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3D droplet-based bioprinting technology is an innovative and time-saving method to precisely generate cell laden 3D structures for multiple clinical and research applications. It is important that the printed droplet must impact so as to leave a single isolated drop, for high printing resolution and accuracy. Therefore, understanding the criteria that control spreading behaviour and prevent droplet splashing is of great importance in optimizing the printing performance. In this experimental work, the physics of the impact of bioink droplets on a pre-printed soft hydrogel matrix was investigated. The droplet size, velocity, and input cell density were varied to generate a range of droplet impact behaviours. It has been shown that the soft substrate inhibited the droplet spreading after impact. The deposition/splashing boundary on the dry/wet flat surface (Nunclon $$^{\text{TM}}$$ TM Delta surface-treated plastic) was defined by $$K = {\text{We}}^{0.5} {\text{Re}} ^{0.25} = 86.19$$ K = We 0.5 Re 0.25 = 86.19 . The splashing threshold on the soft hydrogel matrix was defined by $$K = 44.78$$ K = 44.78 and $$L = {\text{We}} {\text{Re}}^{-0.4} = 13.85$$ L = WeRe - 0.4 = 13.85 for both blank and cell-laden inks on dry/wet soft substrates. Beyond this threshold, the printed droplet volume will be much lower than the expected volume due to splashing, leading to poor printing performance. The absolute splashing threshold on the dry/wet soft hydrogel matrix was defined by $$K = 73.41$$ K = 73.41 and $$L = 27.36$$ L = 27.36 . The printed 3D cell-laden structures were also presented to illustrate how the impact behaviours influence the possible printing fidelity and structure integrity.

This Viewpoint examines the future role of the Administration for Strategic Preparedness and Response in light of the COVID-19 pandemic.

The Consumer Operated and Oriented Plans (CO-OPs), the subject of Section 1322 of the Affordable Care Act (ACA), were to constitute "qualified nonprofit health insurance issuers." Designed with an eye toward increasing competition with the extant commercial and nonprofit insurance sector, the CO-OPs were to enhance consumer choice as well as hold down prices on the state and federal exchanges. To achieve these ends, the consumer-governed state-licensed CO-OPs were to target the individual and small-group markets. At least one qualified CO-OP was to be established in each and every state. By the fall of 2013, however, coincident with the first open enrollment period of the ACA, only 23 CO-OPs were on tap. At the time of this writing, only three of these CO-OPs remain operational in the states of Maine, Montana, and Wisconsin. Viewed in hindsight, the thorough dissolution of the CO-OPs was the product of incremental financial privation effectuated by congressional opponents of the ACA. In this Commentary, we revisit the ontogeny of the CO-OP construct, review its partisan dismantling, and explore the potential resurrection thereof.

Three-dimensional (3D) bioprinting offers great potential for the fabrication of complex 3D cell-laden constructs for clinical and research applications. The droplet formation process is the important first step in droplet-based 3D bioprinting, affecting the positional accuracy and printing fidelity. In this paper, the drop ejection behavior, thresholds for stable droplet generation, and formation of satellite drops are studied, under various ink properties, printing conditions, and input cell concentrations using a micro-valve-based 3D bioprinter. Three droplet ejection behaviors are identified under different conditions: an isolated stable droplet, satellites coalescing into a single droplet, and the presence of one/multiple satellites. The droplet state is represented by a phase diagram bounded by a dimensionless Z number (the inverse of the Ohnesorge number) and a jet Weber number, Wej, to define the printability of the utilized bioprinter. The printability range is defined as 2 j < 25 by considering characteristics, such as stable single droplet formability and sufficient drop falling velocity. There is no fatal damage on cells within this printability range. The results show there is no strong influence of an actuation system on droplet-based bioprinting printability. As the input cell concentration increases, the bioink's density and viscosity increases, and surface tension decreases, which, in turn, causes the Z number to slightly decrease. The change in the cell concentration (from 0 to [Formula: see text] cells/ml), within a Newtonian bioink, has negligible impact on the droplet volume, falling velocity, drop ejection behavior, breakup time, and ligament length in microvalve-based bioprinting.

