Children regularly engage with online information access systems, yet much of our understanding of how they search has not been revisited in recent years. Further, the reasons children avoid certain search practices remain largely unexamined. To address these gaps, we introduce a tangible card‑based activity designed to elicit and make visible children’s reasoning during search. Building on prior literature, we examine whether previously documented practices remain relevant for the current generation of young searchers. We, then, investigate children’s explanations for avoiding particular practices, revealing insights into their understanding of the information-seeking process. Overall, this work contributes to a better understanding of children’s decision‑making processes when conducting online searches in school contexts, shedding light on both rationally adopted and discarded online search practices in the classroom setting.

Teamwork is an integral part of computer science, but group projects in education do not always teach teamwork skills tailored to the context of software project teams. As a result, early-career computer scientists often struggle with team projects that involve interdependent tasks. This research focuses on identifying which teamwork skills are important to computer scientists at the start of their careers and how they perceive the career-education gap regarding teamwork. We conducted semi-structured interviews with 19 early-career computer scientists and analysed them using deductive and inductive coding. Social skills were perceived as the most important by the participants. Although leadership and coordination were among the most prevalent in university education, they were considered less important in industry. These findings can be used to motivate educational design choices in university group projects, focusing on teaching the teamwork skills that will be relevant to students' careers.

The traditional Information Retrieval (IR) evaluation framework—anchored in topical relevance and relevance‑based metrics—reflects a system-centred perspective. Yet for specific user groups, relevance alone is insufficient; benchmarking that relies exclusively on conventional metrics overlooks qualities intrinsic to the users IR approaches are meant to serve. Here, we draw attention to Children IR and examine the value of extending traditional evaluation with a human-centred perspective that accounts for how children interpret and evaluate information to more authentically capture performance and better reflect how well an approach truly meets children’s needs. Our empirical exploration using a child‑focused dataset, multiple ranking strategies, and traditional and extended frameworks reveals not only the limitations of relevance-based assessments but also the advantages of employing frameworks that are tailored to reflect the needs of child users, paving the way for more inclusive and effective evaluation frameworks.

Recommender systems increasingly shape children’s online experiences, yet the risks they might pose remain poorly understood. Formal risk assessments are rare, overlook children’s experiences, treat them as a homogeneous group, and often consider risks on a population-wide level. To provide a more nuanced child-centered perspective on risks associated with recommenders, we conduct a comprehensive risk assessment that examines multiple risks simultaneously and captures individual-level exposure rather than solely aggregated metrics. We also explore behavioral characteristics that make some children more susceptible to risk exposure to grasp how individual differences shape the manifestation of risks. Our results highlight the need for risk assessments that recognize heterogeneity among children and understand when risks become ‘systemic’ through individualized recommendation patterns. These insights spark the discussion about child-aware recommenders and ongoing regulatory efforts for evidence-based accountability.

Children are often exposed to items curated by recommendation algorithms. Yet, research seldom considers children as a user group, and when it does, it is anchored on datasets where children are underrepresented, risking overlooking their interests, favoring those of the majority, i.e., mainstream users. Recently, Ungruh et al. demonstrated that children's consumption patterns and preferences differ from those of mainstream users, resulting in inconsistent recommendation algorithm performance and behavior for this user group. These findings, however, are based on two datasets with a limited child user sample. We reproduce and replicate this study on a wider range of datasets in the movie, music, and book domains, uncovering interaction patterns and aspects of child-recommender interactions consistent across domains, as well as those specific to some user samples in the data. We also extend insights from the original study with popularity bias metrics, given the interpretation of results from the original study. With this reproduction and extension, we uncover consumption patterns and differences between age groups stemming from intrinsic differences between children and others, and those unique to specific datasets or domains.

In an existing study, the InsideOut Framework is used to produce and explore the emotional profiles of search engines (SE) in response to queries formulated by children aged 9 to 11 in the classroom context, revealing the emotional diversity of SE responses. Since then, there have been significant technological advances in emotion detection and information access. In this work, we conduct a comprehensive reproducibility study where we probe today's emotional profile of SE using both a lexicon-based and a language-model based approach tailored to the Italian language, thus addressing an acknowledged limitation of the original study. Additionally, considering the prevalence of agents based on Large Language Models (LLM) as information access systems among children, we extend the analysis to capture the emotional undertones of LLM responses and juxtapose them to those of SE. Our findings emphasize the importance of leveraging the appropriate emotion detection technique to produce and explore emotional profiles and lead us to reflect on the interplay of emotions on children's search-as-learning experience.

The Workshop on Information Retrieval for Understudied Users (IR4U2) serves as a platform to highlight information retrieval (IR) research that directly impacts often understudied user groups. The second (IR4U2) workshop focuses on a user-centred AI perspective, which is vital for informing the design, development, and assessment of information retrieval systems that thoughtfully address the diverse needs of understudied populations, ensuring genuine accessibility and inclusivity. The objectives of IR4U2 are: (1) to build community and awareness by sharing AI and IR developments that serve underrepresented user groups in this research area; (2) to identify challenges and open issues along with lessons learned and challenges inherent to this area of research; and (3) to spark discussions that establish common frameworks for future research.

There is a lack of a steady and solid influx of information retrieval (IR) research that has children (as the user group) as the protagonist. Existing work is scattered, conducted by only a few research groups, and often based on small-scale user studies or data that cannot be widely shared. Moreover, much of the current research focuses on specific age ranges and abilities, neglecting the broader spectrum of children's needs. Consequently, the paucity of IR research on how search and recommender systems serve and/or ultimately affect children translates into one of many 'Low-resource environments' in IR. Drawing from the literature and our experience in this area, we highlight key challenges and encourage greater attention from the IR community to address this critical gap.

Simulation is widely used in recommender systems research to study algorithm behavior and its impact on users. A common strategy involves adopting a universal choice model to represent users, assuming all follow the same consumption patterns. This one-size-fits-all approach overlooks the diversity in user preferences and decision-making patterns. In this work, we scrutinize whether this universal view fails to account for unique user behavior, thus harming realism and reliability of simulation outcomes. We conduct multiple simulations with various recommendation algorithms and choice models in the movie domain, comparing outcomes to users' organic consumption patterns. Further, we evaluate whether a holistic model that captures users' differences in behavior would better reflect a wide user base. Our findings highlight the limitations of using a naive, universal choice model and emphasize the need for more nuanced, user-specific approaches to make contributions from simulation studies more reflective of real-world effects.

