Learning personalized tag ontology from user tagging information

The cross-sections of the Social Web and the Semantic Web has put folksonomy in the spot light for its potential in overcoming knowledge acquisition bottleneck and providing insight for "wisdom of the crowds". Folksonomy which comes as the results of collaborative tagging activities has provided insight into user's understanding about Web resources which might be useful for searching and organizing purposes. However, collaborative tagging vocabulary poses some challenges since tags are freely chosen by users and may exhibit synonymy and polysemy problem. In order to overcome these challenges and boost the potential of folksonomy as emergence semantics we propose to consolidate the diverse vocabulary into a consolidated entities and concepts. We propose to extract a tag ontology by ontology learning process to represent the semantics of a tagging community. This paper presents a novel approach to learn the ontology based on the widely used lexical database WordNet. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. We provide empirical evaluations by using the semantic information contained in the ontology in a tag recommendation experiment. The results show that by using the semantic relationships on the ontology the accuracy of the tag recommender has been improved.

Federal Court Rules O23 r 11 - offers of compromise - offer for dismissal of claim and to forego recovery of costs.

In Uniline Australia Ltd ACN 010752057 v S Briggs Pty Ltd ACN 007415518 (No 2) [2009] FCA 920 Greenwood J considered a number of principles guiding the exercise of discretion in relation to costs, particularly when offers of compromise have been made under the formal process provided by the Federal Court Rules.

Can we deliver both population-based and personalized approaches to cancer care?

Responding to the individual needs of the person affected by cancer is a fundamental tenet of nursing care. The evidence base to enable highly personalized approaches to the way we provide care has grown enormously in recent years. Today, we have a much better understanding of the mechanisms underpinning health needs of people with cancer, as well as the wide range of environmental, sociocultural, psychological, and biological influences on these needs. This growing evidence base enables us to better target and tailor interventions in increasingly sophisticated ways.

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0.

Personalized recommender systems integrating tags and item taxonomy

The social tags in Web 2.0 are becoming another important information source to profile users' interests and preferences to make personalized recommendations. To solve the problem of low information sharing caused by the free-style vocabulary of tags and the long tails of the distribution of tags and items, this paper proposes an approach to integrate the social tags given by users and the item taxonomy with standard vocabulary and hierarchical structure provided by experts to make personalized recommendations. The experimental results show that the proposed approach can effectively improve the information sharing and recommendation accuracy.

Adopting relevance feature to learn personalized ontologies

Relevance feature and ontology are two core components to learn personalized ontologies for concept-based retrievals. However, how to associate user native information with common knowledge is an urgent issue. This paper proposes a sound solution by matching relevance feature mined from local instances with concepts existing in a global knowledge base. The matched concepts and their relations are used to learn personalized ontologies. The proposed method is evaluated elaborately by comparing it against three benchmark models. The evaluation demonstrates the matching is successful by achieving remarkable improvements in information filtering measurements.

UCPR r 360 – offers to settle – joint offer by first and second plaintiffs

The decision in ACN 070 037 599 Pty Ltd v Larvik Pty Ltd (No 2) [2008] QSC 118 involved a consideration of the implications for a plaintiff whose offer to settle under Part 5 of the Uniform Civil Procedure Rules 1999 (Qld) was made jointly with another plaintiff who abandoned her action before trial. The court found nothing wrong with the making of a joint offer. It concluded the successful plaintiff would be entitled to indemnity costs on the simple test of whether the judgment for that plaintiff was more favourable than the offer.

Offers to settle - by the rules

In Balnaves v Smith [2012] QSC 408 Byrne SJA concluded that an offer to settle could be an “offer to settle” under Chapter 9 Part 5 of the Uniform Civil Procedure Rules 1999 (Qld) (UCPR) despite the inclusion of non-monetary terms. His Honour took a different approach to that taken by Moynihan SJA in Taske v Occupational & Medical Innovations Ltd [2007] QSC 147.

Personalized ontology learning for enhancing text mining effectiveness

Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves significant improvements in most information filtering measurements. This research contributes to the fields of ontological filtering, user profiling, and knowledge representation. The related outputs are critical when systems are expected to return proper mining results and provide personalized services. The scientific findings have the potential to facilitate the design of advanced preference mining models, where impact on people's daily lives.

A social matching system : using implicit and explicit information for personalized recommendation in online dating service

Online dating websites enable a specific form of social networking and their efficiency can be increased by supporting proactive recommendations based on participants' preferences with the use of data mining. This research develops two-way recommendation methods for people-to-people recommendation for large online social networks such as online dating networks. This research discovers the characteristics of the online dating networks and utilises these characteristics in developing efficient people-to-people recommendation methods. Methods developed support improved recommendation accuracy, can handle data sparsity that often comes with large data sets and are scalable for handling online networks with a large number of users.

Settlement offers on appeal : when might costs be awarded on the indemnity basis?

In Roberts v Prendergast [2013] QCA 89 the respondent had offered to settle the appeal, purporting to make the offer under Chapter 9 Part 5 of the Uniform Civil Procedure Rules 1999 (Qld) (UCPR). Differing views were expressed in the Court of Appeal regarding the impact in the circumstances of the offer to settle, with the majority concluding that the appellant should pay the respondent’s costs on the standard basis.

Mandatory offers under the Motor Accident Insurance Act 1994 - with interest please!

In Windon v Edwards [2005] QDC 029 Robin QC DCJ considered the cost consequence of mandatory final offers under the Motor Accident Insurance Act 1994 (Qld)

Practice and Procedure - offers to settle - no costs benefit if no compromise

In Jones v Millward [2005]QCA76 the Queensland Court of Appeal held that an offer to settle under the UCPR will not attract a costs benefit unless it involves some element of compromise

Motor Accident Insurance Act 1994 - compulsory conferences and offers to settle

In Lindsay v Aumaali [2004] QDC 028 the Court considered whether it could, in effect, postpone the requirement for a compulsory conference under s51A of the Moror Accident insurance Act 1994 (Qld) or the exchange of final offers under s51C of the Act until after the start of proceedings.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.