVI 2018, AVI 2020, AVI 2022 and AVI 2024 and a large number of recent events and projects focusing on CH, the goal of the workshop is to bring together researchers and practitioners interested in presenting and discussing the potential of state of the art advanced visual interfaces in enhancing our daily cultural heritage experience. This year the workshop theme will be: AVI for accessibility in CH.

The Workshop on Recommenders in Tourism (RecTour) has been successfully held in conjunction with the ACM Conference on Recommender Systems (RecSys) since 2016, with the exception of one year. This workshop focuses on the unique and evolving challenges of recommender systems in the tourism domain. Over time, RecTour has fostered an active community supported by both academia and industry. This year, the workshop features a special challenge focused on ranking travel reviews. In this overview paper, we outline our motivations for organizing the RecTour workshop and highlight the main topics covered in RecTour submissions, including destination recommendation, privacy concerns in travel recommender systems, the cold-start problem, transformer-based approaches in recommendation systems, and best practices for evaluation and experimentation.

This workshop is dedicated to emphasizing the pivotal role of temporal dynamics in advancing recommender systems across various fields. While the significance of temporal factors in user behavior is widely acknowledged, effectively integrating these aspects into recommendation algorithms remains a complex challenge. In this workshop, we aim to showcase the application of temporal aspects in recommender systems across diverse domains such as healthcare, e-commerce, fashion, banking, travel, and film. We believe that this workshop will contribute to advancing temporal methodologies in recommender systems, ultimately leading to more precise recommendations.

Users’ feedback is becoming more and more important in many different contexts of users’ interaction with computerised systems, such as in recommender systems, social network, e-democracy, quantifi...

Abstract Bias in news search engines has been shown to influence users' perceptions of a news topic and contribute to the polarisation of society. As a result, there is a need for news search engines that increase user awareness of biases in the search results. While technical approaches have been developed to mitigate biases in search, very few studies have investigated user preferences in interface designs for potentially raising their awareness of biases in news search engines. In this study, we utilized a participatory design methodology to develop eight prototypes with different features that could potentially be used to raise user awareness of biases in news search engines. We conducted three user studies, involving 132 participants with Computer Science backgrounds, to evaluate these prototypes. Our findings indicate the importance of news search engines that (a) inform users of possible biases in the results (bias visualization approach) and (b) allow users to access alternative search results (results‐reranking approach). Our study provides further insights into the strengths and possible risks of each approach, which are important for future research on designing interfaces for raising user awareness of biases in news search engines.

The Workshop on Recommenders in Tourism (RecTour) 2023, which is held in conjunction with the 17th issue of the ACM Conference on Recommender Systems (RecSys) in Singapore, addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.

Whether we like it or not, algorithmic decision-making systems (ADMSs) are all around us. These systems assist both public institutions and private organizations in making decisions that exert a significant impact on our lives. The widespread use of artificial intelligence (AI) and machine learning (ML) systems and the potential risks of using them are the subjects of intensive, ongoing research. It is imperative to ensure their fairness and transparency. The understanding that ADMSs should be subject to human supervision and examined for laypeople’s perceived fairness is clear. Laypeople’s perceptions regarding ADMSs’ fairness, their understanding of the reasons underlying the systems’ outcome (decision), and their comprehension of the linkage between the explanations and the results, influence their willingness to trust the systems, use them and accept their decisions. To determine and better understand which factors affect laypeople’s perceptions of the fairness of algorithmic decisions, we conducted an online between-subject experiment, employing a case study of a simulated AI-based recruitment decision-support system. We focused on three aspects: system characteristics (SC), personality characteristics (PC), and demographic characteristics (DC). We conducted an in-depth analysis to determine which explanation increases the perceived fairness the most. Based on the results, we suggest a framework for predicting a layperson’s perception of the fairness of the explanations. Our findings may help in understanding how to involve humans in the development and evaluation process of ADMSs, how to create personalized explanations based on the SC as well as on users’ PC and DC, and, consequently, how to enhance laypeople’s fairness perceptions regarding ADMSs.

AVI-CH is the 14th workshop in the series of PATCH workshops, since 2007 and the 4th in a row at AVI. It is the meeting place for researchers and practitioners focusing on the application of advanced information and communication technology (ICT) in cultural heritage with a specific focus on user interfaces, visualization and interaction. This year, eight papers were submitted by researchers from Greece, Italy and Israel. All were accepted.