In this work, we discuss the findings emerging from co-design sessions between children ages 6 to 11 and adults, which were conducted to advance knowledge on how to best support children using well-known search tools for online information discovery. Specifically, we argue that by leveraging scaffolding, gamification techniques, and design choices via an application, it is possible to enhance children’s habits related to query formulation. Outcomes from this preliminary exploration reveal that gameplay incentives (e.g. levels, points, and other incentives like customization) are needed and effective in motivating further interaction with the application, which in turn leads to further utilization of the scaffolding needed to positively impact query formulation.

We discuss the foundation of a collaborative effort to explore AI’s role in supporting (teachers and) children in their learning experiences. We integrate principles of educational psychology, AI, and HCI, and align with best practices in education while undertaking a human-centered focus on design and development that puts the student at the centre and keeps the expert-in-the-loop. Initially, we study assessment items—questions or tasks tied to a learning target. These items vary in complexity, serve as indicators of students’ grasp of specific concepts and spotlight areas where support may be needed. This preliminary analysis will help us outline a framework to guide the design and evaluation of AI technology for K-12 education. Such a framework would ensure that assessment item generation technology goes beyond the current one-dimensional approach by incorporating multifaceted, adaptable perspectives that consider the variegated landscape of learners’ needs, subject matter complexities, and pedagogical goals.

Children often interact with search engines within a classroom context to complete assignments or discover new information. To successfully identify relevant resources among those presented on a search engine results page (SERP), users must first be able to comprehend the text included in SERP snippets. While this task may be straightforward for an adult user, children may encounter obstacles in terms of readability and comprehension when attempting to navigate a SERP. Previous research has demonstrated the positive impact of including visual cues on a SERP as relevance signals to guide children toward appropriate resources. In this work, we explore the effect of supplying visual cues related to readability and text difficulty on children’s (ages 6-12) navigation of a SERP. Using quantitative data collected from user-interface interactions and qualitative data gathered from participant interviews, we analyze the impact of these visual cues on children’s selection of results on a SERP when carrying out information discovery tasks.

An essential characteristic for unbiased and diligent information-seeking that can enable informed opinion formation and decision-making is intellectual humility (IH), the awareness of the limitations of one’s knowledge and opinions. While researchers have recognized the potential to boost IH in individuals, the effect of such interventions on their search behavior, along with the broader significance of IH in the context of web search on debated topics remains unexplored. In this paper, we present the results of a preregistered user study (N = 299) that we conducted to (1) test the effect of three interventions that boost self-reported IH on opinionated individuals’ search behavior and (2) explore the role of IH in the search process of opinionated individuals more broadly. IH-boosting interventions did not affect search behavior; we attribute this to the high familiarity of the search environment, prompting searchers to default to their usual search behavior. Still, explorations of the role of IH in the search process indicate that IH and IH-related search intentions should be considered as relevant factors in the pursuit of supporting unbiased and diligent search on debated topics. Based on our exploratory findings, we argue that future research should investigate interventions that are more directly integrated into the search process, as well as such that combine boosting IH with encouraging searchers to approach the search task in an IH-driven way and promoting transparency for appropriate reliance on the search system and ranking.

Current approaches in automatic readability assessment have found success with the use of large language models and transformer architectures. These techniques lead to accuracy improvement, but they do not offer the interpretability that is uniquely required by the audience most often employing readability assessment tools: teachers and educators. Recent work that employs more traditional machine learning methods has highlighted the linguistic importance of considering semantic and syntactic characteristics of text in readability assessment by utilizing handcrafted feature sets. Research in Education suggests that, in addition to semantics and syntax, phonetic and orthographic instruction are necessary for children to progress through the stages of reading and spelling development; children must first learn to decode the letters and symbols on a page to recognize words and phonemes and their connection to speech sounds. Here, we incorporate this word-level phonemic decoding process into readability assessment by crafting a phonetically-based feature set for grade-level classification for English. Our resulting feature set shows comparable performance to much larger, semantically- and syntactically-based feature sets, supporting the linguistic value of orthographic and phonetic considerations in readability assessment.

The AltRecsys workshop, held in conjunction with the 18th edition of the ACM Conference on Recommender Systems (RecSys) in Bari, Italy, provides a platform for highlighting “alternative” work in recommender systems. Modeled after alt.chi and the CRAFT sessions at the FAccT conference, AltRecSys offers a space to discuss interesting, preliminary, offbeat, unexpected, and critical ideas in recommender systems that do not (yet) fit well into the kinds of publications and formats for the main conference or traditional workshops. This workshop is not a venue to showcase research advances. Instead, it is envisioned as a forum where researchers, (industry) practitioners, and other associated stakeholders can exchange ideas and together identify areas of study and new questions to expand the discussions and research agendas of the RecSys community in future years. The call for contributions and the workshop sessions are centered around the question “what are the vital questions, needs, or opportunities that the RecSys community is currently overlooking?”

Popularity bias is a prominent phenomenon in recommender systems (RS), especially in the music domain. Although popularity bias mitigation techniques are known to enhance the fairness of RS while maintaining their high performance, there is a lack of understanding regarding users’ actual perception of the suggested music. To address this gap, we conducted a user study (n=40) exploring user satisfaction and perception of personalized music recommendations generated by algorithms that explicitly mitigate popularity bias. Specifically, we investigate item-centered and user-centered bias mitigation techniques, aiming to ensure fairness for artists or users, respectively. Results show that neither mitigation technique harms the users’ satisfaction with the recommendation lists despite promoting underrepresented items. However, the item-centered mitigation technique impacts user perception; by promoting less popular items, it reduces users’ familiarity with the items. Lower familiarity evokes discovery—the feeling that the recommendations enrich the user’s taste. We demonstrate that this can ultimately lead to higher satisfaction, highlighting the potential of less-popular recommendations to improve the user experience.

When using web search engines to conduct inquiries on debated topics, searchers’ interactions with search results are commonly affected by a combination of searcher and system biases. While prior work has mainly investigated these biases in isolation, there is a lack of a comprehensive understanding of web search on debated topics. Addressing this gap, we conducted an exploratory user study (N = 255), aimed at advancing the understanding of the intricate searcher-system interplay. Particularly, we investigated the relations between (i) search system exposure, searchers’ attitude strength, prior knowledge, and receptiveness to opposing views, (ii) search interactions, and (iii) post-search epistemic states. We observed that search interaction was shaped by search system exposure, attitude strength, and prior knowledge, and that attitude change was influenced by the level of confirmation bias and initial attitude strength, but not search system exposure. Insights from this work suggest the need to adapt interventions that mitigate the risks of searcher and system bias to searchers’ nuanced pre-search epistemic states. They further emphasize the threat of customizing the search ranking to enhance user satisfaction in the context of debated topics to responsible opinion formation.

