270 resultados para User interests


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The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

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The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences for making personalized recommendations. However, the uncontrolled vocabulary causes a lot of problems to profile users accurately, such as ambiguity, synonyms, misspelling, low information sharing etc. To solve these problems, this paper proposes to use popular tags to represent the actual topics of tags, the content of items, and also the topic interests of users. A novel user profiling approach is proposed in this paper that first identifies popular tags, then represents users’ original tags using the popular tags, finally generates users’ topic interests based on the popular tags. A collaborative filtering based recommender system has been developed that builds the user profile using the proposed approach. The user profile generated using the proposed approach can represent user interests more accurately and the information sharing among users in the profile is also increased. Consequently the neighborhood of a user, which plays a crucial role in collaborative filtering based recommenders, can be much more accurately determined. The experimental results based on real world data obtained from Amazon.com show that the proposed approach outperforms other approaches.

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Understanding network traffic behaviour is crucial for managing and securing computer networks. One important technique is to mine frequent patterns or association rules from analysed traffic data. On the one hand, association rule mining usually generates a huge number of patterns and rules, many of them meaningless or user-unwanted; on the other hand, association rule mining can miss some necessary knowledge if it does not consider the hierarchy relationships in the network traffic data. Aiming to address such issues, this paper proposes a hybrid association rule mining method for characterizing network traffic behaviour. Rather than frequent patterns, the proposed method generates non-similar closed frequent patterns from network traffic data, which can significantly reduce the number of patterns. This method also proposes to derive new attributes from the original data to discover novel knowledge according to hierarchy relationships in network traffic data and user interests. Experiments performed on real network traffic data show that the proposed method is promising and can be used in real applications. Copyright2013 John Wiley & Sons, Ltd.

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In mobile videos, small viewing size and bitrate limitation often cause unpleasant viewing experiences, which is particularly important for fast-moving sports videos. For optimizing the overall user experience of viewing sports videos on mobile phones, this paper explores the benefits of emphasizing Region of Interest (ROI) by 1) zooming in and 2) enhancing the quality. The main goal is to measure the effectiveness of these two approaches and determine which one is more effective. To obtain a more comprehensive understanding of the overall user experience, the study considers user’s interest in video content and user’s acceptance of the perceived video quality, and compares the user experience in sports videos with other content types such as talk shows. The results from a user study with 40 subjects demonstrate that zooming and ROI-enhancement are both effective in improving the overall user experience with talk show and mid-shot soccer videos. However, for the full-shot scenes in soccer videos, only zooming is effective while ROI-enhancement has a negative effect. Moreover, user’s interest in video content directly affects not only the user experience and the acceptance of video quality, but also the effect of content type on the user experience. Finally, the overall user experience is closely related to the degree of the acceptance of video quality and the degree of the interest in video content. This study is valuable in exploiting effective approaches to improve user experience, especially in mobile sports video streaming contexts, whereby the available bandwidth is usually low or limited. It also provides further understanding of the influencing factors of user experience.

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This paper reports findings from a study of user behaviours and intentions towards online news and information in Australia, undertaken by the Queensland University of Technology Creative Industries Faculty and the Smart Services Cooperative Research Centre. It has used a literature review, online survey, focus groups and interviews to explore attitudes and behaviours towards online news and information. The literature review on consumer user of online media highlighted emerging technical opportunities, and flagged existing barriers to access experienced by consumers in the Australian digital media sector. The literature review highlighted multiple disconnects between consumer interests in online news and their ability to fulfil them. This presents an opportunity for news entities to appraise and resolve. Doing so may enhance their service offering, attract consumers and improve loyalty. These themes were further explored by the survey. The survey results revealed three typologies of user, described as ‘convenience’, ‘loyal’ and ‘customising’. Convenience users tend to access news by default, for example when they log out of email. Loyal users seek out a trusted brand such as mainstream news mastheads. Customising users tend to tailor news to their preferences, and be the first to use leading edge media. Respondents to the survey were then invited to participate in focus groups, which aimed to test the survey results. Consumer perceptions and attitudes are important factors in progression towards an information economy, because ultimately consumers are customers. By segmenting the online news market according to customer typology, media providers may identify new opportunities to attract and retain customers.

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This thesis presents a case study of the Special Broadcasting Service documenting the broadcasting challenges posed by user-generated content initiatives and the work-place approach to strategies for participation. Using the action research method, the project findings reveal that limitations to resources and funding determined the scope for innovation and that the practice of executive editorial control over content was considered fundamental to fulfilling the responsibilities of the public service mandate. Media workers were overwhelmingly positive about the enhanced productive capabilities of the audience and willing to facilitate moderated interactions, however the effectiveness of these initiatives differed according to the level of skills required. This thesis demonstrates how participatory initiatives can enhance aspects of the public service remit relating to cultural diversity, the servicing of niche interests, and broader social representation, and help reinvigorate the relevance of public service broadcasting in the digitalised media sphere.

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Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.

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The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users’ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Del.icio.us website.