SummaryUnderstanding the underlying mechanisms of migration and metastasis is a key focus of cancer research. There is an urgent need to developin vitro3D tumor models that can mimic physiological cell-cell and cell-extracellular matrix interactions, with high reproducibility and that are suitable for high throughput (HTP) drug screening. Here, we developed a HTP 3D bioprinted migration model using a bespoke drop-on-demand bioprinting platform. This HTP platform coupled with tunable hydrogel systems enables (i) the rapid encapsulation of cancer cells withinin vivotumor mimicking matrices, (ii)in situand real-time measurement of cell movement, (iii) detailed molecular analysis for the study of mechanisms underlying cell migration and invasion, and (iv) the identification of novel therapeutic options. This work demonstrates that this HTP 3D bioprinted cell migration platform has broad applications across quantitative cell and cancer biology as well as drug screening.

Congressional and presidential records reveal a consistent pattern of political intercession with the regulatory authority of the Food and Drug Administration (FDA) over the approval and labeling of mifepristone (RU-486). This pattern is unlikely to abate any time soon. It is against this backdrop that we examine herein the ongoing legislative and legal disputes over mifepristone at a point in time which is just beyond the 20th anniversary of its approval by the FDA "for the medical termination of intrauterine pregnancy."

In vitro three-dimensional (3D) cell models have been accepted to better recapitulate aspects of in vivo organ environment than 2D cell culture. Currently, the production of these complex in vitro 3D cell models with multiple cell types and microenvironments remains challenging and prone to human error. Here we report a versatile bioink comprised of a 4-arm PEG based polymer with distal maleimide derivatives as the main ink component and a bis-thiol species as the activator that crosslinks the polymer to form the hydrogel in less than a second. The rapid gelation makes the polymer system compatible with 3D bioprinting. The ink is combined with a drop-on-demand 3D bioprinting platform consisting of eight independently addressable nozzles and high-throughput printing logic for creating complex 3D cell culture models. The combination of multiple nozzles and fast printing logic enables the rapid preparation of many complex 3D structures comprising multiple hydrogel environments in the one structure in a standard 96-well plate format. The platform compatibility for biological applications was validated using pancreatic ductal adenocarcinoma cancer (PDAC) cells with their phenotypic responses controlled by tuning the hydrogel microenvironment.

3D cell bioprinting is an innovative and time-saving additive manufacturing technology; it precisely generates complex cell-laden constructs to overcome the limitations of 2D cell culture and conventional tissue engineering scaffold technology. Many efforts have been made to evaluate the bioprinter performance by considering printed cell number and the consistency of printed cell number. In this paper, a modified droplet imaging system is used to study the printing performance for a micro-valve-based 3D bioprinter using fluorescence MCF-7 cells. The effects of droplet dispensing physics (dosing energy [Formula: see text]), ink properties (Z number—the inverse of the Ohnesorge number and particle sedimentation velocity), and input cell concentration are considered. The droplet imaging system demonstrates a strong capability and accuracy in analyzing bioprinting performance for printed cell density less than 300 cells/droplet. The average printed cell number is positively correlated with the increasing input cell concentration, dosing energy, and printing time. Printing ink, with Z number ranging from 4 to 7.41 and cell sedimentation velocity at [Formula: see text] m/s, can provide the estimated printed cell number and consistent cell printing results within 2 min printing time. Printing inks with higher Z number or cell sedimentation velocity should be ejected under dosing energy below 2.1 La and printed right after filling the reservoir to achieve reliable and stable printing results.