Choosing between two or more alternatives, ranging from relatively simple alternatives to more complex ones, is a common and important task. Visualization techniques may help users to examine differences between such alternatives. The potential contribution of an adequate visualization may increase as the comparison becomes more complicated, when the compared alternatives include multiple dimensions, and when they are examined over time. We propose a novel visualization technique designed to represent the temporal evolution of multiple dimensions between two competitors at a glance. We exemplify how the visualization supports the comparison of two events according to their changes in eight discrete emotions over time. We report on a study that compared our suggested visualization with two common visualization techniques. User performance and preferences were measured under a formal task taxonomy, using Twitter data of real-world events. The experimental results indicate the effectiveness of our visualization as demonstrated by task completion time and accuracy.

The Workshop on Recommenders in Tourism (RecTour) 2022, which is held in conjunction with the 16th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.

With the rapid evolution of technology, computers and their users’ workspaces have become an essential part of our life in general. Today, many people use computers both for work and for personal needs, spending long hours sitting at a desk in front of a computer screen, changing their pose slightly from time to time. This phenomenon impacts people’s health negatively, adversely affecting their musculoskeletal and ocular systems. To mitigate these risks, several different ergonomic solutions have been suggested. This study proposes, demonstrates, and evaluates a technological solution that automatically adjusts the computer screen position and orientation to its user’s current pose, using a simple RGB camera and robotic arm. The automatic adjustment will reduce the physical load on users and better fit their changing poses. The user’s pose is extracted from images continuously acquired by the system’s camera. The most suitable screen position is calculated according to the user’s pose and ergonomic guidelines. Thereafter, the robotic arm adjusts the screen accordingly. The evaluation was done through a user study with 35 users who rated both the idea and the prototype system itself highly.

Body gestures could be used as an intuitive interaction method with computerized systems. Previous studies explored gesture-based interaction mostly with digital displays, thus there is no standard set of gestures for a system that lacks a display. In this work we conducted a pilot study to explore the potential of using gestures to control an eye tracker based mobile museum visitors’ guide. Our objective was to identify a user-defined set of gestures for controlling the mobile guide. In this work we present the preliminary results of the experiment and discuss the participants’ suggestions and concerns for using this type of interaction.

The Workshop on Recommenders in Tourism (RecTour) 2021, which is held in conjunction with the 15th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.

Over the years, the Web has become a premier source of information in almost every area we can think about. When considering tourism, the Web became the primary source of information for travelers. When planning trips, people search for information about destinations, accommodations, attractions, means of transportation, in short, everything related to their future trip. Once done searching they reserve almost everything online. The blessing of the easily accessible information comes with the curse of information overload. This brings Web search techniques and recommendation systems come into play. This is especially true recently with the appearance of COVID-19 and the uncertainty and transformative power it brings to travelling. WebTour 2021 brings together researchers and practitioners working on developing and improving tools and techniques for improving users ability to better find relevant information that matches their needs.

When we consider our CV, it is full of entities - where we studied, where we worked, who we collaborated with on a project or on a paper. Entities we are linked to are part of our profile and as such they help to understand who we are and what are we interested in. Hence, we adapt the typed entity-relation graph (profile) concept and based on this presentation we propose a personalized multifaceted graph visualization for the entity profile. In the context of an academic conference, we allow scholars to explore a graph of related entities and a word cloud representing the links, providing the user a comprehensive, compact and structured overview about the explored scholar. We evaluated our proposed personalized multifaceted visualization in a user study with encouraging results which showed that this visualization is engaging, easy to use and helpful.

AVI2CH is a meeting place for researchers and practitioners focusing on the application of advanced information and communication technology in cultural heritage (CH) with a specific focus on user interfaces, visualization and interaction. It builds on a series of PATCH workshops, since 2007 including three at AVI and also a series of European workshops on cultural informatics. Eleven papers range from novel interfaces in museums to wider community engagement; all share a common mission to ensure that the latest digital technology helps preserve the past in ways that enrich the lives of current and future generations

There is an increasing evidence that data visualization is an important and useful tool for quick understanding and filtering of large amounts of data. In this paper, we contribute to this body of work with a study that compares chord and ranked list for presentation of a temporal TV program genre similarity in next-program recommendations. We consider genre similarity based on the similarity of temporal viewing patterns. We discover that chord presentation allows users to see the whole picture and improves their ability to choose items beyond the ranked list of top similar items. We believe that similarity visualization may be useful for the provision of both the recommendations and their explanations to the end users.

