ABSTRACT. Scope - Personalised Information Retrieval (PIR) has been gaining attention because it investigates intelligent ways for enhancing content delivery. Web users can have personalised services and more accurate information. Problem - Several PIR systems have been proposed in the literature; however, they have not been properly tested or evaluated. Proposal – The authors propose a generally applicable web-based interface, which provides PIR developers and evaluators with: i) implicit recommendations on how to evaluate a specific PIR system; ii) a repository containing studies on user-centred and layered evaluation studies; iii) recommendations on how to best combine different evaluation methods, metrics and measurement criteria in order to most effectively evaluate their system; iv) a UCE methodology which details how to apply existing UCE techniques; v) a taxonomy of evaluations of adaptive systems; and vi) interface translation support (49 languages supported).
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In the intervention group, the questionnaire about distress and problems served as a self-screening tool for physical and psychosocial problems. By using WINS, patients could obtain personalised information about reported problems. Self-screening was performed by the online version of the Dutch Distress Thermometer (DT) and Problem List (PL). Immediately after completion of the DT and PL. Patients received online information on the physical and psychosocial problems they reported on the digital PL. This information comprises: a) descrip- tion and background information of their reported problem b) advice on how to cope with the problem (self-help) c) what health care professional could be con- sulted when self-help insufficiently alleviates problems. On the WINS, patients could also find general informa- tion about the disease, read about the experiences of other NET patients and find links to other relevant websites. Any time, patients could send an e-mail with a question or request a telephone consultation with the investigators (physicians experienced in treating NET patients) in case of questions, problems or request further referral ’ .
Social media such as blog, Flickr, Youtube, Facebook, and Twitter has arisen as the major user-generated media platform in recent years. A blog is a simple web page consisting of brief paragraphs of opinion, information, personal diary entries, or links. People express their opinions, ideas, experiences, thoughts, wishes through these free-form writings. A typical blog post can combine text, images, and links to other blogs, web pages, and other media related to its topic. The individual users show their interest in online opinions about products or services. They share their brand experiences and opinions, positive or negative, regarding any product or service. The vendors of these items are increasingly coming to realize that these consumer voices can potentially wield enormous influence in shaping the opinions of other consumers and they are paying more and more attention to these issues . Currently, many sentiment analysis works are focus on product reviews or movie review , , ,  on blogs, customer review sites, and Web Pages. The opinion mining and sentiment analysis such as customer opinion summarisation  and sentiment analysis of user reviews  are possibly as augmentations to recommendation systems , since it might behoove such a system not to recommend items that receive a lot of negative feedback. The researchers Joshi and Belsare  developed a blog mining program called BlogHarvest, which searches for, and extracts, a blogger’s interests in order to recommend blogs with similar topics. The program uses classification, links, topic similarity clustering and tagging based on opinion
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same issues as KDDM, they can also benefit from such a consolidated process model. Steps 1 to 3 involve selecting, defining and preparing the raw data sources in terms of the domain, as well as the specification and encoding of domain expertise. There is already much research being conducted in the areas of data integration and automatic semantic mapping which may feed into these steps in future. SARA has its focus on personalized user exploration that leverages domain expertise, thus it was necessary to change step 4 of their generic process model (the selection of a specific data mining technique) to personalized exploration. Though incorporating all 6 steps of the process model, this fourth step is where the innovation in SARA is focused. Steps 5 and 6 of the process model, which evaluate the results and present them to the end user are outsourced by SARA to its client applications, and are supported through SARA’s API. There is a lot of research being conducted in the area of information visualization and exploratory search that client applications will be able to incorporate. Figure 1 highlights how this six step process is applied to SARA.