Since the last decade, data collection is becoming more pervasive, passive and easier to perform. This is resulting in the rise of data wherein a user performs some activities in a sequence, such as locations visited, physical activities performed, and modes of transport taken. In such cases, activities are often performed in a particular order, and each activity in turn may influence the subsequent activities to be performed. Moreover, such activities may be associated with multiple features or contexts, such as location, time, weather, etc. The order encoded in such data, along with the context, capture important information when it comes to modelling the preferences and personal habits of users. Traditional recommender systems, however, typically do not consider the order in which users perform activities and there is little work which considers both sequence and context simultaneously. In this work, a generic recommendation framework is proposed which leverages both sequences and context in user activity data for activity recommendation. To model user activities, a semantic view of the user’s past activities as a timeline of activity objects is presented. An essential step in the recommendation process is finding patterns in past activities performed which are closely aligned to the recent activities undertaken by the user. To calculate the distance between timelines, a novel two-level distance metric is presented which calculates distance with respect to the order of the activities as well as the context features associated with each activity occurrence. The efficacy of the proposed activity recommendation framework in various recommendation scenarios, is demonstrated using real-world datasets from multiple domains.

3D bioprinting is an innovative and time-saving method to precisely generate cell-laden 3D structures for clinical and research applications. Ejected cell number and cell distribution are two key technical parameters for evaluation of the bioprinter performance. In this paper, a modified droplet imaging system is used to study cell-size fluorescent particle number and distribution within droplets ejected from a microvalve-based 3D bioprinter. The effects of droplet dispensing physics (dosing energy Ed), ink properties (Z number—the inverse of the Ohnesorge number and particle sedimentation velocity), and input particle concentration are considered. The droplet imaging system demonstrates a strong capability in analyzing bioprinting performance for seeded concentrations less than 3×106 particles/ml. The printed particle number increases near-linearly under increasing dosing energy and Z number. It was found that for 7

Starting their academic career can be overwhelming for many young people. Students are often presented with a variety of options within their programmes of study and making appropriate and informed decisions can be a challenge. Compared to many other areas in our everyday life, recommender systems remain underused in the academic setting. In this part of our research we use non-negative matrix factorisation to identify dependencies between modules, visualise sequential recommendations, and bring structure and clarity into the academic module space.

The e-commerce boom has shifted the real estate market to an online medium whereby realtors and owners can post their property listings. A recent focus of the research community has been studying various aspects of these online property portals. We perform a textual analytics study for a popular Irish portal with Dublin being our case-study on account of its severe housing crisis and heterogeneous regions. We extract various textual features from within the property advertisements and analyse their correlations with the deprivation index of the location in which the property is located thereby attempting to understand how affluence of a location influences the way in which advertisements are framed, and thereby aiding the user towards the interpretation of property advertisements. The correlation analysis reveals interesting outcomes depicting tendency of realtors/owners to over emphasize location aspects of a property for affluent locations.

Recommender systems are omni-present in our every day lives, guiding us through the vast amount of information available. However, in the academic world, personalised recommendations are less prominent, leaving students to navigate through the typically large space of available courses and modules manually. Since it is crucial for students to make informed choices about their learning pathways, we aim to improve the way students discover elective modules by developing a hybrid recommender system prototype that is specifically designed to help students find elective modules from a diverse set of subjects. We can improve the discoverability of long-tail options and help students broaden their horizons by combining notions of similarity and diversity.

Personalised recommendations feature prominently in many aspects of our lives, from the movies we watch, to the news we read, and even the people we date. However, one area that is still relatively underdeveloped is the educational sector where recommender systems have the potential to help students to make informed choices about their learning pathways. We aim to improve the way students discover elective modules by using a hybrid recommender system that is specifically designed to help students to better explore available options. By combining notions of content-based similarity and diversity, based on structural information about the space of modules, we can improve the discoverability of long-tail options that may uniquely suit students’ preferences and aspirations.

