Recommender Systems are used to select online information relevant to a given user. Traditional (memory based) recommenders explore the user-item rating matrix and make recommendations based on users who have rated similarly or items that have been rated similarly. With the growing popularity of social networks, recommender systems can benefit from combining history of user preferences with information from the social/trust network of users. This thesis explores two techniques of combining user-item rating history with trust network information to make better user-item rating predictions. The first approach (SCOAL [5]) simultaneously co-clusters and learns separate models for each co-cluster. The co-clustering is based on the user features as well as the rating history. This captures the intuition that certain groups of users have similar preferences for certain groups of items. The grouping of certain users is affected by the similarity in the rating behavior and the trust network. The second graph-based label propagation approach (MAD [27]) works in a transductive setting and propagates ratings of user-item pairs directly on the user social graph. We evaluate both approaches on two large public data-sets from Epinions.com and Flixster.com. The thesis is amongst the first to explore the role of distrust in rating prediction. Since distrust is not as transitive as trust i.e. an enemy's enemy need not be an enemy or a friend, distrust can't directly replace trust in trust propagation approaches. By using a low dimensional representation of the original trust network in SCOAL, we use distrust as it is and don't propagate it. Using SCOAL, we can pin-point the groups of users and the groups of items that have the same preference model. Both SCOAL and MAD are able to seamlessly integrate side information such as item-subject and item-author information into the trust based rating prediction model. / text

Consumers continuously review products and services on the internet. Others have frequently relied on those reviews in making purchasing decisions. Review texts are usually free-form and associated with a star rating on a 5-point scale. The majority of restaurants receive a 3.5 or 4 star rating on average, so a standalone star rating does not provide adequate information for readers to make a decision. Many researchers have approached the problem with sentiment analysis to classify a sentence or a text as expressing a positive or a negative review. Sentiment analysis, even at the fine-grained level, can only provide classification of positive and negative judgments on any particular aspect under consideration. The novel method proposed in this thesis provides insight into what aspects reviewers deem as relevant when assigning star rating to restaurants. This is accomplished by using an interpretable star rating classification method that predicts star rating based on aspect and polarity score from the review. The model first assigns a polarity score for each aspect in the review text, then predicts a star rating, and outputs a ranked list of aspect importance according to a widely used restaurant reviews dataset. The result from this thesis suggests that the classification model is able to output a reliable ranking from the review texts.

A pilot study had previously demonstrated the utility of a tranquillity prediction tool TRAPT for use in 3 green open spaces in a densely populated area. This allows the calculation of perceived levels of tranquillity in open spaces. The current study expands the range of sites to 8 and importantly considers the views of visitors to these spaces. In total 252 face to face interviews were conducted in these spaces. An important aim of the survey was to determine the extent to which reported tranquillity obtained from the questionnaire survey could be predicted by a previously developed prediction tool TRAPT. A further aim was to determine what additional factors may need to be considered in addition to the purely physical descriptors in TRAPT. The questions included the sounds and sights that were noticed, factors affecting tranquillity as well as questions relating to the benefits of visiting these areas. Predictions were considered satisfactory and could be further improved by taking account of issues surrounding personal safety. Examining the trends in these data it was also shown that the percentage of people feeling more relaxed after visiting the spaces was closely related to overall assessments of perceived tranquillity. Further trends and their implications are presented and discussed in the paper. / Made available in full text March 2014 at the end of the publisher's embargo period.

yes / The UK has recently recognized the importance of tranquil spaces in the National Planning Policy Framework, NPPF. This paper reports on applying the tranquillity rating prediction tool, TRAPT for predicting the perceived tranquillity of a place and using this tool to classify the levels of tranquillity in existing areas. The tool combines soundscape and landscape measures to produce a tranquillity rating on a 0-10 rating scales. For these purposes noise maps, spot noise level measurements, photographic surveys were used to predict tranquillity levels in 8 parks and open spaces in or near the city of Bradford in West Yorkshire in the UK. In addition interviews were conducted with visitors to validate these predictions. It was found that there was a reasonably close relationship between predicted and average assessments given by park visitors which confirmed the usefulness of the tranquillity rating prediction tool for planning and conservation purposes.