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In larger developments there is potential for construction cranes to encroach into the airspace of neighbouring properties. To resolve issues of this nature, a statutory right of user may be sought under s 180 of the Property Law Act 1974 (Qld). Section 180 allows the court to impose a statutory right of user on servient land where it is reasonably necessary in the interests of effective use in any reasonable manner of the dominant land. Such an order will not be made unless the court is satisfied that it is consistent with public interest, the owner of the servient land can be adequately recompensed for any loss or disadvantage which may be suffered from the imposition and the owner of the servient land has refused unreasonably to agree to accept the imposition of that obligation. In applying the statutory provision, a key practical concern for legal advisers will be the basis for assessment of compensation. A recent decision of the Queensland Supreme Court (Douglas J) provides guidance concerning matters relevant to this assessment. The decision is Lang Parade Pty Ltd v Peluso [2005] QSC 112.

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Search log data is multi dimensional data consisting of number of searches of multiple users with many searched parameters. This data can be used to identify a user’s interest in an item or object being searched. Identifying highest interests of a Web user from his search log data is a complex process. Based on a user’s previous searches, most recommendation methods employ two-dimensional models to find relevant items. Such items are then recommended to a user. Two-dimensional data models, when used to mine knowledge from such multi dimensional data may not be able to give good mappings of user and his searches. The major problem with such models is that they are unable to find the latent relationships that exist between different searched dimensions. In this research work, we utilize tensors to model the various searches made by a user. Such high dimensional data model is then used to extract the relationship between various dimensions, and find the prominent searched components. To achieve this, we have used popular tensor decomposition methods like PARAFAC, Tucker and HOSVD. All experiments and evaluation is done on real datasets, which clearly show the effectiveness of tensor models in finding prominent searched components in comparison to other widely used two-dimensional data models. Such top rated searched components are then given as recommendation to users.

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Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.

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Section 180 of the Property Law Act 1974 (Qld) makes provision for an applicant to seek a statutory right of user over a neighbour’s property where such right of use is reasonably necessary in the interests of effective use in any reasonable manner of the dominant land. A key issue in an application under s 180 is compensation. Unfortunately, while s 180 expressly contemplates that an order for compensation will include provision for payment of compensation to the owner of servient land there are certain issues that are less clear. One of these is the basis for determination of the amount of compensation. In this regard, s 180(4)(a) provides that, in making an order for a statutory right of user, the court: (a) shall, except in special circumstances, include provision for payment by the applicant to such person or persons as may be specified in the order of such amount by way of compensation or consideration as in the circumstances appears to the court to be just The operation of this statutory provision was considered by de Jersey CJ (as he then was) in Peulen v Agius [2015] QSC 137.

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This paper focuses on the fundamental right to be heard, that is, the right to have one’s voice heard and listened to – to impose reception (Bourdieu, 1977). It focuses on the ways that non-mainstream English is heard and received in Australia, where despite public policy initiatives around equal opportunity, language continues to socially disadvantage people (Burridge & Mulder, 1998). English is the language of the mainstream and most people are monolingually English (Ozolins, 1993). English has no official status yet it remains dominant and its centrality is rarely challenged (Smolicz, 1995). This paper takes the position that the lack of language engagement in mainstream Australia leads to linguistic desensitisation. Writing in the US context where English is also the unofficial norm, Lippi-Green (1997) maintains that discrimination based on speech features or accent is commonly accepted and widely perceived as appropriate. In Australia, non-standard forms of English are often disparaged or devalued because they do not conform to the ‘standard’ (Burridge & Mulder, 1998). This paper argues that talk cannot be taken for granted: ‘spoken voices’ are critical tools for representing the self and negotiating and manifesting legitimacy within social groups (Miller, 2003). In multicultural, multilingual countries like Australia, the impact of the spoken voice, its message and how it is heard are critical tools for people seeking settlement, inclusion and access to facilities and services. Too often these rights are denied because of the way a person sounds. This paper reports a study conducted with a group that has been particularly vulnerable to ongoing ‘panics’ about language – international students. International education is the third largest revenue source for Australia (AEI, 2010) but has been beset by concerns from academics (Auditor-General, 2002) and the media about student language levels and falling work standards (e.g. Livingstone, 2004). Much of the focus has been high-stakes writing but with the ascendancy of project work in university assessment and the increasing emphasis on oracy, there is a call to recognise the salience of talk, especially among students using English as a second language (ESL) (Kettle & May, 2012). The study investigated the experiences of six international students in a Master of Education course at a large metropolitan university. It utilised data from student interviews, classroom observations, course materials, university policy documents and media reports to examine the ways that speaking and being heard impacted on the students’ learning and legitimacy in the course. The analysis drew on Fairclough’s (2003) model of the dialectical-relational Critical Discourse Analysis (CDA) to analyse the linguistic, discursive and social relations between the data texts and their conditions of production and interpretation, including the wider socio-political discourses on English, language difference, and second language use. The interests of the study were if and how discourses of marginalisation and discrimination manifested and if and how students recognised and responded to them pragmatically. Also how they juxtaposed with and/or contradicted the official rhetoric about diversity and inclusion. The underpinning rationale was that international students’ experiences can provide insights into the hidden politics and practices of being heard and afforded speaking rights as a second language speaker in Australia.