In the world of recommender systems, so-called content-based methods are an important approach that rely on the availability of detailed product or item descriptions to drive the recommendation process. For example, recommendations can be generated for a target user by selecting unseen products that are similar to the products that the target user has liked or purchased in the past. To do this, content-based methods must be able to compute the similarity between pairs of products (unseen products and liked products, for example) and typically this is achieved by comparing product features or other descriptive elements. The approach works well when product descriptions are readily available and when they are detailed enough to afford an effective similarity comparison. But this is not always the case. Detailed product descriptions may not be available since they can be expensive to create and maintain. In this article we consider another source of product descriptions in the form of the user-generated reviews that frequently accompany products on the web. We ask whether it is possible to mine these reviews, unstructured and noisy as they are, to produce useful product descriptions that can be used in a recommendation system. In particular we describe a novel approach to product recommendation that harnesses not only the features that can be mined from user-generated reviews but also the expressions of sentiment that are associated with these features. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers, and we demonstrate the practical benefits of this approach across a variety of Amazon product domains. Science Foundation Ireland

This paper demonstrates a Web service called TweetCric to uncover cricket insights from Twitter with the aim of facilitating sports analysts and journalists. It essentially arranges crowdsourced Twitter data about a team in comprehensive visualizations by incorporating domain-specific approaches to sentiment analysis.

In this paper, we propose a new evaluation measure to assess the quality of a hierarchy in supporting search queries to content collections. The evaluation measure models the scenario of a searcher seeking a particular target item in the hierarchy. It takes into account the structure of the hierarchy by measuring the cognitive challenge of determining the correct path in the hierarchy as well as the reduction in search time afforded by hierarchy. The goal is to propose a general-purpose measure that can be applied in different application contexts, allowing different hierarchical arrangements of content to be quantitatively assessed.

Real-time web (RTW) services such as Twitter allow users to express their opinions and interests, often expressed in the form of short text messages providing abbreviated and highly personalized commentary in real-time. Although this RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research, it can contain useful consumer reviews on products, services and brands. This paper describes how Twitter-like short-form messages can be leveraged as a source of indexing and retrieval information for product recommendation. In particular, we describe how users and products can be represented from the terms used in their associated reviews. An evaluation performed on four different product datasets from the Blippr service shows the potential of this type of recommendation knowledge, and the experiments show that our proposed approach outperforms a more traditional collaborative-filtering based approach. Science Foundation Ireland

Collaborative web search utilises past search histories in a community of like-minded users to improve the quality of search results. Search results that have been selected by community members for past queries are promoted in response to similar queries that occur in the future. The I-SPY system is one example of such a collaborative approach to search. As is the case with all open systems, however, it is difficult to establish the integrity of those who access a system and thus the potential for malicious attack exists. In this paper we investigate the robustness of the I-SPY system to attack. In particular, we consider attack scenarios whereby malicious agents seek to promote particular result pages within a community. In addition, we analyse robustness in the context of community homogeneity, and we show that this key characteristic of communities has implications for system robustness.

A work highlights the lack of robustness collaborative recommender systems exhibit against attack. This vulnerability can lead to significantly biased recommendations for target items. Here, we examine such attacks from a cost perspective, focusing on how attack size - that is, the number of ratings inserted - affects attack success. We introduce a framework for quantifying the gains attackers realize, taking into account the financial cost of mounting the attack. A cost-benefit analysis of third-party attacks on recommender systems shows that attackers realize profits even when incurring costs associated with rating insertions.

Modern web search engines have come to dominate how millions of people find the information that they are looking for online. While the sheer scale and success of the leading search engines is a testimony to the scientific and engineering progress that has been made over the last two decades, mainstream search is not without its challenges. Mainstream search engines continue to provide a largely one-size-fits-all service to their user-base, ultimately limiting the relevance of their result-lists. And they have only very recently begun to consider how the rise of the social web may support novel approaches to search and discovery, or how such signals can be used to inform relevance. In this chapter we will explore recent research which aims to do just that: to make web search a more personal and collaborative experience and to leverage important information such as the reputation of searchers during result-ranking. In short we look towards a more social future for mainstream search. Science Foundation Ireland Insight Centre for Data Analytics

User-generated reviews are now plentiful online and they have proven to be a valuable source of real user opinions and real user experiences. In this chapter we consider recent work that seeks to extract topics, opinions, and sentiment from review text that is unstructured and often noisy. We describe and evaluate a number of practical case-studies for how such information can be used in an information filtering and recommendation context, from filtering helpful reviews to recommending useful products.

