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You searched for +publisher:"University of Manchester" +contributor:("CHEN, KE K"). Showing records 1 – 3 of 3 total matches.

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University of Manchester

1. Alahmadi, Dimah Hussain N. RECOMMENDER SYSTEMS BASED ON ONLINE SOCIAL NETWORKS -AN IMPLICIT SOCIAL TRUST AND SENTIMENT ANALYSIS APPROACH.

Degree: 2017, University of Manchester

Recommender systems (RSs) provide personalised suggestions of information orproducts relevant to user’s needs. RSs are considered as powerful tools that help usersto find interesting items matching their own taste. Although RSs have made substantialprogress in theory and algorithm development and have achieved many commercialsuccesses, how to utilise the widely available information on Online Social Networks(OSNs) has largely been overlooked. Noticing this gap in existing research on RSsand taking into account a user’s selection being greatly influenced by his/her trustedfriends and their opinions, this thesis proposes a novel personalised RecommenderSystem framework, so-called Implicit Social Trust and Sentiment (ISTS) based RSs.The main motivation was to overcome the overlooked use of OSNs in RecommenderSystems and to utilises the widely available information from such networks. Thiswork also designs solutions to a number of challenges inherent to the RSs domain,such as accuracy, cold-start, diversity and coverage.ISTS improves the existing recommendation approaches by exploring a new sourceof data from friends’ short posts in microbloggings. In the case of new users who haveno previous preferences, ISTS maps the suggested recommendations into numericalrating scales by applying the three main components. The first component is measuringthe implicit trust between friends based on their intercommunication activities andbehaviour. Owing to the need to adapt friends’ opinions, the implicit social trust modelis designed to include the trusted friends and give them the highest weight of contributionin recommendation encounter. The second component is inferring the sentimentrating to reflect the knowledge behind friends’ short posts, so-called micro-reviews.The sentiment behind micro-reviews is extracted using Sentiment Analysis (SA) techniques.To achieve the best sentiment representation, our approach considers the specialnatural environment in OSNs brief posts. Two Sentiment Analysis methodologiesare used: a bag of words method and a probabilistic method. The third ISTS componentis identifying the impact degree of friends’ sentiments and their level of trustby using machine learning algorithms. Two types of machine learning algorithms areused: classification models and regressions models. The classification models includeNaive Bayes, Logistic Regression and Decision Trees. Among the three classificationsmodels, Decision Trees show the best Mean absolute error (MAE) at 0.836. SupportVector Regression performed the best among all models at 0.45 of MAE.This thesis also proposes an approach with further improvement over ISTS, namelyHybrid Implicit Social Trust and Sentiment (H-ISTS). The enhanced approach appliesimprovements by optimising trust parameters to identify the impact of the features(re-tweets and followings/followers list) on recommendation results. Unlike the ISTSwhich allocates equal weight to trust features, H-ISTS provides different weights todetermine the different effects of the two trust features. As a… Advisors/Committee Members: CHEN, KE K, Zeng, Xiaojun, Chen, Ke.

Subjects/Keywords: Recommender systems; trust; sentiment analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Alahmadi, D. H. N. (2017). RECOMMENDER SYSTEMS BASED ON ONLINE SOCIAL NETWORKS -AN IMPLICIT SOCIAL TRUST AND SENTIMENT ANALYSIS APPROACH. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:306813

Chicago Manual of Style (16th Edition):

Alahmadi, Dimah Hussain N. “RECOMMENDER SYSTEMS BASED ON ONLINE SOCIAL NETWORKS -AN IMPLICIT SOCIAL TRUST AND SENTIMENT ANALYSIS APPROACH.” 2017. Doctoral Dissertation, University of Manchester. Accessed July 16, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:306813.

MLA Handbook (7th Edition):

Alahmadi, Dimah Hussain N. “RECOMMENDER SYSTEMS BASED ON ONLINE SOCIAL NETWORKS -AN IMPLICIT SOCIAL TRUST AND SENTIMENT ANALYSIS APPROACH.” 2017. Web. 16 Jul 2019.

Vancouver:

Alahmadi DHN. RECOMMENDER SYSTEMS BASED ON ONLINE SOCIAL NETWORKS -AN IMPLICIT SOCIAL TRUST AND SENTIMENT ANALYSIS APPROACH. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2019 Jul 16]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:306813.

Council of Science Editors:

Alahmadi DHN. RECOMMENDER SYSTEMS BASED ON ONLINE SOCIAL NETWORKS -AN IMPLICIT SOCIAL TRUST AND SENTIMENT ANALYSIS APPROACH. [Doctoral Dissertation]. University of Manchester; 2017. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:306813


University of Manchester

2. Liu, Zixu. INTEGRATED DEMAND AND SUPPLY SIDE MANAGEMENT AND SMART PRICING FOR ELECTRICITY MARKET.

Degree: 2018, University of Manchester

On the one hand, the demand response management and dynamical pricing supported by the smart grid had started to lead to fundamentally different energy consumption behaviours; On the other hand, energy supply has gone through a dramatic new pattern due to the emergence and development of renewable energy resources. Facing these changes, this thesis investigates one of the resulting challenges, which is how to integrate the wholesale market and the retail market into one framework in order to achieve optimal balancing between demand and supply. Firstly, based on the existing mechanisms of the wholesale and retail electricity markets, a simulation tool is proposed and developed. This enables the ISO to find the best balance between supply and demand, by taking into account the different objectives of the generators, retailers and customers. Secondly, a new market mechanism based on the interval demand is proposed in order to address the challenges of the unpredictable demand due to the demand response management programs. Under the proposed new market mechanism, the corresponding approaches are investigated in order to support the retailers to find their profit-optimal pricing strategies, the generators to develop their best bidding strategies, and the ISO to identify the market clearing price function in order to best balance supply and demand. In particular: 1) For the ISO, our designed mechanism could effectively handle unpredictable demand under the dynamic retail pricing. It also enables the realisation of the goals of dynamic pricing by utilising smart meters; 2) In the retail market, we extend the smart pricing model in the current research in order to enable the retailers to find the most-profitable pricing scheme under the proposed new mechanism with the demand-based piecewise cost (i.e., market clearing price) function; 3) For the wholesale market, we developed a pricing forecasting model in order to forecast a market clearing price. Based on this model, we analysed the optimal bidding strategies for a generator under an interval demand from the ISO. Simulation results are provided in order to verify the effectiveness of the proposed approaches. Advisors/Committee Members: CHEN, KE K, Zeng, Xiaojun, Chen, Ke.