In terms of personalisation, IR and AH systems have both increasingly investigated the concept of facet preferences in order to provide more suitable rankings for a particular user. User involvement is still the most valuable input for IR systems in order to infer precise and context-sensitive relevance scores. AH techniques make use of user models to store a user’s prior knowledge and additional preferences in order to predict appropriate information relevance. Although this addition would prove very useful in IR systems as well, it constitutes a great challenge to mine such information using standard data mining techniques. In order to retrieve this prior knowledge information from open web data, one would first need to map users’ page visits to a domain model and consequently estimate a user’s information gain about the appropriate concept. This problem represents a fundamental issue in implicit feedback techniques, as a user’s browsing activity does not necessarily correspond to learning about the documents’ topics. Different approaches can be applied to address this problem. For example, one possibility is to assign suitably low value-adds for page visits in order to avoid overestimating a user’s experience. Alternatively, rather than looking at a user’s search or browsing sessions (page counts, word counts), another approach could employ public user profile mining. Once a user has been identified, user profile data from social networking sites such as Facebook or Twitter could be mined and consequently utilised within the personalised application (Abel et al., 2011).
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According to Jameson (2008), personalisation techniques include Recommender Systems (RS), learning personal assistant, adaptation to situational impairments, ability-based user interfaces (i.e. exploratory search) and algorithm-based approaches (i.e. personalised search). Once the users' interests have been gathered, these techniques might be employed to exploit this feedback. Due to its nature, this work focuses on personalised search (James et al. 2002) and neglects other paradigms. However, content-based filtering, although it is a part of RS, will be briefly included (see Section 22.214.171.124) as it is an essential component of the current approach. For more details related to recommender systems, the reader is referred to Pazzani and Billsus (2007). The difference between those approaches focusing on interaction between user and system and personalised search can be made clear through search mechanisms. For instance, James et al. (2002) defined two personalised searching techniques: their first approach is based on query augmentation. Here, the system expands the users' queries by considering the context of their searches (see Section 3.1.5). Their second approach is based on result processing. Here, the search result is analysed and modified further to better reflect the users' contexts based upon the augmented queries.
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AmbieSense addresses ambient, personalised, and context-sensitive information systems for mobile users. The overall goal of such systems is to help achieve the digital, ambient environments that make user’s information-related tasks easier by adapting to user’s context and personal requirements. Our approach to solve this is illustrated in Figure 1 below. The figure illustrates the AmbieSense reference architecture at an overall level. It can be used to build various digital information channels for mobile users. The objective is to provide the correct information to the right situation of the mobile user. The figure depicts three central cornerstones of the system: Content Service Providers (CSP), context tags and mobile/travelling users.
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Have the notifications had an effect on the TM or SMM of newcomers? The TM and SMM of newcomers were examined using the first and second newcomers’ questionnaires. Similarly to oldtimers, newcomers had to identify three other members from the VC who: may have similar research interests to them (Q8), may read similar resources to them (Q9), and may upload similar resources to them (Q10). Following the method described in Section 7.1.3, the data from both questionnaires was compared and statistically analysed. Wilcoxon non-parametric test was applied on the data collected for Q8, the results show a small difference with p = 0.024 (Table 6) between the two questionnaires. This can be due to newcomers’ interests extracted according to what they had provided as interests and thus TM or SMM was easier to be captured through this data. In terms of Q9 nothing could be extracted from the community model for the newcomers except M14 and M15 who were reading resources from the VC. The data for these two members showed that they had changed their opinions with respect to their selections after receiving notifications. M14 selects M1, M13 and M2 in the first questionnaire. After he received the notifications, M14 selects M1, M13 and M7. For Q10, the community model extracted information only for M15 who was the only newcomer uploading resources to the VC. The selection M15 made at the first questionnaire did not change after he received notifications. It is important to note that the selection of M15 represents what was happening in the physical community and not in the VC since M10 and M12 (selected by M15 as similar in uploading) were members from his research group and supervised by the same supervisor.
