The 12th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'25) takes a user-centric perspective on recommender systems research. It brings together an interdisciplinary community of researchers and practitioners who explore a range of important issues on the "user side"of recommender systems, including user studies, psychology-informed design of RSs, novel interfaces, and evaluation methodologies. Its purpose is to identify critical challenges and emerging topics with a strong focus on fundamental historical challenges in the field. In this summary, we introduce the motivation and perspective of the workshop, review its history, and discuss the most critical issues that deserve attention for future research directions.

The primary goal of Recommender Systems is to suggest the most suitable items to a user, aligning them with the user’s interests and needs. RSs are essential for modern e-commerce, helping users discover content and products by predicting suitable items based on their past behavior. However, their success isn’t just about advanced algorithms. The design of the user interface and a good integration with the human decision-making process are equally crucial. A well-designed interface enhances the user experience and makes recommendations more effective, while a poor interface can lead to frustration. Recognizing this limitation, recent trends in Recommender Systems (RSs) are increasingly focusing on integrating Symbiotic Human-Machine Decision-Making models. These models aim to offer users a dynamic and persuasive interface that helps them better understand and engage with recommendations. This shift is a crucial step toward developing recommender systems that truly connect with users and offer a more enjoyable, trustworthy, explainable, and user-friendly experience. Although early efforts concentrated on creating systems that could proactively predict user preferences and needs, modern RSs also emphasize the importance of providing users with control and transparency over their recommendations. Finding the right balance between proactivity and user control is essential to ensure that the system supports users without being too intrusive, thus improving their overall satisfaction. As Large Language Models (LLMs) become more integrated into recommender systems, the importance of user-centric interfaces and a deep understanding of decision-making becomes even more critical. Effective integration of LLMs requires interfaces that are both visually and cognitively engaging.

Offline data-driven evaluation is considered a low-cost and more accessible alternative to the online empirical method of assessing the quality of recommender systems. Despite their popularity and effectiveness, most data-driven approaches are unsuitable for evaluating interactive recommender systems. In this article, we attempt to address this issue by simulating the user interactions with the system as a part of the evaluation process. Particularly, we demonstrate that simulated users find their desired item more efficiently when recommendations are presented as a list of carousels compared to a simple ranked list.

Automated analysis of programming data using code representation methods offers valuable services for programmers, from code completion to clone detection to bug detection. Recent studies show the effectiveness of Abstract Syntax Trees (AST), pre-trained Transformer-based models, and graph-based embeddings in programming code representation. However, pre-trained large language models lack interpretability, while other embedding-based approaches struggle with extracting important information from large ASTs. This study proposes a novel Subtree-based Attention Neural Network (SANN) to address these gaps by integrating different components: an optimized sequential subtree extraction process using Genetic algorithm optimization, a two-way embedding approach, and an attention network. We investigate the effectiveness of SANN by applying it to two different tasks: program correctness prediction and algorithm detection on two educational datasets containing both small and large-scale code snippets written in Java and C, respectively. The experimental results show SANN's competitive performance against baseline models from the literature, including code2vec, ASTNN, TBCNN, CodeBERT, GPT-2, and MVG, regarding accurate predictive power. Finally, a case study is presented to show the interpretability of our model prediction and its application for an important human-centered computing application, student modeling. Our results indicate the effectiveness of the SANN model in capturing important syntactic and semantic information from students' code, allowing the construction of accurate student models, which serve as the foundation for generating adaptive instructional support such as individualized hints and feedback.