Subjects/Keywords: Electricity Market; Demand Response; Balancing Mechanism; Optimal Bidding; Smart Pricing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Liu, Z. (2018). INTEGRATED DEMAND AND SUPPLY SIDE MANAGEMENT AND SMART PRICING FOR ELECTRICITY MARKET. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313677

Chicago Manual of Style (16th Edition):

Liu, Zixu. “INTEGRATED DEMAND AND SUPPLY SIDE MANAGEMENT AND SMART PRICING FOR ELECTRICITY MARKET.” 2018. Doctoral Dissertation, University of Manchester. Accessed July 16, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313677.

MLA Handbook (7th Edition):

Liu, Zixu. “INTEGRATED DEMAND AND SUPPLY SIDE MANAGEMENT AND SMART PRICING FOR ELECTRICITY MARKET.” 2018. Web. 16 Jul 2019.

Vancouver:

Liu Z. INTEGRATED DEMAND AND SUPPLY SIDE MANAGEMENT AND SMART PRICING FOR ELECTRICITY MARKET. [Internet] [Doctoral dissertation]. University of Manchester; 2018. [cited 2019 Jul 16]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313677.

Council of Science Editors:

Liu Z. INTEGRATED DEMAND AND SUPPLY SIDE MANAGEMENT AND SMART PRICING FOR ELECTRICITY MARKET. [Doctoral Dissertation]. University of Manchester; 2018. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:313677

3. Reeve, Henry. Learning in high dimensions with asymmetric costs.

Degree: 2019, University of Manchester

A classifier making predictions in noisy environments with limited data will inevitably make some mistakes. However, some types of error are more harmful than others. For example, the cost of misidentifying an explosive as a benign electronic device is far greater than the cost of misidentifying a laptop as an explosive. In such cases it is of paramount significance that the relative cost of different types of errors is taken into account when constructing and assessing algorithms for making predictions under uncertainty. This thesis will develop a distribution dependent theory of learning with asymmetric costs. For a comprehensive coverage we consider four different learning scenarios: (1) Supervised learning with a fixed cost matrix, (2) Weakly supervised cost-sensitive learning with unknown costs, (3) Sequential cost-sensitive learning with bandit feedback and (4) Neyman Pearson classification for qualitatively asymmetric costs. In each of these scenarios we seek to identify the key properties of the data distribution which influence the difficulty of the learning problem. These include the distribution's dimensionality, smoothness and the level of noise. We shall pay particular attention to minimax rates which give the minimum risk attainable by a classification algorithm, uniformly, over a given family of distributions. We begin our analysis with a supervised setting in which the costs of the possible prediction errors are given by a known cost matrix. In this setting we give a natural generalisation of Tysbakov's margin condition which quantifies the level of noise in the data in relation to the optimal decision boundaries for the cost matrix. We then identify the minimax rates with an exponent which depends solely upon the dimensionality of the distribution, the smoothness of the regression function and the level of noise. Significantly, these rates hold for all compact sub-manifolds and for any reasonable cost matrix. We then show that the minimax rates are actually attained by a simple cost-sensitive k-nearest neighbour method. Departing from the fully supervised setting we turn to a class of weakly supervised cost-sensitive learning problems with stochastic and feature dependent costs. We show that the minimax rates have the same exponent as their supervised counter parts and once again, these rates are attainable by simple k-nearest neighbour methods. We then move to an online scenario in which the learner sequentially makes a prediction and then incurs the cost of their prediction, without ever observing either the true class label or the costs that would have been incurred if an alternative prediction had been made. This problem is equivalent to the problem of multi-armed bandits with covariates. We present the conceptually simple k-NN UCB algorithm for this problem, which combines the k-NN method from supervised learning with the UCB algorithm for multi-armed bandits. We establish a minimax optimal regret bound for this algorithm (up to logarithmic terms) which demonstrates that the method is capable of… Advisors/Committee Members: CHEN, KE K, Brown, Gavin, Chen, Ke.

Subjects/Keywords: Classification; Machine Learning; Cost sensitive; Neyman Pearson; Minimax; Non-parametric; k nearest neighbours

…and s/he has given The University of Manchester certain rights to use such Copyright… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Reeve, H. (2019). Learning in high dimensions with asymmetric costs. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318266

Chicago Manual of Style (16th Edition):

Reeve, Henry. “Learning in high dimensions with asymmetric costs.” 2019. Doctoral Dissertation, University of Manchester. Accessed July 16, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318266.

MLA Handbook (7th Edition):

Reeve, Henry. “Learning in high dimensions with asymmetric costs.” 2019. Web. 16 Jul 2019.

Vancouver:

Reeve H. Learning in high dimensions with asymmetric costs. [Internet] [Doctoral dissertation]. University of Manchester; 2019. [cited 2019 Jul 16]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318266.

Council of Science Editors:

Reeve H. Learning in high dimensions with asymmetric costs. [Doctoral Dissertation]. University of Manchester; 2019. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318266

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