The presence of direct current (DC) injury currents in ischemic tissue is an important diagnostic indicator of pathophysiology in cortical spreading depression and particularly in myocardial infarction. To date, no measurements of DC injury currents in the alimentary tract have been reported. We used a SQUID magnetometer to measure changes in the baseline of the magnetic field of intestinal electrical activity during induced segmental ischemia. We computed the magnetic field DC baseline by subtracting sequential recordings made while the bowel segment was first directly beneath the SQUID and then pulled away. We observed a significant baseline decrease of 38% +/- 4% in experimental animals, while the control group decreased by only 1% +/- 6%. This magnetic field baseline decrease is consistent with the flow of injury currents between normally perfused and hypoxic tissue regions. This study is the first report of DC injury currents in ischemic smooth muscle of the alimentary tract.

Collaborative recommendation has emerged as an effective technique for personalized information access. However, there has been relatively little theoretical analysis of the conditions under which the technique is effective. To explore this issue, we analyse the robustness of collaborative recommendation: the ability to make recommendations despite (possibly intentional) noisy product ratings. There are two aspects to robustness: recommendation accuracy and stability. We formalize recommendation accuracy in machine learning terms and develop theoretically justified models of accuracy. In addition, we present a framework to examine recommendation stability in the context of a widely-used collaborative filtering algorithm. For each case, we evaluate our analysis using several real-world data-sets. Our investigation is both practically relevant for enterprises wondering whether collaborative recommendation leaves their marketing operations open to attack, and theoretically interesting for the light it sheds on a comprehensive theory of collaborative recommendation.

A national Salmonella control program in the pork industry was enacted in Ireland in August 2002. This study was undertaken as part of a larger project investigating the role of pork as a source of human salmonellosis in Ireland. The objective of this survey was to assess the prevalence of Salmonella in Irish pork sausage at retail level. Samples, comprising branded prepacked sausages and loose sausages from supermarket meat counters and butcher shops, were collected from selected retail sites in four cities from October to December 2001 and from June to August 2002. A three-tube most-probable-number method was used to enumerate Salmonella in a selected number of samples that were positive by enrichment. Salmonella serotypes were detected in 4.4 and 1.7% of samples at each of the respective sampling periods, a level similar to those reported in other U.S. and U.K. studies. Isolates were characterized by serotype, phage type, and antimicrobial susceptibility. Eighteen (70%) were resistant to at least one antimicrobial, and 15 (58%) were resistant to four or more antimicrobials. Most of the isolates exhibited resistance to tetracycline. Five different phage types were detected. DT104 was the predominant phage type among Salmonella Typhimurium isolates. This study revealed that multidrug-resistant salmonellae are present in a proportion of Irish sausages and that further risk analysis work is necessary in order to quantify the risk posed to public health.

Personalisation features are key to the success of many web applications and collaborative recommender systems have been widely implemented. These systems assist users in finding relevant information or products from the vast quantities that are frequently available. In previous work, we have demonstrated that such systems are vulnerable to attack and that recommendations can be manipulated. We introduced the concept of robustness as a performance measure, which is defined as the ability of a system to provide consistent predictions in the presence of noise in the data. In this paper, we expand on our previous work by examining the effects of several neighbourhood formation schemes and similarity measures on system performance. We propose a neighbourhood filtering mechanism for filtering false profiles from the neighbourhood in order to improve the robustness of the system.