Recommender systems (RSs) have undoubtedly played a significant role in addressing the information overload problem by efficiently filtering and suggesting relevant items to users. These systems use both explicit and implicit user preferences to filter available data and suggest items that might align with the user’s interests. This can range from recommending movies on a streaming platform based on previous views to suggesting products for purchase based on browsing history. In their early stages, RSs focused on enhancing their algorithmic capabilities to provide accurate recommendations. However, the overemphasis on algorithms resulted in neglecting the human aspect of the user experience. Recognizing this limitation, recent trends in RSs have started to shift their attention toward incorporating Symbiotic Human-Machines Decision Making models. These models aim to provide users with dynamic and persuasive interfaces that empower them to understand and engage better with the recommendations. This shift represents an essential step in creating recommender systems that truly resonate with users and create a more enjoyable, trustable, and user-friendly experience. A crucial aspect of recommender systems’ evolution lies in their proactive nature. Early works focused on designing systems that could proactively anticipate user preferences and needs. While this remains a valuable trait, modern RSs also recognize the importance of giving users control and transparency over their recommendations. Striking the right balance between proactivity and user control ensures that the system supports users without being overly intrusive, thus enhancing their overall satisfaction. These aspects are the main discussion topics of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’23. In this summary, we introduce the workshop’s motivation and view, review its history, and discuss the most critical issues that deserve attention for future research directions.

The constant increase in the amount of data and information available on the Web has made the development of systems that can support users in making relevant decisions increasingly important. Recommender systems (RSs) have emerged as tools to address this task. RSs use the preferences expressed by a user, either explicitly or implicitly, to filter the available information and proactively suggest items that might be of interest to him or her. Although in early works about the topic there was a strong interest in ways to make such systems proactive, user-friendly, and persuasive, over time they became increasingly focused on the algorithmic component solely. However, this trend is gradually being reversed and always more attention is nowadays placed also on Human Decision Making models that focus on supporting the end user in understanding what is being proposed through RSs by using dynamic and persuasive interfaces. A recommender system should be based on valuable strategies for proactively guiding users to items that match their preferences and therefore should put attention on how it is possible to make this process trustable, pleasant, and user-friendly. Such systems, moreover, should take into account psychological, cognitive and emotional aspects to enable personalization that is appropriate not only to the context of use but also to the psychological reactions of the end user. The workshop provides a venue for works that invest in the design of recommender systems which consider users’ experience during the interaction, as well as for works that explore the implications of human-computer interactions with different theories of human decision-making. In this summary, we introduce the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’22, review its history, and discuss the most important topics considered at the workshop.

Recommending online resources to patients with ovarian cancer and their caregivers is a challenging task. On one hand, the recommended items must be relevant, recent, and reliable. On the other hand, they need to match the user’s levels of disease-specific health literacy. In this demonstration, we describe the overall architecture and key components of HELPeR, a knowledge-adaptive interactive recommender system for ovarian cancer patients and their caregivers.

Recommender systems were originally developed as interactive intelligent systems that can proactively guide users to items that match their preferences. Despite its origin on the crossroads of HCI and AI, the majority of research on recommender systems gradually focused on objective accuracy criteria paying less and less attention to how users interact with the system as well as the efficacy of interface designs from users’ perspectives. This trend is reversing with the increased volume of research that looks beyond algorithms, into users’ interactions, decision making processes, and overall experience. The series of workshops on Interfaces and Human Decision Making for Recommender Systems focuses on the ”human side” of recommender systems. The goal of the research stream featured at the workshop is to improve users’ overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems. In this summary, we introduce the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’21, review its history, and discuss most important topics considered at the workshop.

We present Grapevine, a user-controlled recommender that enables undergraduate and graduate students to find a suitable research advisor. This system combines the ideas from the areas of exploratory search, user modeling, and recommender systems by employing state-of-the-art knowledge extraction, grape-based recommendation, and an intelligent user interface. In this paper, we demonstrate the system’s key components and how they work as a whole.

As an interactive intelligent system, recommender systems are developed to give recommendations that match users’ preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users’ perspectives. The field has reached a point where it is ready to look beyond algorithms, into users’ interactions, decision making processes, and overall experience. The series of workshops on Interfaces and Human Decision Making for Recommender Systems focuses on the ”human side” of recommender systems. The goal of the research stream featured at the workshop is to improve users’ overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems. In this summary, we introduce 7th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’20, review its history, and discuss most important topics considered at the workshop.

As an interactive intelligent system, recommender systems are developed to give recommendations that match users' preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users' perspectives. The field has reached a point where it is ready to look beyond algorithms, into users' interactions, decision making processes, and overall experience. This workshop will focus on the "human side" of recommender systems research. The workshop goal is to improve users' overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems.