There is increasing evidence that user characteristics can have a significant impact on visualization effectiveness, suggesting that visualizations could be enriched with personalization mechanisms that better fit each user's specific needs and abilities. In this paper, we contribute to this body of work with a study that investigates the impact of six user characteristics on the effectiveness of time series visualizations, which was not previously investigated in relation to personalizing Information visualization. We report on a controlled user study that compare four possible time series visualization techniques. User performance and how it was affected by user characteristics was measured while performing tasks from a formal taxonomy using Twitter data about real-world events. Our results show that both the personality trait of locus of control and the cognitive ability of verbal working memory influence which visualization is more effective when dealing with demanding and complex tasks. These findings extend the need for personalization to visualizations for time series data, and we discuss them in the context of creating systems that can utilize knowledge of the user's specific characteristics in order to present the most suitable visualization for each user.

The Workshop on Recommenders in Tourism (RecTour) 2019, which is held in conjunction with the 13th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topics of the submissions that we received. The topics of this year's workshop include context-aware recommendations, group recommender systems, hotel recommendations, destination characterization, next-POI recommendation, user interaction and experience, preference elicitation, user modeling and application of machine learning algorithms in the context of tourism recommender systems.

Cultural Heritage (CH) is a challenging domain of application for novel Information and Communication Technologies (ICT), where visualization plays a major role in enhancing visitors' experience, either onsite or online. Technology-supported natural human-computer interaction is a key factor in enabling access to CH assets. Advances in ICT ease visitors to access collections online and better experience CH onsite. The range of visualization devices - from tiny smart watch screens and wall-size large situated public displays to the latest generation of immersive head-mounted displays - together with the increasing availability of real-time 3D rendering technologies for online and mobile devices and, recently, Internet of Things (IoT) approaches, require exploring how they can be applied successfully in CH. Following the successful workshop at AVI 2016 and the large numbers of recent events and projects focusing on CH and, considering that 2018 has been declared the European Year of Cultural Heritage, the goal of the workshop is to bring together researchers and practitioners interested in presenting and discussing the potential use of state-of-the-art advanced visual interfaces in enhancing our daily CH experience.

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.

The Workshop on Recommenders in Tourism (RecTour) 2018, which is held in conjunction with the 12th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems within the tourism domain. In this paper, we summarize our motivations to organize this workshop and give an overview of the submissions that we received. The topics of this year's workshop include points-of-interest (POI), hotel and airline recommendations, recommending composite items such as POI sequences, group recommender systems, context-aware recommendation, decision making, user interaction issues, explanations and evaluation of tourism recommenders.

Graph-based recommendation approaches can model associations between users and items alongside additional contextual information. Recent studies demonstrated that representing features extracted from social media (SM) auxiliary data, like friendships, jointly with traditional users/items ratings in the graph, contribute to recommendation accuracy. In this work, we take a step further and propose an extended graph representation that includes socio-demographic and personal traits extracted from the content posted by the user on SM. Empirical results demonstrate that processing unstructured textual information collected from Twitter and representing it in structured form in the graph improves recommendation performance, especially in cold start conditions.

The workshop focus is on considering temporal aspects for recommender systems in general, regardless of the specific domain and application, trying to develop a holistic approach for dealing with temporal aspects in recommender systems, like personal assistants, news, tourism, health care, TV, e-commerce, social networks and so on.

The Workshop on Recommenders in Tourism (RecTour) 2017, which is held in conjunction with the eleventh Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems within the tourism domain. In this overview, we summarize our motivations to organize such a workshop and give an overview of the submissions that we received. The main topics discussed in this year's workshop include context-aware recommender systems, group recommender systems, and the impact of itinerary, sequence, and trust on travel-related recommendations.