This special issue highlights state-of-the-art research in intelligent systems and technology for people with diverse abilities. To control scope, we pay particular attention to cognitive diversity, including but not limited to, neurodevelopmental disorders and autism, cognitive and learningdisabilities, and dementia. We introduce the papers in this special issue by contextualizing them according to different research areas. By curating leading-edge contributions in this area, we aim to raise awareness about research challenges and requirements inherent in the development and assessment of systems designed for these categories of users.

The use of clarifying questions (CQs) is a fairly new and useful technique to aid systems in recognizing the intent, context, and preferences behind user queries. Yet, understanding the extent of the effect of CQs on user behavior and the ability to identify relevant information remains relatively unexplored. In this work, we conduct a large user study to understand the interaction of users with CQs in various quality categories, and the effect of CQ quality on user search performance in terms of finding relevant information, search behavior, and user satisfaction. Analysis of implicit interaction data and explicit user feedback demonstrates that high-quality CQs improve user performance and satisfaction. By contrast, low- and mid-quality CQs are harmful, and thus allowing the users to complete their tasks without CQ support may be preferred in this case. We also observe that user engagement, and therefore the need for CQ support, is affected by several factors, such as search result quality or perceived task difficulty. The findings of this study can help researchers and system designers realize why, when, and how users interact with CQs, leading to a better understanding and design of search clarification systems.

Researchers worldwide have explored the behavioral nuances that emerge from interactions of individuals afflicted by mental health disorders (MHD) with persuasive technologies, mainly social media. Yet, there is a gap in the analysis pertaining to a persuasive technology that is part of their everyday lives: web search engines (SE). Each day, users with MHD embark on information seeking journeys using popular SE, like Google or Bing. Every step of the search process for better or worse has the potential to influence a searcher’s mindset. In this work, we empirically investigate what subliminal stimulus SE present to these vulnerable individuals during their searches. For this, we use synthetic queries to produce associated query suggestions and search engine results pages. Then we infer the subliminal stimulus present in text from SE, i.e., query suggestions, snippets, and web resources. Findings from our empirical analysis reveal that the subliminal stimulus displayed by SE at different stages of the information seeking process differ between MHD searchers and our control group composed of “average” SE users. Outcomes from this work showcase open problems related to query suggestions, search engine result pages, and ranking that the information retrieval community needs to address so that SE can better support individuals with MHD.

Social networks are a platform for individuals and organizations to connect with each other and inform, advertise, spread ideas, and ultimately influence opinions. These platforms have been known to propel misinformation. We argue that this could be compounded by the recommender algorithms that these platforms use to suggest items potentially of interest to their users, given the known biases and filter bubbles issues affecting recommender systems. While much has been studied about misinformation on social networks, the potential exacerbation that could result from recommender algorithms in this environment is in its infancy. In this manuscript, we present the result of an in-depth analysis conducted on two datasets ( Politifact FakeNewsNet dataset and HealthStory FakeHealth dataset ) in order to deepen our understanding of the interconnection between recommender algorithms and misinformation spread on Twitter. In particular, we explore the degree to which well-known recommendation algorithms are prone to be impacted by misinformation. Via simulation, we also study misinformation diffusion on social networks, as triggered by suggestions produced by these recommendation algorithms. Outcomes from this work evidence that misinformation does not equally affect all recommendation algorithms. Popularity-based and network-based recommender algorithms contribute the most to misinformation diffusion. Users who are known to be superspreaders are known to directly impact algorithmic performance and misinformation spread in specific scenarios. Findings emerging from our exploration result in a number of implications for researchers and practitioners to consider when designing and deploying recommender algorithms in social networks.

The role that technology plays in supporting children at school and at home is more prominent than ever before due to the global COVID-19 pandemic. This has prompted us to focus the 6th International and Interdisciplinary Perspectives on Children & Recommender and Information Retrieval Systems (KidRec) workshop on what the lasting changes will be to the design and development of child information retrieval systems. After two years, are information retrieval systems used more in and out of the classroom? Are they more interactive, more or less personalized? What is the impact on the research and business community? Are there long-term and unexpected changes on the design, ethics, and algorithms? The primary goal of our workshop continues to be to build community by bringing together researchers, practitioners, and other stakeholders from various backgrounds and disciplines to understand and advance information retrieval systems for children.

We study the effect of visual cues on children searching in the classroom. We do so by examining whether Search Engine Result Pages (SERP) enhanced with emojis, unlike standard SERP, affect children’s search behaviour. To capture search behaviour, we use well-known metrics, in addition to users’ success in identifying relevant results on SERP. Outcomes from our work reveal that a one-size-fits-all approach for SERP does not befit students who are searching for learning. Thus, we discuss the implications of our findings and suggest directions for future research, focused on the design and evaluation of information retrieval systems that can better support the classroom setting.

In this paper, we take a step towards understanding how to design search engine results pages (SERP) that encourage children’s engagement as they seek for online resources. For this, we conducted a participatory design session to enable us to elicit children’s preferences and determine what children (ages 6–12) find lacking in more traditional SERP. We learned that children want more dynamic means of navigating results and additional ways to interact with results via icons. We use these findings to inform the design of a new SERP interface, which we denoted CHIRP. To gauge the type of engagement that a SERP incorporating interactive elements–CHIRP–can foster among children, we conducted a user study at a public school. Analysis of children’s interactions with CHIRP, in addition to responses to a post-task survey, reveals that adding additional interaction points results in a SERP interface that children prefer, but one that does not necessarily change engagement levels through clicks or time spent on SERP.

In this past year, the role of technology to support education has been more prominent than ever. This has prompted us to focus the 5th Edition of the International and Interdisciplinary Perspectives on Children & Recommender and Information Retrieval Systems (KidRec) around a major stakeholder when it comes to technology adoption for the classroom: the teacher. Much like in the previous editions of the workshop, our priority remains understanding what is good when it comes to information retrieval systems for children, this time from the perspectives of teachers. In order to control scope of our discussion and continue to advance knowledge in this area, we focus on technology that enables information discovery in the classroom context: search and recommendation systems. Expected outcomes from this workshop remain building community in this important area of work and extend the framework we have been defining to enable design and evaluation of good information retrieval systems for children.