The increasing utilization of massive open online courses has significantly expanded global access to formal education. Despite the technology's promising future, student interaction on MOOCs is still a relatively under-explored and poorly understood topic. This work proposes a multi-level pattern discovery through hierarchical discriminative tensor factorization. We formulate the problem as a hierarchical discriminant subspace learning problem, where the goal is to discover the shared and discriminative patterns with a hierarchical structure. The discovered patterns enable a more effective exploration of the contrasting behaviors of two performance groups. We conduct extensive experiments on several real-world MOOC datasets to demonstrate the effectiveness of our proposed approach. Our study advances the current predictive modeling in MOOCs by providing more interpretable behavioral patterns and linking their relationships with the performance outcome.

This article explores the factors influencing the future citations of conference papers. We concentrated on the explanatory power of early attention on conference papers for citations collected from Google Scholar and Scopus. The early attention data includes users’ online activities in a conference support system: CN3. Bookmarks from the bibliographic management system, Citeulike, were used as a collateral source of early attention. To examine the chronological contributions of 13 factors on citations, a multiple sequential regression analysis was conducted for three timepoints of the publication cycle—paper submission, time of conferences, and months after conferences. Our results illustrate that online readers’ early attention of Citeulike bookmarks were found to have the most influence on the future impact of the conference papers. The early attention records from CN3 made noteworthy improvements to explaining both the Google and Scopus citations as well. We also found that the type of papers the number of papers presented at a conference, and the best article award records were significant factors influencing future citations. However, the magnitude of the effects made by online readers’ early attention from both sources appears to be larger than these three traditional factors.

As an interactive intelligent system, recommender systems are developed to give recommendations that match users' preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users' perspectives. The field has reached a point where it is ready to look beyond algorithms, into users' interactions, decision making processes, and overall experience. his workshop will focus on the "human side" of recommender systems research. The workshop goal is to improve users' overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems.

The education sector has been undergoing a welcoming change in recent years with the introduction of a wide variety of digital content openly available to students. Due to the volume of this new digital content, it is very difficult for learners to find the needed information at the right time. Digital textbooks, as well-curated domain knowledge sources, could provide a conceptual and physical platform that unites disparate educational resources as one entity. Educational resource linkage, with state-of-the-art techniques, are based on term-level and topic-level representations. However, term-level representations suffer from the term-mismatch problem and often, topics are too broad for linking to other educational resources. To address these challenges, we propose to link educational resources through concept-level representation. The proposed model generates concept embeddings by utilizing domain-specific educational content and external knowledge graph resources to achieve robust and effective concept-level representations. We conducted evaluations of the proposed models on multiple contents linking tasks, and the results demonstrate that concept-level representations perform better than the state-of-the-art representations in helping students to find more learning resources easily. This could increase both students' learning and satisfaction.

Educational content of today no longer only resides in textbooks and classrooms; more and more learning material is found in a free, accessible form on the Internet. Our long-standing vision is to transform this web of educational content into an adaptive, web-scale "textbook", that can guide its readers to most relevant "pages" according to their learning goal and current knowledge. In this paper, we address one core, long-standing problem towards this goal: identifying outcome and prerequisite concepts within a piece of educational content (e.g., a tutorial). Specifically, we propose a novel approach that leverages textbooks as a source of distant supervision, but learns a model that can generalize to arbitrary documents (such as those on the web). As such, our model can take advantage of any existing textbook, without requiring expert annotation. At the task of predicting outcome and prerequisite concepts, we demonstrate improvements over a number of baselines on six textbooks, especially in the regime of little to no ground-truth labels available. Finally, we demonstrate the utility of a model learned using our approach at the task of identifying prerequisite documents for adaptive content recommendation --- an important step towards our vision of the "web as a textbook".

As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selection behavior of the users. Consequently, it is important to look beyond algorithms. The main goals of the IntRS workshop are to analyze the impact of user interfaces and interaction design, and to explore human interaction with recommender systems from a human decision making perspective. Methodologies for evaluating these aspects are also within the scope of the workshop.