We describe a graph-based entity profiling system (GBEP) that extracts information about persons of interest from the Web and uses this information to construct a joint social graph. GBEP then employs graph-based measures to assess inter-personal relatedness, performing social recommendation. Importantly, GBEP provides detailed explanations for its suggestions in the form of relational connecting paths. Initial positive results were obtained for recommending related conference participants to each other using a joint social graph constructed for this purpose.

Many websites allow users to rate items and share their ratings with others, for social or personalisation purposes. In recommender systems in particular, personalised suggestions are generated by predicting ratings for items that users are unaware of, based on the ratings users provided for other items. Explicit user ratings are collected by means of graphical widgets referred to as ‘rating scales’. Each system or website normally uses a specific rating scale, in many cases differing from scales used by other systems in their granularity, visual metaphor, numbering or availability of a neutral position. While many works in the field of survey design reported on the effects of rating scales on user ratings, these, however, are normally regarded as neutral tools when it comes to recommender systems. In this paper, we challenge this view and provide new empirical information about the impact of rating scales on user ratings, presenting the results of three new studies carried out in different domains. Based on these results, we demonstrate that a static mathematical mapping is not the best method to compare ratings coming from scales with different features, and suggest when it is possible to use linear functions instead.

Visitors to museums and other cultural heritage sites encounter a wealth of exhibits in a variety of subject areas, but can explore only a small number of them. Moreover, there typically exists rich complementary information that can be delivered to the visitor about exhibits of interest, but only a fraction of this information can be consumed during the limited time of the visit. Recommender systems may help visitors to cope with this information overload. Ideally, the recommender system of choice should model user preferences, as well as background knowledge about the museum's environment, considering aspects of physical and thematic relevancy. We propose a personalized graph‐based recommender framework, representing rating history and background multi‐facet information jointly as a relational graph. A random walk measure is applied to rank available complementary multimedia presentations by their relevancy to a visitor's profile, integrating the various dimensions. We report the results of experiments conducted using authentic data collected at the Hecht museum. An evaluation of multiple graph variants, compared with several popular and state‐of‐the‐art recommendation methods, indicates on advantages of the graph‐based approach.

Cultural heritage traditionally draws a lot of research attention when it comes to exploring the potential benefits from application of novel technology in realistic settings. The domain is rich of physical as well as virtual sites objects and infinite information about them. Hence, it is only natural that whenever new technology appears, it is experimented in cultural heritage -- from early dialog systems to state of the art Humanoid robots, eye trackers, virtual/augmented reality devices, and the Internet of Things (IoT). The AVI-CH workshop nicely demonstrate this with the diversity of topics presented by the papers accepted to the workshop.

A smart context-aware mobile guide may provide the visitor with personalized relevant information from the vast amount of content available at the museum, adapted for his or her personal needs. Earlier studies relied on using sensors for location-awareness and interest detection. This work explores the potential of mobile eye-tracking and vision technology in enhancing the museum visit experience. We report here on satisfactory preliminary results from examining the performance of a mobile eye tracker in a realistic setting.

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.

Augmented reality (AR) technology has the potential to enrich our daily lives in many aspects. One of them is the museum visit experience. Nowadays, state of the art mobile museum visitor guides provide us with rich personalized, context aware information. However, these systems have one major drawback -- they force the visitor to hold the guide and to look at its screen. The utilization of smart-glasses technology allows providing a wearable AR display without the necessity to hold the guide and to look at it and without distracting the user from the real object. This paper describes initial steps towards the implementation of a head-worn display (HWD) museum visitor guide -- the results of users' requirements elicitation process, the implementation of a first research prototype and initial insights gathered during the process.

In this paper, we summarize RecTour 2016 -- a workshop on recommenders in tourism co-located with RecSys 2016. There was a great variety of submissions, i.e., research papers, demo papers and position papers, addressing fundamental challenges of recommender systems in the tourism domain. The main topics included group recommendations, context-aware recommenders, choice-based recommenders and event recommendations.

Since 2007, the PATCH workshop series (https://patchworkshopseries.wordpress.com/) have been gathering successfully researchers and professionals from various countries and institutions to discuss the topics of digital access to cultural heritage and specifically the personalization aspects in this process. Due to this rich history, the reach of the PATCH workshop in various research communities is extensive.