We discuss the exploratory study we conducted to better understand children's ability to recognise relevant results when searching in the classroom. Teachers in two European schools sharing the same language assigned their students (ages 10 and 11) an online information discovery exercise about a history topic covered in class. For this, children used a classic search interface and two novel ones enriched with emojis associated to relevant vs. irrelevant results. At the end of the exercise, children filled out a post-task questionnaire meant to elicit their perception on usability of the interfaces. Guided by four lenses, we analyse our findings and discuss whether (i) emoji-enriched interfaces lead to better performance for children using a search engine in the classroom and (ii) "actions speak louder than words'' when looking at children's search experience. We learned various lessons from our examination of children's search behaviour that will guide the design of future interfaces, including the fact that emoji-enriched interfaces result in significant improvement in terms of children identifying relevant resources.

During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that seek to provide personalized interactions to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increasingly faced with challenges of dealing with greater variety and complexity in the inputs to those recommender systems. For example, there has been more reliance on fine-grained user signals as inputs rather than simple ratings or likes. Applications require more complex domain-specific constraints on inputs to the recommender systems. Likewise, the outputs of recommender systems are moving towards more complex composite items, such as package or sequence recommendations. This increasing complexity requires smarter recommender algorithms that can deal with this diversity in inputs and outputs. For the past four years, the ComplexRec workshop series has offered an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.

Music is an important part of childhood development, with online music listening platforms being a significant channel by which children consume music. Children’s offline music listening behavior has been heavily researched, yet relatively few studies explore how their behavior manifests online. In this paper, we use data from LastFM 1 Billion and the Spotify API to explore online music listening behavior of children, ages 6–17, using education levels as lenses for our analysis. Understanding the music listening behavior of children can be used to inform the future design of recommender systems.

Over 20 years ago, Information Retrieval (IR) researchers began their quest for sound IR systems for children. The path was not straightforward. Challenges posed by interface design, relevance determination, diverse contexts, ethics, and many more, were taken up and explored from different perspectives. Large projects such as Puppy-IR and the International Children's Digital Library gave this field a certain boost; still, there is neither a sound solution for children in the search area in 2021 nor a roadmap to get there. What is the reason for this? Does the field cry out for specific IR solutions developed on a small scale for very small sub-fields and specific target groups? Are there some significant unforeseen barriers that hinder researchers? What about obstacles natural to areas of study such as this one that require a multidisciplinary approach or involve protected populations? With this workshop, we want to bring together as many key experts as possible from research and industry who focus on IR for children to understand why, unlike other IR areas, this one has not flourished and look for the biggest challenges for the next 10 years. We are not only thinking of traditional researchers and designers but also of those who develop and use IR systems for fields, such as in music, film, and education, as a way to push past this immobility and look at the problem from new, and perhaps more stimulating, perspectives.

We report on the exploration we conducted to understand better children’s needs for the design of Search Engine Result Pages (SERP) that can help them notice relevant resources when performing online inquiry tasks in a classroom context. We analyse children’s interactions with traditional and emoji-enriched SERP and look for trends linking children’s engagement with SERP and search success (based on experts’ assessments). We also identify areas remaining to be interpreted and considered in future studies. On this mixed ground, we discuss the complexity of this design space and the need to bypass the one-size-fits-all approach in favor of adaptive SERP to cater to children’s different and ever-evolving skills in searching and recognising useful results if we aim to support learning.

Spellchecking functionality embedded in existing search tools can assist children by offering a list of spelling alternatives when a spelling error is detected. Unfortunately, children tend to generally select the first alternative when presented with a list of options, as opposed to the one that matches their intent. In this paper, we describe a study we conducted with 191 children ages 6-12 in order to offer empirical evidence of: (1) their selection habits when identifying spelling suggestions that match the word they meant to type, and (2) the degree of influence multimodal cues, i.e., synthesized speech and images, have in prompting children to select the correct spelling suggestion. The results from our study reveal that multimodal cues, primarily synthesized speech, have a positive impact on the children's ability to identify their intended word from a list of spelling suggestions.

For help with their spelling errors, children often turn to spellcheckers integrated in software applications like word processors and search engines. However, existing spellcheckers are usually tuned to the needs of traditional users (i.e., adults) and generally prove unsatisfactory for children. Motivated by this issue, we introduce KidSpell, an English spellchecker oriented to the spelling needs of children. KidSpell applies (i) an encoding strategy for mapping both misspelled words and spelling suggestions to their phonetic keys and (ii) a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word based on their respective keys. To assess the effectiveness of, we compare the model’s performance against several popular, mainstream spellcheckers in a number of offline experiments using existing and novel datasets. The results of these experiments show that KidSpell outperforms existing spellcheckers, as it accurately prioritizes relevant spelling corrections when handling misspellings generated by children in both essay writing and online search tasks. As a byproduct of our study, we create two new datasets comprised of spelling errors generated by children from hand-written essays and web search inquiries, which we make available to the research community.

We present the preliminary exploration we conducted to identify traits that can influence children’s preferences in books. Findings offer insights for the design of recommender algorithms that would look beyond patterns inferred from traditional user-system interactions (e.g., ratings) for recommendation purposes, since when it comes to children such data is rarely, if at all, available.

In the classroom, children mainly use general search systems such as Google, Baidu or Bing. For many years and from different perspectives, a call has been made that it is necessary to provide children in an educational context with child-friendly search systems. Research responding to this call often focuses on the relevance, readability and reliability of the retrieved documents. Instead, inspired by a recent study based on adult users on the role emotions play in web search, we explore whether and how children searching in a school context react to the emotional content often part of Search Engine Result Pages. We do so by examining emotions inferred from queries and corresponding retrieved resources in query logs produced by children ages 9 to 11 in a classroom setting in 3 different countries. We also consider teachers' observations that contextualize this analysis.

Most research efforts related to automatic readability assessment focus on the design of strategies that apply to a specific language. These state‐of‐the‐art strategies are highly dependent on linguistic features that best suit the language for which they were intended, constraining their adaptability and making it difficult to determine whether they would remain effective if they were applied to estimate the level of difficulty of texts in other languages. In this article, we present the results of a study designed to determine the feasibility of a cross‐lingual readability assessment strategy. For doing so, we first analyzed the most common features used for readability assessment and determined their influence on the readability prediction process of 6 different languages: English, Spanish, Basque, Italian, French, and Catalan. In addition, we developed a cross‐lingual readability assessment strategy that serves as a means to empirically explore the potential advantages of employing a single strategy (and set of features) for readability assessment in different languages, including interlanguage prediction agreement and prediction accuracy improvement for low‐resource languages.