As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selection behavior of the users. Consequently, it is important to look beyond algorithms. The main goals of the IntRS workshop are to analyze the impact of user interfaces and interaction design, and to explore human interaction with recommender systems. Methodologies for evaluating these aspects are also within the scope of the workshop.

Although the volume of online educational resources has dramatically increased in recent years, many of these resources are isolated and distributed in diverse websites and databases. This hinders the discovery and overall usage of online educational resources. By using linking between related subsections of online textbooks as a testbed, this paper explores multiple knowledge-based content linking algorithms for connecting online educational resources. We focus on examining semantic-based methods for identifying important knowledge components in textbooks and their usefulness in linking book subsections. To overcome the data sparsity in representing textbook content, we evaluated the utility of external corpuses, such as more textbooks or other online educational resources in the same domain. Our results show that semantic modeling can be integrated with a term-based approach for additional performance improvement, and that using extra textbooks significantly benefits semantic modeling. Similar results are obtained when we applied the same approach to other domains.

As the heterogeneity of data sources are increasing on the web, and due to the sparsity of data in each of these data sources, cross-domain recommendation is becoming an emerging research topic in the recent years. Cross-domain collaborative filtering aims to transfer the user rating pattern from source (auxiliary) domains to a target domain for the purpose of alleviating the sparsity problem and providing better target recommendations. However, the studies so far have either focused on a limited number of domains that are assumed to be related to each other (such as books and movies), or a division of the same dataset (such as movies) into different domains based on an item characteristic (such as genre). In this paper, we study a broad set of domains and their characteristics to understand the factors that affect the success or failure of cross-domain collaborative filtering, the amount of improvement in cross-domain approaches, and the selection of best source domains for a specific target domain. We propose to use Canonical Correlation Analysis (CCA) as a significant major factor in finding the most promising source domains for a target domain, and suggest a cross-domain collaborative filtering based on CCA (CD-CCA) that proves to be successful in using the shared information between domains in the target recommendations.

No abstract available.

Crowdsourcing has emerged as a way to harvest social wisdom from thousands of volunteers to perform a series of tasks online. However, little research has been devoted to exploring the impact of various factors such as the content of a resource or crowdsourcing interface design on user tagging behavior. Although images' titles and descriptions are frequently available in image digital libraries, it is not clear whether they should be displayed to crowdworkers engaged in tagging. This paper focuses on offering insight to the curators of digital image libraries who face this dilemma by examining (i) how descriptions influence the user in his/her tagging behavior and (ii) how this relates to the (a) nature of the tags, (b) the emergent folksonomy, and (c) the findability of the images in the tagging system. We compared two different methods for collecting image tags from Amazon's Mechanical Turk's crowdworkers—with and without image descriptions. Several properties of generated tags were examined from different perspectives: diversity, specificity, reusability, quality, similarity, descriptiveness, and so on. In addition, the study was carried out to examine the impact of image description on supporting users' information seeking with a tag cloud interface. The results showed that the properties of tags are affected by the crowdsourcing approach. Tags from the “with description” condition are more diverse and more specific than tags from the “without description” condition, while the latter has a higher tag reuse rate. A user study also revealed that different tag sets provided different support for search. Tags produced “with description” shortened the path to the target results, whereas tags produced without description increased user success in the search task.

As an interactive intelligent system, recommender systems are developed to give predictions that match users preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from the end-user perspective. The field has reached a point where it is ready to look beyond algorithms, into users interactions, decision making processes and overall experience. Accordingly, the goals of this workshop (Int[email protected]) are to explore the human aspects of recommender systems, with a particular focus on the impact of interfaces and interaction design on decision-making and user experiences with recommender systems, and to explore methodologies to evaluate these human aspects of the recommendation process that go beyond traditional automated approaches.