Ubiquitous computing environments continuously infer our context and proactively offer us context aware services and information, suggested by notifications on our mobile devices. However, notifications come with a cost. They may interrupt the user in the current task and be annoying in the wrong context. The challenge becomes how to notify the user about the availability of relevant services while minimizing the level of disruptiveness. Thus, an understanding of what affects the subjective perception of the disruptiveness of the notification is needed. As yet, most of the research on disruptiveness of notifications focused on stationary, task-oriented environments. In this study, we examine the effect of notifications in a special leisure scenario—a museum visit. In two user studies conducted in a museum setting, participants used a context-aware mobile museum guide to receive information on various museum exhibits while periodically receiving notifications. We examined how the user’s activity, the modality of the notification, and the message content affected the perceived level of disruption that the notifications created. We discuss our results in light of existing work in the desktop and mobile domains and provide a framework and recommendations for designing notifications for a mobile museum guide system.

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.

Many types of recommender systems rely on a rich ensemble of user, item, and context features when generating recommendations for users. The features can be either manually engineered or automatically extracted from the available data, such that feature engineering becomes an important step in the recommendation process. In this work, we propose to leverage graph based representation of the data in order to generate and automatically populate features. We represent the standard user-item rating matrix and some domain metadata, as graph vertices and edges. Then, we apply a suite of graph theory and network analysis metrics to the graph based data representation, to populate features that augment the original user-item ratings data. The augmented data is fed into a classifier that predicts unknown user ratings, which are used for the generation of recommendations. We evaluate the proposed methodology using the recently released Yelp business ratings dataset. Our results indicate that the automatically populated graph features allow for more accurate and robust predictions, with respect to both the variability and sparsity of ratings.

Abstract For museum curators it is imperative to learn, analyze, and understand the behavior patterns of the visitors in their museum. Recent developments in the field of indoor positioning systems make the acquisition and availability of visitor behavior data more attainable. However, the analysis of such data remains a challenge due to its noisiness, complexity and sheer size. The current paper applies information visualization techniques to analyze this data and make it more accessible to museum curators and personnel. We first provide a detailed description of the application domain including an analysis of the curators' information needs and a description of how a dataset on visitors' spatiotemporal behavior could be acquired. In order to address the curators' needs, we designed a visualization to encode and convey the information based on a newly adjusted visual glyph that we callTangram Diagrams. We thereby focus on the adaptability of the technique to a particular domain, rather than on the novelty aspects of the technique itself. We have evaluated our design decisions empirically, and conducted an expert study to describe the insights gained and the value of the information obtained from the visualization. The contribution of this work is twofold. First, we apply information visualization to the museum domain and discuss how it extends to general indoor spatiotemporal behavior analysis. Second, we show how a visual glyph metaphor can be applied in different ways and contexts to efficiently encode multi‐faceted information.

In many cases, visitors come to a museum in small groups. In such cases, the visitors’ social context has an impact on their museum visit experience. Knowing the social context may allow a system to provide socially aware services to the visitors. Evidence of the social context can be gained from observing/monitoring the visitors’ social behavior. However, automatic identification of a social context requires, on the one hand, identifying typical social behavior patterns and, on the other, using relevant sensors that measure various signals and reason about them to detect the visitors’ social behavior. We present such typical social behavior patterns of visitor pairs, identified by observations, and then the instrumentation, detection process, reasoning, and analysis of measured signals that enable us to detect the visitors’ social behavior. Simple sensors’ data, such as proximity to other visitors, proximity to museum points of interest, and visitor orientation are used to detect social synchronization, attention to the social companion, and interest in museum exhibits. The presented approach may allow future research to offer adaptive services to museum visitors based on their social context to support their group visit experience better.

In recent years, we have witnessed the incredible popularity and widespread adoption of mobile devices. Millions of Apps are being developed and downloaded by users at an amazing rate. These are multi-feature Apps that address a broad range of needs and functions. Nowadays, every user has dozens of Apps on his mobile device. As time goes on, it becomes more and more difficult simply to find the desired App among those that are installed on the mobile device. In spite of several attempts to address the problem, no good solution for this increasing problem has yet been found. In this paper we suggest the use of unsupervised machine learning for clustering Apps based on their functionality, to allow users to access them easily. The functionality is elicited from their description as retrieved from various App stores and enriched by content from professional blogs. The Apps are clustered and grouped according to their functionality and presented hierarchically to the user in order to facilitate the search on the small screen of the mobile device.