Abstract The alignment of word embedding spaces in different languages into a common crosslingual space has recently been in vogue. Strategies that do so compute pairwise alignments and then map multiple languages to a single pivot language (most often English). These strategies, however, are biased towards the choice of the pivot language, given that language proximity and the linguistic characteristics of the target language can strongly impact the resultant crosslingual space in detriment of topologically distant languages. We present a strategy that eliminates the need for a pivot language by learning the mappings across languages in a hierarchical way. Experiments demonstrate that our strategy significantly improves vocabulary induction scores in all existing benchmarks, as well as in a new non-English–centered benchmark we built, which we make publicly available.

Misspellings in queries used to initiate online searches is an everyday occurrence. When this happens, users either rely on the search engine's ability to understand their query or they turn to spellcheckers. Spellcheckers are usually based on popular dictionaries or past query logs, leading to spelling suggestions that often better resonate with adult users because that data is more readily available. Based on an educational perspective, previous research reports, and initial analyses of sample search logs, we hypothesize that existing spellcheckers are not suitable for young users who frequently encounter spelling challenges when searching for information online. We present early results of our ongoing research focused on identifying the needs and expectations children have regarding spellcheckers.

Children use popular web search tools, which are generally designed for adult users. Because children have different developmental needs than adults, these tools may not always adequately support their search for information. Moreover, even though search tools offer support to help in query formulation, these too are aimed at adults and may hinder children rather than help them. This calls for the examination of existing technologies in this area, to better understand what remains to be done when it comes to facilitating query-formulation tasks for young users. In this paper, we investigate interaction elements of query formulation--including query suggestion algorithms--for children. The primary goals of our research efforts are to: (i) examine existing plug-ins and interfaces that explicitly aid children's query formulation; (ii) investigate children's interactions with suggestions offered by a general-purpose query suggestion strategy vs. a counterpart designed with children in mind; and (iii) identify, via participatory design sessions, their preferences when it comes to tools / strategies that can help children find information and guide them through the query formulation process. Our analysis shows that existing tools do not meet children's needs and expectations; the outcomes of our work can guide researchers and developers as they implement query formulation strategies for children.

Today's children spend considerable time online, searching and receiving information from various websites and apps. While searching for information, e.g. for school or hobbies, children use search systems to locate resources and receive site recommendations that might be useful for them. The call for good, reliable, child-friendly systems has been made many times and the thesis that the algorithms of "adult" information systems are not necessarily suitable or fair for children is widely accepted. However, there is still no clear and balanced view on what makes one search/recommendation system for children good or better than other systems, nor on what content should be considered "good enough to be retrieved" or recommended. The goal of this workshop is to bring together researchers and practitioners in education, child-development, computer science, and more who can address this questions while considering issues related to education, algorithms, ethics, privacy, evaluation.

We present StoryTime, a book recommender for children. Our web-based recommender is co-designed with children and uses images to elicit their preferences. By building on existing solutions related to both visual interfaces and book recommendation strategies for children, StoryTime can generate suggestions without historical data or adult guidance. We discuss the benefits of StoryTime as a starting point for further research exploring the cold start problem, incorporating historical data, and needs related to children as a complex audience to enhance the recommendation process.

As part of the main program of the 2019 ACM Recommender System Conference, the Late-Breaking Results offers a unique opportunity to share with the community the latest ideas related to recommender systems. This year, we received 42 submissions for the track, out of which 13 were accepted, resulting in a acceptance rate of 31%.

We present a multiattentive recurrent neural network architecture for automatic multilingual readability assessment. This architecture considers raw words as its main input, but internally captures text structure and informs its word attention process using other syntax- and morphology-related datapoints, known to be of great importance to readability. This is achieved by a multiattentive strategy that allows the neural network to focus on specific parts of a text for predicting its reading level. We conducted an exhaustive evaluation using data sets targeting multiple languages and prediction task types, to compare the proposed model with traditional, state-of-the-art, and other neural network strategies.

Popular tools used to search for online resources are tuned to satisfy a broad category of users---primarily adults. Because children have specific needs, these tools may not always be successful in offering the right level of support in their quest for information. While search tools often provide query assistance, children still face many difficulties expressing their information needs in the form of a query. In this paper, we share results from our ongoing research work focused on understanding children's interactions with query suggestions and their preferences with respect to suggestions offered by a general-purpose strategy versus a counterpart designed exclusively for children. Our goal is to inform researchers and developers about when it is necessary to turn to technologies tailored exclusively for children and to further outline needs that should be addressed when it comes to designing query-formulation-related technology for children.

Resources for children are abundant, but finding suitable and appropriate resources for children in our information-rich society can be challenging. Due to this abundance of information, systems to find and recommend appropriate information for children are needed. Recommender systems (RS) for children have only recently begun to be researched. This area of research brings together researchers in education, child-development, computer scientists, designers, and more who address several issues including those related to education, algorithms, ethics, privacy, security. In this workshop we will: discuss and identify issues related to RS designed for children including challenges and limitations, discuss possible solutions to the identified challenges and plan for future research, and of critical importance work to build a community that explicitly looks at these critical issues. This workshop has a specific theme of educationally-related recommendations, but welcomes other child-oriented recommender system contributions.

Popular search engines are usually tuned to satisfy the information needs of a general audience. As a result, non-traditional, yet active groups of users, such as children, experience challenges composing queries that can lead them to the retrieval of adequate results. To aid young users in formulating keyword queries that can facilitate their information-seeking process, we introduce ReQuIK, a multi-perspective query suggestion system for children. ReQuIK informs its suggestion process by applying (i) a strategy based on search intent to capture the purpose of a query, (ii) a ranking strategy based on a wide and deep neural network that considers both raw text and traits commonly associated with kid-related queries, (iii) a filtering strategy based on the readability levels of documents potentially retrieved by a query to favor suggestions that trigger the retrieval of documents matching children»s reading skills, and (iv) a content-similarity strategy to ensure diversity among suggestions. For assessing the quality of the system, we conducted initial offline and online experiments based on 591 queries written by 97 children, ages 6 to 13. The results of this assessment verified the correctness of ReQuIK»s recommendation strategy, the fact that it provides suggestions that appeal to children and ReQuIK»s ability to recommend queries that lead to the retrieval of materials with readability levels that correlate with children»s reading skills.