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Some lecturers claim that there is no designated time table allocation for the creation of TEL resources (O’Donnell, 2008) not to mention personalised eLearning resources which would take more time, computer skills, and commitment to create as previously mentioned. There is also the concern regarding copyright; some lecturers are afraid of putting all their work from over the years onto the www for fear it will be stolen by others. Harvesting data on students and data pro- tection considerations would have to be taken into consideration; what types of data are educators allowed to harvest regarding their students and subsequently concerns with respect to the cor- rect and secure storage of this collected data. In addition, lecturers may have concerns regarding whether or not the students will receive the most appropriate combination of learning resources to enhance their personal learning experience. Also, how can anyone be sure that the system developed to deliver a personalised interactive eLearning experience for students will work effectively? There is always the possibility that personalised eLearning resources may impact negatively on the students learning experience and some students may feel that they were not given the opportunity to engage with all the interactive learning resources available to the class group and failure could ensue if the course material was not adequately presented to all participants.
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anthropometrics. However, this could potentially reduce parental engagement and understanding of the activity of overweight risk assessment. The next phase of the study will explore whether HVs are best placed to under- take risk assessment discussions with parents and if so what training will ensure parents receive accurate infor- mation. Most HVs showed parents all the preventative information available on the therapeutic wheel rather than guiding them towards their own goals for behaviour change. HV service schedule advises them to use a moti- vational approach, 55 but it seemed to be challenging
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which reflects the inclusion of SNPs in several genes involved in LDL-cholesterol metabolism (APOB, PCSK9, APOE, CETP) and, not surprisingly for a genetic instru- ment, with higher family history of CHD, which has previously been shown to be an independent risk factor for CHD in this cohort  and many others [21, 22]. However, the score was not significantly associated with other CHD or T2D-specific risk factors (total choles- terol, HDL-cholesterol, age, smoking, systolic blood pres- sures, BMI and risk of diabetes). In the smaller CoRDia SMI + RR cohort, the gene score was not significantly associated with any of the CHD-risk variables included in the UKPDS score, indicating that the gene score is cap- turing information concerning CHD risk over and above that captured by the UKPDS score. Therefore, inclusion of the gene score in risk prediction may improve the performance of the UKPDS risk tool alone. Since the composition of the gene score was finalised dozens of robustly association CHD risk loci have been identified primarily by the CARDIoGRAM consortium . Inclu- sion of these variants in the gene score may improve its performance and provide a more accurate measure of an individual’s genetic risk of CHD, although the use of such gene score has given mixed results [19, 20].
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Web technology offers JointZone a built-in browsing and navigation functionality. This basic navigation mechanism is built upon simple linking strategies where a simple link is generated from a single source to a single destination. In author- ing JointZone, we have expanded on this basic notion of linking using linkbases. This idea is adopted from Microcosm (Fountain, Hall, Heath, & Davis, 1990) where links are maintained in databases(linkbases) separated from the documents to which these links apply. JointZone employs the use of different types of links in order to better manage the linking of information piece. These links are classified into structural links, referential links and associative links, adopted from the link taxonomy by (Lowe & Hall, 1996).
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Continuing this example, it may be expected the experience of reading the bus timetable may vary between different people, some may have a positive and others a frustrating experience. These different experiences may result from focusing on different aspects of the timetable, correctly or incorrectly, or from attending to other aspects of the wider situation. What constitutes information may thus differ… for one individual information may be the arrival and departure times, for another it may be the poor quality of the web site the timetable is available on, or for another it may be the significant difference between what is stated on the timetable and what actually happens. And the ‘information’ may be used in different ways. In each case the information may also be experienced as objective (an unchanging fact), subjective (subject to varied interpretation) or transformational (impacting, changing the life of the person involved). These ideas, the variation in ways of
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Synthesis, hierarchy of evidence and clinical effectiveness It is clear from the results of this systematic review and narrative summary that a very important factor in secur- ing beneficial outcomes for pregnant asthmatic women is targeted support from a dedicated healthcare profes- sional who is able to help women with management and self-management. Personalised contact featured in all five of the studies reported here: in the two qualitative designs [14, 19] it was described as important but lack- ing, whilst the three quantitative designs [22, 23, 26] gave positive evidence concerning the beneficial impact that could be achieved with targeted input and personal contact. These quantitative designs [22, 23, 26] are par- ticularly important in this regard as they have quantified a range of clinically and statistically important benefits ac- cruing from dedicated asthma care including improve- ments in asthma symptoms measured by ACQ scores, pre-term births, birth weight and Caesarean section rates.