The tremendous popularity of Online Social Networks (OSN) has led to situations, where users have their profiles spread across multiple networks. These partial profiles reflect different user characteristics, depending mainly on the nature of the network, e.g., Facebook's social vs. LinkedIn's professional focus. Combining data gathered by multiple networks may benefit individual users, and the community as a whole, as this could facilitate the provision of more accurate services and recommendations. This paper reports on an exploratory study of the process of making such recommendations using a unique multi-network dataset containing user interests across multiple domains, e.g., music, books, and movies. We represent the data using a graph model and generate recommendations using a set of features extracted from and populated by the model. We assess the contribution of various network- and domain-related features to the accuracy of the recommendations and motivate future work into automated feature selection.

Many museums offer their visitors the use of a mobile guide to enhance their visit experience. Novel mobile guides have the potential to provide personalized, context-aware, rich content to museum visitors. However, they might also affect the way visitors behave and interact. While many studies have examined novel features that these guides can provide to enhance the visit experience, few have looked into the impact that a mobile guide might have on the actual behavior of the visitors. We describe a field study conducted with 403 actual museum visitors, over a period of 10 months comparing behaviors of visitors who used a mobile multimedia location-aware guide during their visit and that of visitors who did not use any electronic aid. Results indicate that visitors' behavior was altered considerably when using a mobile guide. Visitors using a mobile guide visited the museum longer and were attracted to and spent more time at exhibits where they could get information from the guide. In addition, we provide empirical evidence of the decoupling effect that a mobile guide has on pairs of visitors. Using a mobile guide caused visitors to reduce proximity and to interact less with their fellow group members. Finally, we discuss what may be done to reduce this negative social effect.

An important feature for many applications is the ability to support personalised and context-aware information delivery. User positioning and the use of maps are essential for this purpose. The RoughMaps platform accommodates the use of symbolic maps, which are often not-to-scale, non-linear, highly abstract in nature, and which often contain only the most salient and most relevant features of a map based on the immediate needs of a given user. This work describes the design, implementation, and validation of the RoughMaps research platform, for managing and retrieving contextually relevant symbolic maps for indoor positioning. This is to our knowledge one of the first platforms that focuses on the use of symbolic maps to support indoor positioning in personalised and context-aware mobile applications.

This workshop focuses on the specific challenges for personalization in the cultural heritage setting from the point of view of user interaction and visitor experience. It investigates how the user interface - the contact point of visitors and systems - can become more intelligent by means of personalization. Overall, the workshop aims at attracting presentations of novel ideas for addressing these challenges and the current state of the art in this field.

Museums are both appealing and challenging as an environment for indoor positioning research. By nature, they are dense and rich in objects and information, and as a result they contain more information than a visitor can absorb in a time-limited visit. Many research projects have explored the potential of novel technologies to support information delivery to museum visitors. Having an accurate visitor position is a key factor in the success of such projects. In spite of numerous technologies experimented with, there is no prevailing indoor positioning technology. Each technology has its benefits as well as its limitations. In addition, museums have their own constrains when it comes to installation of sensors in their space. In the framework of the PIL project, a flexible "light weight" proximity based positioning system was developed and deployed at the Hecht museum and a general framework for indoor positioning is proposed. The inherent limitations of the basic technology are addressed by an abstract reasoning layer and by a dialog with the user.

In this paper we describe the in-progress work of the Pathlight navigation system for groups and individuals. Pathlight provides indoor navigation support in the museum using a handheld projector. We describe some of the advantages the system provides, look at some background, briefly describe some system features, and posit some open questions for further investigation.

No abstract available.