The goal of this thesis is to study the possibilities of Artificial neural network as an innovative mathematical methods for financial analysis of company performance, to find out what are today´s requests for performance evaluation of companies are and to identify possible ways how to use this relatively new concept in this area. When processing the possibilities of the computer program Maple for mathematical calculations will be applied. Intermediate objectives are then acquainted with the basic principle on which the artificial neural networks works, to analyze the financial performance of specific company and evaluate potential predictive abilities of the proposed network. The result of the work should be evaluating the success of this approach to financial analysis and evaluation of its use in practice.

yes / Previous work on elucidating the tranquillity of various environments has largely focussed on prediction and validation in urban environments. The setting for the latest phase of research was an English country park and surrounding moors on the urban fringe located 8 miles west of Bradford. Within the area selected there were a number of environments and man-made features and sounds that were thought to significantly affect tranquillity and which were not covered in earlier studies. The experiment extended over a number of months and utilised a jury technique for evaluation involving leading small groups of walkers to different locations in quasi-random order. At each location participants were asked to complete a short questionnaire and measurements of the physical soundscape and landscape images were used to interpret the results and give insights into the importance of the various factors affecting tranquillity. Such data will be useful for effective environmental management and conservation in the countryside.

Recommender systems are an advanced form of software applications, more specifically decision-support systems, that efficiently assist the users in finding items of their interest. Recommender systems have been applied to many domains from music to e-commerce, movies to software services delivery and tourism to news by exploiting available information to predict and provide recommendations to end user. The suggestions generated by recommender systems tend to narrow down the list of items which a user may overlook due to the huge variety of similar items or users’ lack of experience in the particular domain of interest. While the performance of traditional recommender systems, which rely on relatively simpler information such as content and users’ filters, is widely accepted, their predictive capability perfomrs poorly when local context of the user and situated actions have significant role in the final decision. Therefore, acceptance and incorporation of context of the user as a significant feature and development of recommender systems utilising the premise becomes an active area of research requiring further investigation of the underlying algorithms and methodology. This thesis focuses on categorisation of contextual and non-contextual features within the domain of context-aware recommender system and their respective evaluation. Further, application of the Multilayer Perceptron Model (MLP) for generating predictions and ratings from the contextual and non-contextual features for contextual recommendations is presented with support from relevant literature and empirical evaluation. An evaluation of specifically employing artificial neural networks (ANNs) in the proposed methodology is also presented. The work emphasizes on both algorithms and methodology with three points of consideration: contextual features and ratings of particular items/movies are exploited in several representations to improve the accuracy of recommendation process using artificial neural networks (ANNs), context features are combined with user-features to further improve the accuracy of a context-aware recommender system and lastly, a combination of the item/movie features are investigated within the recommendation process. The proposed approach is evaluated on the LDOS-CoMoDa dataset and the results are compared with state-of-the-art approaches from relevant published literature.

Yes / Tranquil environments can provide relief from stresses of everyday of life and can be considered restorative environments. This paper considers the effects of “greening” urban environments to enhance tranquillity and ultimately well-being and health benefits. A number of studies have been conducted at the Bradford Centre for Sustainable Environments at the University of Bradford which have examined the effects of natural features on ratings of tranquillity. These include quantifying the effects of the percentage of natural and contextual features and soundscape quality on rated tranquillity. Recently the resulting prediction equation TRAPT (Tranquillity Rating Prediction Tool) has been used to examine a number of scenarios including city parks and squares, country parks and moorland areas and validated using tranquillity ratings made by visitors to these green spaces and their reported levels of relaxation. In this paper TRAPT is used for predicting tranquillity in city squares of different sizes, to examine rated tranquillity behind natural (green) and manufactured noise barriers and to predict changes in urban streets of introducing avenues of trees, hedges and grass verges. Using such scenarios this paper demonstrates how the application of TRAPT can enable changes in tranquillity to be estimated. This can provide planners, environmentalists, civic leaders and concerned citizens with a further tool to guide improvements in the urban environment by “greening” measures and noise reduction of various kinds and to help counter threats such as over development, tree removal or traffic densification that might threaten existing benefits. / Bradford Centre for Sustainable Environments in the Faculty of Engineering and Informatics at the University of Bradford.