With the increasing availability of passive, wearable sensor devices, digital lifelogs can now be captured for individuals. Lifelogs contain a digital trace of a person’s life, and are characterised by large quantities of rich contextual data. In this paper, we propose a content-based recommender sys- tem to leverage such lifelogs to suggest activities to users. We model lifelogs as timelines of chronological sequences of activity objects, and describe a recommendation framework in which a two-level distance metric is proposed to measure the similarity between current and past timelines. An ini- tial evaluation of our activity recommender performed using a real-world lifelog dataset demonstrates the utility of our approach.

The basic legal framework covering government information procurement, production, and dissemination has been in place for over 100 years. Congress is currently developing revisions to Title 44 of the United State Code in order to reform this system. Fundamental principles of public access to government information, embodied in the Federal Depository Library Program (FDLP), must guide these revisions. The strategic planning process, currently underway by federal agencies, must give due consideration to the entire life cycle of government information, particularly the dissemination and public access responsibilities.

This paper describes a novel approach to product recommendation that is based on opinionated product descriptions that are automatically mined from user-generated product reviews. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers. We demonstrate the benefits of this approach across a variety of Amazon product domains.

Information streams allow social network users to receive and interact with the latest messages from friends and followers. But as our social graphs grow and mature it becomes increasingly difficult to deal with the information overload that these realtime streams introduce. Some social networks, like Facebook, use proprietary interestingness metrics to rank messages in an effort to improve stream relevance and drive engagement. In this paper we evaluate learning to rank approaches to rank content based on a variety of features taken from live-user data.

Real-time information streams such as Twitter have become a common way for users to discover new information. For most users this means curating a set of other users to follow. However, at the moment the following granularity of Twitter is restricted to the level of individual users. Our research has highlighted that many following relationships are motivated by a subset of interests that are shared by the users in question. For example, user A might follow user B because of their technology related tweets, but shares little or no interest in their other tweets. As a result, this all-or-nothing following relationship can quickly overwhelm users' timelines with extraneous information. To improve this situation we propose a user profiling approach based on the topical categorisation of users' posted URLs. These topics can then be used to filter information streams so that they focus on more relevant information from the people they follow, based on their core interests. In particular, we present a system called CatStream that provides for a more fine-grained way to follow users on specific topics and filter our timelines accordingly. We present the results of a live-user study that shows how filtered timelines offer a better way to organise and filter their information streams. Most importantly users are generally satisfied with the categories predicted for their profiles and tweets.

Supplementing product information with user-generated content such as ratings and reviews can help to convert browsers into buyers. As a result this type of content is now front and centre for many major e-commerce sites such as Amazon. We believe that this type of content can provide a rich source of valuable information that is useful for a variety of purposes. In this work we are interested in harnessing past reviews to support the writing of new useful reviews, especially for novice contributors. We describe how automatic topic extraction and sentiment analysis can be used to mine valuable information from user-generated reviews, to make useful suggestions to users at review writing time about features that they may wish to cover in their own reviews. We describe the results of a live-user trial to show how the resulting system is capable of delivering high quality reviews that are comparable to the best that sites like Amazon have to offer in terms of information content and helpfulness.

In this paper we describe a novel approach to case-based product recommendation. It is novel because it does not leverage the usual static, feature-based, purely similarity-driven approaches of traditional case-based recommenders. Instead we harness experiential cases, which are automatically mined from user generated reviews, and we use these as the basis for a form of recommendation that emphasises similarity and sentiment. We test our approach in a realistic product recommendation setting by using live-product data and user reviews.

As the Federal Depository Library Program moves toward the increased utilization of electronic technologies to meet the government information needs of the public, libraries must plan how to best take advantage of local, state, and regional resources to make this transition successful. This paper suggests that depository libraries should begin immediately a planning process to develop state or regional service strategies for electronic government information and outlines the steps to develop such plans.