Cultural heritage is an area that has recently drawn research attention, especially for exploring ways to harness novel mobile technologies for supporting visitors. The main benefit of these novel technologies is their ability to provide personalized, context-aware information services to their users. However, the use of context-awareness is connected to a fundamental issue of proactiveness – should the system keep the user in control all the time and only respond to user requests, or should the system take initiative and propose its services when needed? Proactiveness of mobile visitors' guides brings with it a possibility for better service to the user at the cost of taking control out of the user's hand. The amount of choice given to visitors is another key issue. With the vast amount of information available for each exhibit, adaptation of the amount of information by limiting the number of content items, could be warranted to filter the information according to the visitors needs. However, it is not clear how reducing choice in terms of the number of content items that are presented to the visitor affects visitor behavior and satisfaction. We examined these issues in a controlled user study conducted with actual museum visitors; comparing usage, behavior patterns, and attitudes of visitors using three versions of a location-aware mobile museum guide.

Social Signal Processing of small groups enables detection of their social context. Monitoring of the social context may be based on position proximity (as a pre-condition for conversation), and on voice communication (an evidence for interaction). Understanding of the social context of a group may allow a system to intervene at the right moment and to suggest relevant services/information. This, in turn, may enhance the group members' experience during leisure activity. This study focuses on assessing the possibility of automatic detection of intra group interaction in a museum environment. It presents analysis and tools that intend to set the foundation for computer aided group interaction during leisure activities.

No abstract available.

In today's information world, small personal computerized devices, such as PDAs, smart phones and other smart appliances, become widely available and essential tools in many situations. This ongoing penetration of computers into everyday life leads to so-called ubiquitous environments, where computational power and networking capabilities are available (and used) everywhere. The strive of providing personal services to users made user modeling capability an essential part of any ubiquitous application. Ubiquitous user modeling describes ongoing modeling and exploitation of user behavior with a variety of systems that share their user models. Currently, issues relating to ubiquitous user modeling are gaining more and more attention from research groups representing the user modeling, the human-computer interaction, and the ubiquitous computing research communities. The goal of this workshop is to bring together academic and industrial researchers from these communities to discuss the most innovative approaches to ubiquitous user modeling, to enhance the exchange of ideas and concepts, to determine the veins the research should proceed, and to go one step further towards personalization in ubiquitous computing.

Visitors often tend to visit museums in groups, mainly with family or friends, yet most of the today mobile museum guides focus on supporting the individual visitor. The technology described in this paper allows supporting groups of visitors in addition to individuals by providing context-aware services aimed at supporting the whole group. These include context-aware communication and alerting services that are provided by the museum visitor's guide system developed in the framework of the PIL (PEACH-Israel) project, as an example case of a larger variety of possible context-aware services.

Collaborative Filtering (CF) is a powerful technique for generating personalized predictions. CF systems are typically based on a central storage of user profiles used for generating the recommendations. However, such centralized storage introduces a severe privacy breach, since the profiles may be accessed for purposes, possibly malicious, not related to the recommendation process. Recent researches proposed to protect the privacy of CF by distributing the profiles between multiple repositories and exchange only a subset of the profile data, which is useful for the recommendation. This work investigates how a decentralized distributed storage of user profiles combined with data modification techniques may mitigate some privacy issues. Results of experimental evaluation show that parts of the user profiles can be modified without hampering the accuracy of CF predictions. The experiments also indicate which parts of the user profiles are most useful for generating accurate CF predictions, while their exposure still keeps the essential privacy of the users.

User data scarcity has always been indicated among the major problems of collaborative filtering recommender systems. That is, if two users do not share sufficiently large set of items for whom their ratings are known, then the user-to-user similarity computation is not reliable and a rating prediction for one user can not be based on the ratings of the other. This paper shows that this problem can be solved, and that the accuracy of collaborative recommendations can be improved by: a) partitioning the collaborative user data into specialized and distributed repositories, and b) aggregating information coming from these repositories. This paper explores a content-dependent partitioning of collaborative movie ratings, where the ratings are partitioned according to the genre of the movie and presents an evaluation of four aggregation approaches. The evaluation demonstrates that the aggregation improves the accuracy of a centralized system containing the same ratings and proves the feasibility and advantages of a distributed collaborative filtering scenario.

Museum visitors can continue interacting with museum exhibits even after they have left the museum. We can help them do this by creating a report that includes a basic, personalized narration of their visit, the items and relationships they found most interesting, pointers to additional related online information, and suggestions for future visits to the current and other museums. In this work we describe the automatic generation of personalized natural language reports to help create one episode in an ongoing coherent sequence of cultural activities.