Games play a significant role in modern society, since they affect people of all ages and all walks of life, whether it be socially or mentally, and have direct impacts on adults with autism. Autism spectrum disorders (ASD) are a collection of neurodevelopmental disorders characterized by qualitative impairments in social relatedness and interaction, as well as difficulties in acquiring and using communication and language abilities. Adults with ASD often find it difficult to express and recognize emotions which makes it hard for them to interact with others socially. We have designed new interactive and collaborative games for autistic adults and developed a novel strategy to recommend games to them. Using modern computer vision and graphics techniques, we (i) track the player's speech rate, facial features, eye contact, audio communication, and emotional states, and (ii) foster their collaboration. These games are personalized and recommended to a user based on games interested to the user, besides the complexity of games at different levels according to the deficient level of the emotional understanding and social skills to which the user belongs. The objective of developing and recommending short-head (i.e., familiar) and long-tail (i.e., unfamiliar) games for adults with ASD is to enhance their social interacting skills with peers so that they can live a better life.

An online exchange system is a web service that allows communities to trade items without the burden of manually selecting them, which saves users' time and effort. Even though online book‐exchange systems have been developed, their services can further be improved by reducing the workload imposed on their users. To accomplish this task, we propose a recommendation‐based book exchange system, called EasyEx, which identifies potential exchanges for a user solely based on a list of items the user is willing to part with. EasyEx is a novel and unique book‐exchange system because unlike existing online exchange systems, it does not require a user to create and maintain a wish list, which is a list of items the user would like to receive as part of the exchange. Instead, EasyEx directly suggests items to users to increase serendipity and as a result expose them to items which may be unfamiliar, but appealing, to them. In identifying books to be exchanged, EasyEx employs known recommendation strategies, that is, personalized mean and matrix factorization, to predict book ratings, which are treated as the degrees of appeal to a user on recommended books. Furthermore, EasyEx incorporates OptaPlanner, which solves constraint satisfaction problems efficiently, as part of the recommendation‐based exchange process to create exchange cycles. Experimental results have verified that EasyEx offers users recommended books that satisfy the users' interests and contributes to the item‐exchange mechanism with a new design methodology.

The 1st Workshop on Children and Recommender Systems (KidRec) is taking place in Como, Italy August 27th, 2017 in conjunction with the ACM RecSys 2017 conference. The goals of the workshop are threefold: (1) discuss and identify issues related to recommender systems used by children including specific challenges and limitations, (2) discuss possible solutions to the identified challenges and plan for future research, and (3) build a community to directly work on these important issues.

We present CEST, a generic method for detection and rich summarization of events occurring in a city. CEST exploits Twitter metadata, does not need prior information on events, and is event category and structure agnostic. We developed CEST to process unstructured documents and take advantage of shorthand notations, hashtags, keywords, geographical and temporal data, as well as sentiment within tweets to both detect and summarize arbitrary events without prior knowledge. We also introduce a novel strategy that analyzes sentiment and tweeting behavior over time to create a qualitative score that captures events' overall appeal to attendees.

Existing popular web search engines have been widely used for retrieving information of interests by their users and offer query suggestions (QS) to assist them in exploring the wealth of information online. These search tools, however, are designed without any specific group of users in mind and thus are not tailored towards the specific needs of children, which can diminish their usability and design objectives when they are employed by children. Given the increasing use of the Web for educational and entertainment purposes by children, there is an urgent need to help them search the Web effectively. In this paper, we present a QS module, denoted CQS, which assists children in finding appropriate query keywords to capture their information needs by (i) analyzing content written for/by children, (ii) examining phrases and other metadata extracted from reputable (children’s) websites, and (iii) using a supervised learning approach to rank suggestions that are appealing to children. CQS offers suggestions with vocabulary that can be comprehended by children and with topics of interest to them. We conducted a number of empirical studies using keyword queries initiated by children, besides gathering feedback on the usefulness of CQS-generated suggestions through crowdsourcing. The performance evaluation of CQS revealed the effectiveness of the methodology of CQS. In addition, it demonstrated that CQS-generated suggestions were preferred over suggestions provided by Bing and Yahoo! and at least as comparable to queries suggested by Google.

The Internet is the biggest data-sharing platform, comprised of an immeasurable quantity of resources covering diverse topics appealing to users of all ages. Children shape tomorrow's society, so it is essential that this audience becomes agile with searching information. Although young users prefer well-known search engines, their lack of skill in formulating adequate queries and the fact that search tools were not designed explicitly with children in mind, can result in poor outcomes. The reasons for this include children's limited vocabulary, which makes it challenging to articulate information needs using short queries, or their tendency to create queries that are too long, which translates to few or irrelevant retrieved results. To enhance web search environments in response to children's behaviors and expectations, in this paper we discuss an initial effort to verify well-known issues, and identify yet to be explored ones, that affect children in formulating (natural language or keyword) queries. We also present a novel search intent module developed in response to these issues, which can seamlessly be integrated with existing search engines favored by children. The proposed module interprets a child's query and creates a shorter and more concise query to submit to a search engine, which can lead to a more successful search session. Initial experiments conducted using a sample of children queries validate the correctness of the proposed search intent module.

The readability levels of books identify suitable reading materials. Unfortunately, the majority of published books are assigned a readability level range, which is not useful to readers who look for books at a particular grade level. Existing readability formulas/analysis tools require at least an excerpt of a book to estimate its readability level, which is a severe constraint, since copyright laws prohibit book contents from being made publicly accessible. To alleviate the constraint, we have developed TRoLL which relies on publicly accessible online book metadata, in addition to using a book's snippet, if it is available, to predict its readability level. Based on a multi‐dimensional regression analysis, TRoLL determines the grade level of any book instantly, even without a sample of its text, and considers its topical suitability, which is unique. Furthermore, TRoLL is a significant contribution to the educational community, since its computed book readability levels can enrich K‐12 readers' book selections and aid parents, teachers, and librarians in locating reading materials suitable for their K‐12 readers, which can be a time‐consuming and frustrating task that does not always yield a quality outcome. Conducted empirical studies have verified the prediction accuracy of TRoLL and demonstrated its superiority over well‐known readability formulas/analysis tools.

Reading is a fundamental skill that each person needs to develop during early childhood and continue to enhance into adulthood. While children/teenagers depend on this skill to advance academically and become educated individuals, adults are expected to acquire a certain level of proficiency in reading so that they can engage in social/civic activities and successfully participate in the workforce. A step towards assisting individuals to become lifelong readers is to provide them adequate reading selections which can cultivate their intellectual and emotional growth. With that in mind, we have developed SOLE-R, a topic map-based tool that yields book recommendations. SOLE-R takes advantage of lexical and semantic resources to infer the likes/dislikes of a reader and thus is not restricted by the syntactic constraints imposed on existing recommenders. Furthermore, SOLE-R relies on publicly-accessible data on books to perform an in-depth analysis of the preferences of a reader that goes beyond book content or reading patterns explored by existing recommenders. We have verified the correctness of SOLE-R using a popular benchmark dataset. In addition, we have compared its performance with (state-of-the-art) recommendation strategies to further demonstrate the effectiveness of SOLE-R.