The nature of water in copolymeric N‐vinyl pyrrolidone/methyl methacrylate hydrogels has been investigated using dielectric thermal analysis (DETA) and differential scanning calorimetry (DSC). Dielectric tan δ measurements are reported over a frequency range 500 Hz‐20 kHz and a temperature range of −140 to +20°C. The observed complex relaxations were attributed to the mobility of water and the relaxation processes of the copolymer matrix. In addition DSC was used to measure the melting endotherm of water in gels partially swollen to a pseudoequilibrium. The resultant data were fitted to a two‐phase approximation model. © 1995 John Wiley & Sons, Inc.

User generated reviews are now a familiar and valuable part of most e-commerce sites since high quality reviews are known to influence purchasing decisions. In this demonstration we describe work on the Reviewer's Assistant (RA), which is a recommendation system that is designed to help users to write better quality reviews. It does this by suggesting relevant topics that they may wish to discuss based on the product they are reviewing and the content of their review so far.

User opinions and reviews are an important part of the modern web and all major e-commerce sites typically provide their users with the ability to provide and access customer reviews across their product catalog. The importance of reviews has driven the need to improve the review quality by providing interactive support for the reviewer and we will demonstrate the first version of an Intelligent Reviewer's Assistant for this purpose. Our browser plugin is designed to work with major sites like Amazon and to provide users with suggestions as they write their reviews. In particular, these suggestions take the form of topics (e.g. product features) that a reviewer may wish to write about and the suggestions automatically adapt as the user writes their review.

User opinions and reviews are an important part of the modern web and all major e-commerce sites typically provide their users with the ability to provide and access customer reviews across their product catalog. Indeed this has become a vital part of the service provided by sites like Amazon and TripAdvisor, so much so that many of us will routinely check appropriate product reviews before making a purchase decision, regardless of whether we intend to purchase online or not. The importance of reviews has highlighted the need to help users to produce better reviews and in this paper we describe the development and evaluation of a Reviewer's Assistant for this purpose. We describe a browser plugin that is designed to work with major sites like Amazon and to provide users with suggestions as they write their reviews. These suggestions take the form of topics (e.g. product features) that a reviewer may wish to write about and the suggestions automatically adapt as the user writes their review. We describe and evaluate a number of different algorithms to identify useful topics to recommend to the user and go on to describe the results of a preliminary live-user trial.

There is no doubting the incredible impact of Twitter on how we communicate, access and share information online. Currently users can follow other users or hashtags in order to benefit from a stream of data from people they trust or on topics that matter to them. However at the moment the following granularity of Twitter means that users cannot limit their information streams to a set of topics by a given user. Thus, even the most carefully curated information streams can quickly become polluted with extraneous content. In this paper we describe our initial steps to improve this situation by proposing a profiling approach that can be used for information filtering purposes as well as recommendation purposes. First, we demonstrate that it is feasible to automatically profile the interests of users by using machine learning techniques to classify the pages that they share via their tweets. We then go on to describe how this profiling mechanism can be used to organise and filter Twitter information streams. In particular we present a system that provides for a more fine-grained way to follow users on specific topics and thereby refine the standard Twitter timeline based on a user’s core topical interests.

Today, online reviews for products and services have become an important class of user-generated content and they play a valuable role for countless online businesses by helping to convert casual browsers into informed and satisfied buyers. In many respects, the content of user reviews is every bit as important as the catalog content that describes a given product or service. As users gravitate towards sites that offer insightful and objective reviews, the ability to source helpful reviews from a community of users is increasingly important. In this work we describe the Reviewer’s Assistant, a case-based reasoning inspired recommender system designed to help people to write more helpful reviews on sites such as Amazon and TripAdvisor. In particular, we describe two approaches to helping users during the review writing process and evaluate each as part of a blind live-user study. Our results point to high levels of user satisfaction and improved review quality compared to a control-set of Amazon reviews.

In this paper we describe an initial attempt to build multi-faceted user models from raw Twitter data. The key contribution is to describe a technique for categorising users and their social ties according to a collection of curated topical categories and in this way resolve much of the preference noise that is inherent within user conversations. We go on to analyse and evaluate this approach on a data set of over 240,000 Twitter users and discuss the applications of these novel user models.