Abstract Today, most document categorization in organizations is done manually. We save at work hundreds of files and e‐mail messages in folders every day. While automatic document categorization has been widely studied, much challenging research still remains to support user‐subjective categorization. This study evaluates and compares the application of self‐organizing maps (SOMs) and learning vector quantization (LVQ) with automatic document classification, using a set of documents from an organization, in a specific domain, manually classified by a domain expert. After running the SOM and LVQ we requested the user to reclassify documents that were misclassified by the system. Results show that despite the subjective nature of human categorization, automatic document categorization methods correlate well with subjective, personal categorization, and the LVQ method outperforms the SOM. The reclassification process revealed an interesting pattern: About 40% of the documents were classified according to their original categorization, about 35% according to the system's categorization (the users changed the original categorization), and the remainder received a different (new) categorization. Based on these results we conclude that automatic support for subjective categorization is feasible; however, an exact match is probably impossible due to the users' changing categorization behavior.

Abstract Web navigation enables easy access to vast amounts of information and services. However, it also poses a major risk to users' privacy. Various eavesdroppers constantly attempt to violate users' privacy by tracking their navigation activities and inferring their interests and needs (profiles). Users who wish to keep their intentions secret forego useful services to avoid exposure. The computer security community has concentrated on improving users' privacy by concealing their identity on the Web. However, users may want or need to identify themselves over the Net to receive certain services but still retain their interests, needs, and intentions in private. PRAW—a PRivAcy model for the Web suggested in this paper—is aimed at hiding users' navigation tracks to prevent eavesdroppers from inferring their profiles but still allowing them to be identified. PRAW is based on continuous generation of fake transactions in various fields of interests to confuse eavesdroppers' automated programs, thus providing them false data. A privacy measure is defined that reflects the difference between users' actual profile and the profile that eavesdroppers might infer. A prototype system was developed to examine PRAW's feasibility and conduct experiments to test its effectiveness. Encouraging results and their analysis are presented, as well as possible attacks and known limitations.

The present study compares an adaptive simulated cellular-phone based recommender system to a non-adaptive one, in order to evaluate user preferences with respect to system adaptivity. The results show that users prefer the adaptive system over the non adaptive one even after minimal interaction with the system.

We present a novel technique to combine services, provided by mobile as well as fixed devices, on-the-fly and in a context-sensitive way within smart environments containing groups of people. The key intuition at the base of our approach is that, in those environments, similar functions are provided simultaneously by many agents on board of different devices; the issue becomes then of combining and coordinating their services in order to exploit their aggregated power or improve delivery. At the core of our work is the idea of implicit organizations, that is, teams of agents that continuously renegotiate how to provide services while the environment evolves. A concept demonstrator shows how implicit organizations can support a smart virtual meeting room. We foresee the application of our approach in assisting users and groups of users in active environments, such as an active museum. Here, our approach can enhance knowledge transfer and communication for groups visiting together cultural exhibits.

We propose a model that enables software developers to systematically evaluate and compare all possible alternative reuse scenarios. The model supports the clear identification of the basic operations involved and associates a cost component with each basic operation in a focused and precise way. The model is a practical tool that assists developers to weigh and evaluate different reuse scenarios, based on accumulated organizational data, and then to decide which option to select in a given situation. The model is currently being used at six different companies for cost-benefit analysis of alternative reuse scenarios; we give a case study that illustrates how it has been used in practice.

Searching the web for information becomes a tedious task. As a result of any query, the user gets large numbers of responses, most of them irrelevant. Existing search tools fail to cope with this information overload, mainly due to lack of personalization. Current developments emphasize better representation of user interests and dynamic adaptation based on relevance feedback, but personalization is much more then just that. The present study examines the impact of search tools adaptation to their users. A significant preference toward user-oriented search method over the conventional content-based method was found. The results clearly demonstrate the need for self-adapting personalized interfaces as mediators between users and information repositories of all kinds.

In information filtering (IF) systems, user long-term needs we expressed as user profiles. The quality of a user profile has a major impact on the performance of IF systems. The focus of the proposed research is on the study of user profile generation and update. The paper introduces methods for user profile generation, and proposes a research agenda for their comparison and evaluation.