The academic performance of students is affected by their reading ability, which explains why reading is one of the most important aspects of school curriculums. Promoting good reading habits among K-12 students is essential, given the enormous influence of reading on students' development as learners and members of society. In doing so, it is indispensable to provide readers with engaging and motivating reading selections. Unfortunately, existing book recommenders have failed to offer adequate choices for K-12 readers, since they either ignore the reading abilities of their users or cannot acquire the much-needed information to make recommendations due to privacy issues. To address these problems, we have developed Rabbit, a book recommender that emulates the readers' advisory service offered at school/public libraries. Rabbit considers the readability levels of its readers and determines the facets, i.e.,appeal factors, of books that evoke subconscious, emotional reactions on a reader. The design of Rabbit is unique, since it adopts a multi-dimensional approach to capture the reading abilities, preferences, and interests of its readers, which goes beyond the traditional book content/topical analysis. Conducted empirical studies have shown that Rabbit outperforms a number of (readability-based) book recommenders.

Commercial web search engines include a query suggestion module so that given a user's keyword query, alternative suggestions are offered and served as a guide to assist the user in formulating queries which capture his/her intended information need in a quick and simple manner. Majorityof these modules, however, perform an in-depth analysis oflarge query logs and thus (i) their suggestions are mostlybased on queries frequently posted by users and (ii) theirdesign methodologies cannot be applied to make suggestions oncustomized search applications for enterprises for which theirrespective query logs are not large enough or non-existent. To address these design issues, we have developed PQS, aprobabilistic query suggestion module. Unlike its counterparts, PQS is not constrained by the existence of query logs, sinceit solely relies on the availability of user-generated contentfreely accessible online, such as the Wikipedia.org documentcollection, and applies simple, yet effective, probabilistic-andinformation retrieval-based models, i.e., the Multinomial, BigramLanguage, and Vector Space Models, to provide usefuland diverse query suggestions. Empirical studies conductedusing a set of test queries and the feedbacks provided byMechanical Turk appraisers have verified that PQS makesmore useful suggestions than Yahoo! and is almost as goodas Google and Bing based on the relatively small difference inperformance measures achieved by Google and Bing over PQS.

Finding books that children/teenagers are interested in these days is a non-trivial task due to the diversity of topics covered in huge volumes of books with varied readability levels. Even though K-12 readers can turn to book recommenders to look for books, the recommended books may not satisfy their personal needs, since they could be beyond/below their readability levels or fail to match their topics of interest. To address these problems, we introduce BReK12, a book recommender that makes personalized suggestions tailored to each K-12 user U based on books available on a social book-marking site that (i) are similar in content to the ones that are known to be of interest to U, (ii) have been bookmarked by users with reading patterns similar to U's, and (iii) can be comprehended by U. BReK12 is an asset to its users, since it suggests books that are appealing to its users and at grade levels that they can cope with, which can increase their reading selection choices and motivate them to read. We have also developed ReLAT, the readability analysis tool employed by BReK12 to determine the grade level of books. ReLAT is novel, compared with existing readability formulas, since it can predict the grade level of a book even if an excerpt of the book is not available. We have conducted empirical studies which have verified the accuracy of ReLAT in predicting the grade level of a book and the effectiveness of BReK12 over existing baseline recommendation systems.

Ideally, students in K-12 grade levels can turn to book recommenders to locate books that match their interests. Existing book recommenders, however, fail to take into account the readability levels of their users, and hence their recommendations may be unsuitable for the users. To address this issue, we introduce BReK12, a recommender that targets K-12 users and prioritizes the reading level of its users in suggesting books of interest. Empirical studies conducted using the Bookcrossing dataset show that BReK12 outperforms a number of existing recommenders (developed for general users) in identifying books appealing to K-12 users.

Researchers, as well as ordinary users who seek information in diverse academic fields, turn to the web to search for publications of interest. Even though scholarly publication recommenders have been developed to facilitate the task of discovering literature pertinent to their users, they (i) are not personalized enough to meet users' expectations, since they provide the same suggestions to users sharing similar profiles/preferences, (ii) generate recommendations pertaining to each user's general interests as opposed to the specific need of the user, and (iii) fail to take full advantages of valuable user-generated data at social websites that can enhance their performance. To address these problems, we propose PubRec, a recommender that suggests closely-related references to a particular publication P tailored to a specific user U, which minimizes the time and efforts imposed on U in browsing through general recommended publications. Empirical studies conducted using data extracted from CiteULike (i) verify the efficiency of the recommendation and ranking strategies adopted by PubRec and (ii) show that PubRec significantly outperforms other baseline recommenders.

Review websites, such as Epinions.com, which offer users a platform to share their opinions on diverse products and services, provide a valuable source of opinion-rich information. Browsing through archived reviews to locate different opinions on a product or service, however, is a time-consuming and tedious task, and in most cases, the large amount of available information is difficult for users to absorb. To facilitate the process of synthesizing opinions expressed in reviews on a product or service P specified in a user query/question Q, we introduce QMSS, a query-based multi-document sentiment summarizer. QMSS creates a summary for Q, which either reflects the general opinions on P or is tailored to specific facets (i.e., features) and/or sentiment of P as specified in Q. QMSS (i) identifies the facets addressed in reviews retrieved for Q, (ii) employs a sentence-based, sentiment classifier to determine the polarity of each sentence in each review, and (iii) clusters sentences in reviews according to the facets captured in the sentences, which are identified using a keyword-label extraction algorithm. This process dictates which sentences in the reviews should be included in the summary for Q. Empirical studies have verified that QMSS is highly effective in generating summaries that satisfy users' information needs and ranks on top among the state-of-the-art query-based multi-document sentiment summarizers

With the large amount of books available nowadays, users are overwhelmed with choices when they attempt to find books of interest. While existing book recommendation systems, which are based on either collaborative filtering, content-based, or hybrid methods, suggest books (among the millions available) that might be appealing to the users, their recommendations are not personalized enough to meet users' expectations due to their collective assumption on group preference and/or exact content matching, which is a failure. To address this problem, we have developed PReF, a Personalized Recommender that relies on Friendships established by user son a social website, such as Library Thing, to make book recommendations tailored to individual users. In selecting books to be recommended to a user U, who is interested in a book B, PReF (i) considers books belonged to U's friends, (ii) applies word-correlation factors to disclose books similar in contents to B, (iii) depends on the ratings given to books by U's friends to identify highly-regarded books, and (iv) determine show reliable individual friends of U are in providing books from their own catalogs (that are similar in content to B)to be recommended. We have conducted an empirical study and verified that (i) relying on data extracted from social websites improves the effectiveness of book recommenders and (ii) PReF outperforms the recommenders employed by Amazon and Library Thing.