This paper investigates recommendation algorithms to suggest talks of interest to attendees of research conferences. In this study, based on a social conference support system Conference Navigator 3 (CN3), we explored three kinds of knowledge sources to generate recommendations: users' preference about talks (CN3 bookmarks), users' social networks (research collaboration network and CN3 following network) and talk content information (titles and abstracts). Using these sources, we explored a diverse set of algorithms from non-personalized community vote-based recommendations and conventional collaborative filtering recommendations to hybrid recommendations such as social network-based (SN) recommendations boosted by content information of talks. We found that SN recommendations fused with content information outperformed the other approaches. Moreover, for cold-start users who have an insufficient number of bookmarks to express their preferences, the recommendations based on their social connections also generated significantly better suggestions than the other approaches. Between two kinds of social networks that we considered as foundations of recommendations, there was no significant difference in the quality of the recommendations.

BooksOnline'12, the fifth workshop in the series, aims to offer a forum for bringing together expertise from academia, industry and libraries to facilitate the exchange of research results and technology in the field of digital libraries with specific focus on online books and complementary social media. The focus of this year's workshop is "engaging reading experiences", starting from the act of deciding what to read, through the exploration and interpretation of a book's content, to sharing the overall experience. Within this overall umbrella theme, the accepted papers naturally showed three salient themes: (1) Search and Discovery, (2) Personalization and Recommendation, and Reading Experiences beyond Text. The contributions demonstrate a range of technologies, including a collaborative tabletop visual approach to support the searching and discovery of books, co-citation methods to enhance document retrieval; exploring open issues in audio-book production to support non-text based reading and improving e-book accessibility; new approaches to recommendation that take into account writing style as well as looking specifically to young readers and their needs in order to develop recommendation tools that consider both content and reading level and match these against the readers' specific interests and reading ability. Following in the theme of the reader playing a central role in the future of our digital era, we are honored to welcome Maribeth Back from FX Palo Alto and Natasa Milic-Frayling from Microsoft Research as our keynote speakers.

The BooksOnline Workshop series aims to foster the discussion and exchange of research ideas towards addressing challenges and exploring opportunities around large collections of digital books and complementary media. The fourth workshop in the series, BooksOnline'11 pays special attention to the role of social media and the phenomena of crowdsourcing in the context of online books, which is expected to be key in defining new user experiences in digital libraries and on the Web. The workshop boasts a high quality program, including keynote addresses by Ville Miettinnen, CEO of Microtask and Adam Farquhar, Head of Digital Library Technology at The British Library. From the accepted papers two main themes became salient: 1) Information retrieval and information extraction methods focused on enhancing digital libraries, and 2) Studies and analyses of reading experience and behaviour. This paper provides an overview of the workshop and the accepted contributions.

No abstract available.

The goal of the 3rd BooksOnline Workshop is to bring together researchers and industry practitioners in information retrieval, digital libraries, e-books, human computer interaction, publishing industry, and online book services to foster progress on addressing challenges and exploring opportunities around large collections of digital books and complementary media. Towards this goal, the workshop programme consists of contributions both from academia and industry, including two keynote talks: James Crawford from Google Books and John Mark Ockerbloom from the University of Pennsylvania.

No abstract available.

This paper aims to combine information about users' self-defined social connections with traditional collaborative filtering (CF) to improve recommendation quality. Specifically, in the following, the users' social connections in consideration were groups. Unlike other studies which utilized groups inferred by data mining technologies, we used the information about the groups in which each user explicitly participated. The group activities are centered on common interests. People join a group to share and acquire information about a topic as a form of community of interest or practice. The information of this group activity may be a good source of information for the members. We tested whether adding the information from the users' own groups or group members to the traditional CF-based recommendations can improve the recommendation quality or not. The information about groups was combined with CF using a mixed hybridization strategy. We evaluated our approach in two ways, using the Citeulike data set and a real user study.

Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems. In joining this stream of research, we have developed and evaluated two enhancements of user-based collaborative filtering algorithms to provide recommendations of articles on Cite ULike, a social tagging service for scientific articles. The result obtained after two phases of evaluation suggests that both enhancements are beneficial. Incorporating the number of raters into the algorithms, as we do in our NwCF approach, leads to an improvement of precision, while tag-based BM25 similarity measure, an alternative to Pearson correlation for calculating the similarity between users and their neighbors, increases the coverage of the recommendation process.