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disadvantaged group and opted for short text messaging instead of smartphone messaging. Since an exponential rise in smartphone use was expected in the RoI, we included the question on smartphone ownership in the CRF for a future study. The response to this question indicated that approximately three-quarters of the par- ticipants already had a smartphone. Therefore, services via smartphone would be the choice for mHealth today, even in a disadvantaged population in RoI. Some smart- phone instant messaging applications signal the sender when the receiver has read a message; information on whether the participant opens the message or not is useful. Artificially intelligent chatbots will easily enable an interactive approach with participants, and may give greater motivation to participants.
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Sources of Information. Participants used a wide variety of sources to collect dementia specific information including; support groups, supports workers, healthcare professionals, the internet and mass media. Face to face meetings with dementia support workers were largely praised and a source of good information. There were mixed experiences of support groups with participants from interviews seven and ten valuing the peer support and information as vital. However, the majority of participants had negative feelings towards attending support groups“They were all doing the conga through the room. I could see his face and it sort of put him off completely” (Carer W)
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In this article, we focused on personalisation in video games. Indeed, to a large extent personalisation techniques can generalise to other gaming domains. First, a clear contribution lies in the domain of educational games (and by extension, other serious games), where personalised learning has already indicated its benefits (Peirce & Wade, 2010; Peirce, Conlan & Wade, 2008; Gobel, Wendel, Ritter & Steinmetz, 2010). Second, ambient games have been shown to benefit from player behavioural analysis for adapting the game context (Dansley, Stevens & Eglin, 2009; Schouten, Tieben, Ven & Schouten, 2011). Third, in classic games such as chess, it has been shown that accurately modelling the opponent player can increase the playing strength (Donkers, Uiterwijk & van den Herik, 2001; Donkers, Uiterwijk & van den Herik, 2003), but moreover can be applied for scaling the playing strength to be appropriate to the human player (van den Herik, Donkers & Spronck, 2005) for entertainment purposes.
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A prime example of promising test set is UseNet News. In , collaborative filtering is applied to a subset of UseNet News. UseNet provides a high number of posts for its users, and sparsity is a problem. It is therefore valuable to filter information. In this project, there are four types of scenario that can occur, hits, misses, false positive and correct rejections. Hits and correct rejections should be maximised, while misses and false positives are to be minimised. It should be noted however, that the effects of a high rate of misses and false positives depend on the type of domain in which the user actively uses. For example, the result of missing a post on a good movie is not necessarily a big deal, but missing a post on an expensive restaurant (e.g. don’t try the beef) could be costly, both in terms of price, and time spent. UseNet has a low cost, in that the cost of a false positive is annoying but nothing more, as it only takes a couple of seconds to gather that the item you are reading is not suitable for the topic you want to discuss. Misses can also be determined as low cost as truly valuable articles will tend to reappear in follow-up discussions.
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satisfaction. It involves the pedagogical task of informing the customer of available options and invites ‘co-production’ from the consumer to forge a long-lasting relationship. Personalisation in education is user centred and tailored to suit users’ information needs and existing information behaviour (Hartley, 2008). Current research expresses enthusiasm towards personalised learning strategies in higher education. Ubiquitous learning spaces, seamless learning, digital citizenship, learner engagement, and learning-oriented assessment as well as lifelong and life- wide learning are envisaged as defining features of personalised learning (Keppell, 2014). Extensive use of Web 2.0 tools and strategies are deemed essential for the design of a personalised learning environment (McLoughlin & Lee, 2010). Challenges faced by the
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