Taking advantage of the Web, many advertisements (ads for short) websites, which aspire to increase client's transactions and thus profits, offer searching tools which allow users to (i) post keyword queries to capture their information needs or (ii) invoke form-based interfaces to create queries by selecting search options, such as a price range, filled-in entries, check boxes, or drop-down menus. These search mechanisms, however, are inadequate, since they cannot be used to specify a natural-language query with rich syntactic and semantic content, which can only be handled by a question answering (QA) system. Furthermore, existing ads websites are incapable of evaluating arbitrary Boolean queries or retrieving partially-matched answers that might be of interest to the user whenever a user's search yields only a few or no results at all. In solving these problems, we present a QA system for ads, called CQAds, which (i) allows users to post a natural-language questionQfor retrieving relevant ads, if they exist, (ii) identifies ads as answers that partially-match the requested information expressed inQ, if insufficient or no answers toQcan be retrieved, which are ordered using asimilarity-rankingapproach, and (iii) analyzes incomplete or ambiguous questions to perform the "best guess" in retrieving answers that "best match" the selection criteria specified inQ. CQAds is also equipped with a Boolean model to evaluate Boolean operators that are eitherexplicitlyorimplicitlyspecified inQ, i.e., with or without Boolean operators specified by the users, respectively. CQAds is easy to use, scalable to all ads domains, and more powerful than search tools provided by existing ads websites, since its query-processing strategy retrieves relevant ads of higher quality and quantity. We have verified the accuracy of CQAds in retrieving ads on eight ads domains and compared its ranking strategy with other well-known ranking approaches.

Plagiarism is a serious problem that infringes copyrighted documents/materials, which is an unethical practice and decreases the economic incentive received by their legal owners. Unfortunately, plagiarism is getting worse due to the increasing numbe

Text classification categories Web documents in large collections into predefined classes based on their contents. Unfortunately, the classification process can be time-consuming and users are still required to spend considerable amount of time scanning through the classified Web documents to identify the ones that satisfy their information needs. In solving this problem, we first introduce CorSum, an extractive single-document summarization approach, which is simple and effective in performing the summarization task, since it only relies on word similarity to generate high-quality summaries. Hereafter, we train a Naive Bayes classifier on CorSum-generated summaries and verify the classification accuracy using the summaries and the speed-up during the process. Experimental results on the DUC-2002 and 20 Newsgroups datasets show that CorSum outperforms other extractive summarization methods, and classification time is significantly reduced using CorSum-generated summaries with compatible accuracy. More importantly, browsing summaries, instead of entire documents, classified to topic-oriented categories facilitates the information searching process on the Web.

Abstract Libraries, private and public, offer valuable resources to library patrons. As of today, the only way to locate information archived exclusively in libraries is through their catalogs. Library patrons, however, often find it difficult to formulate a proper query, which requires using specific keywords assigned to different fields of desired library catalog records, to obtain relevant results. These improperly formulated queries often yield irrelevant results or no results at all. This negative experience in dealing with existing library systems turns library patrons away from directly querying library catalogs; instead, they rely on Web search engines to perform their searches first, and upon obtaining the initial information (e.g., titles, subject headings, or authors) on the desired library materials, they query library catalogs. This searching strategy is an evidence of failure of today's library systems. In solving this problem, we propose an enhanced library system, which allows partial, similarity matching of (a) tags defined by ordinary users at a folksonomy site that describe the content of books and (b) unrestricted keywords specified by an ordinary library patron in a query to search for relevant library catalog records. The proposed library system allows patrons posting a query Q using commonly used words and ranks the retrieved results according to their degrees of resemblance with Q while maintaining the query processing time comparable with that achieved by current library search engines.

Abstract E‐mail messages are unquestionably one of the most popular communication media these days. Not only are they fast and reliable but also free in general. Unfortunately, a significant number of e‐mail messages received by e‐mail users on a daily basis are spam. This fact is annoying since spam messages translate into a waste of the user's time in reviewing and deleting them. In addition, spam messages consume resources such as storage, bandwidth, and computer‐processing time. Many attempts have been made in the past to eradicate spam; however, none has proven highly effective. In this article, we propose a spam e‐mail detection approach, called SpamED, which uses the similarity of phrases in messages to detect spam. Conducted experiments not only verify that SpamED using trigrams in e‐mail messages is capable of minimizing false positives and false negatives in spam detection but it also outperforms a number of existing e‐mail filtering approaches with a 96% accuracy rate.

Plagiarism is a serious problem that infringes copyrighted documents/materials, which is an unethical practice and decreases the economic incentive received by authors (owners) of the original copies. Unfortunately, plagiarism is getting worse due to the increasing number of on-line publications on the Web, which facilitates locating and paraphrasing information. In solving this problem, we propose a novel plagiarism-detection method, called SimPaD, which (i) establishes the degree of resemblance between any two documents D1and D2based on their sentence-to-sentence similarity computed by using pre-defined word-correlation factors, and (ii) generates agraphical view of sentences that are similar (or the same) in D1and D2. Experimental results verify that SimPaD is highly accurate in detecting (non-) plagiarized documents and outperforms existing plagiarism-detection approaches.

Emails are one of the most commonly used modern communication media these days; however, unsolicited emails obstruct this otherwise fast and convenient technology for information exchange and jeopardize the continuity of this popular communication tool. Waste of valuable resources and time and exposure to offensive content are only a few of the problems that arise as a result of junk emails. In addition, the monetary cost of processing junk emails reaches billions of dollars per year and is absorbed by public users and Internet service providers. Even though there has been extensive work in the past dedicated to eradicate junk emails, none of the existing junk email detection approaches has been highly successful in solving these problems, since spammers have been able to infiltrate existing detection techniques. In this paper, we present a new tool, JunEX, which relies on the content similarity of emails to eradicate junk emails. JunEX compares each incoming email to a core of emails marked as junk by each individual user to identify unwanted emails while reducing the number of legitimate emails treated as junk, which is critical. Conducted experiments on JunEX verify its high accuracy.