Information retrieval is one of the most popular information access methods for overcoming the information overload problem of the Web. However, its interaction model is still utilizing the old text-based ranked lists and static interaction algorithm. In this paper, we introduce our adaptive visualization approach for searching the Web, which we call Adaptive VIBE. It is an extended version of a reference point-based spatial visualization algorithm, and is designed to serve as a user interaction module for a personalized search system. Personalized search can incorporate dynamic user interests and different contexts, improving search results. When it is combined with adaptive visualization, it can encourage users to become involved in the search process more actively by exploring the information space and learning new facts for effective searching. In this paper, we introduce the rationale and functions of our adaptive visualization approach and discuss the approaches' potential to create a better search environment for the Web.

Collaborative tagging systems pose new challenges to the developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of collaborative tagging systems. In joining this stream of research, we have developed and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. The first approach, Classic Collaborative filtering (CCF) uses Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. The second approach, Neighbor-weighted Collaborative Filtering, takes into account the number of raters in the ranking formula of the recommendations. The third approach explores an innovative way to form the user neighborhood based on a modified version of the Okapi BM25 model over users' tags. Our results suggest that both alterations of CCF are beneficial. Incorporating the number of raters into the algorithms leads to an improvement of precision, while tag-based BM25 can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors.

Personalized Web search has emerged as one of the hottest topics for both the Web industry and academic researchers. However, the majority of studies on personalized search focused on a rather simple type of search, which leaves an important research topic - the personalization in exploratory searches - as an under-studied area. In this paper, we present a study of personalization in task-based information exploration using a system called TaskSieve. TaskSieve is a Web search system that utilizes a relevance feedback based profile, called a "task model", for personalization. Its innovations include flexible and user controlled integration of queries and task models, task-infused text snippet generation, and on-screen visualization of task models. Through an empirical study using human subjects conducting task-based exploration searches, we demonstrate that TaskSieve pushes significantly more relevant documents to the top of search result lists as compared to a traditional search system. TaskSieve helps users select significantly more accurate information for their tasks, allows the users to do so with higher productivity, and is viewed more favorably by subjects under several usability related characteristics.

Adaptive information filtering is a promising tool for both casual Web news readers and professional intelligence analysts. Adaptive filtering augments the traditional query- or profile-based rankings provided by search engines. An interesting research challenge in this context is to offer users more control over the rankings by letting them mediate between the two extremes - query- and profile-based rankings. To address this challenge, we developed an adaptive relevance-based visual exploration tool based on the VIBE (visual information browsing environment) visualization approach, which was previously developed at our School. This paper presents the rationale and functionality of this visual exploration tool and reports the results of its preliminary evaluation.

In profile-based or content-based adaptive systems, one of the open research questions is how frequently the user's profile and the list of recommended items should be updated. Different systems tend to choose one of the two extremes. Some systems do it once per session (thus called between-session update strategy), whereas some others update whenever there is feedback (called instant update strategy). This paper presents our attempt to assess the value of keeping the list of recommended items up-to-date in the context of task-based information exploration. We conducted controlled studies involving human users performing realistic tasks using two systems that have the same adaptive filtering engine but with the above two different update strategies. Our results show that the between-session strategy helped to find better quality information, and received better subjects' responses about its usefulness and usability. However, it prolonged the selection of useful passages, whereas the instant update strategy helped subjects to obtain almost all of their selected passages (>98%) within the first 5 minutes. Based on the results, we hypothesize that the best strategy for updating might be a hybrid between the two update strategies, where both adaptability and stability can be achieved.

Over the last five years, a range of projects have focused on progressively more elaborated techniques for adaptive news delivery. However, the adaptation process in these systems has become more complicated and thus less transparent to the users. In this paper, we concentrate on the application of open user models in adding transparency and controllability to adaptive news systems. We present a personalized news system, YourNews, which allows users to view and edit their interest profiles, and report a user study on the system. Our results confirm that users prefer transparency and control in their systems, and generate more trust to such systems. However, similar to previous studies, our study demonstrate that this ability to edit user profiles may also harm the system.s performance and has to be used with caution.