4,304 research outputs found
Recurrent Session Approach to Generative Association Rule based Recommendation
This article introduces a generative association rule (AR)-based recommendation system (RS) using a recurrent neural network approach implemented when a user searches for an item in a browsing session. It is proposed to overcome the limitations of the traditional AR-based RS which implements query-based sessions that are not adaptive to input series, thus failing to generate recommendations. The dataset used is accurate retail transaction data from online stores in Europe. The contribution of the proposed method is a next-item prediction model using LSTM, but what is trained to develop the model is an associative rule string, not a string of items in a purchase transaction. The proposed model predicts the next item generatively, while the traditional method discriminatively. As a result, for an array of items that the user has viewed in a browsing session, the model can always recommend the following items when traditional methods cannot. In addition, the results of user-centered validation of several metrics show that although the level of accuracy (similarity) of recommended products and products seen by users is only 20%, other metrics reach above 70%, such as novelty, diversity, attractiveness and enjoyability
ディープラーニングとグラフ分析に基づく実用的なレコメンダーシステムのユーザーモデリング
Tohoku University石垣司課
Understanding and Mitigating Multi-sided Exposure Bias in Recommender Systems
Fairness is a critical system-level objective in recommender systems that has
been the subject of extensive recent research. It is especially important in
multi-sided recommendation platforms where it may be crucial to optimize
utilities not just for the end user, but also for other actors such as item
sellers or producers who desire a fair representation of their items. Existing
solutions do not properly address various aspects of multi-sided fairness in
recommendations as they may either solely have one-sided view (i.e. improving
the fairness only for one side), or do not appropriately measure the fairness
for each actor involved in the system. In this thesis, I aim at first
investigating the impact of unfair recommendations on the system and how these
unfair recommendations can negatively affect major actors in the system. Then,
I seek to propose solutions to tackle the unfairness of recommendations. I
propose a rating transformation technique that works as a pre-processing step
before building the recommendation model to alleviate the inherent popularity
bias in the input data and consequently to mitigate the exposure unfairness for
items and suppliers in the recommendation lists. Also, as another solution, I
propose a general graph-based solution that works as a post-processing approach
after recommendation generation for mitigating the multi-sided exposure bias in
the recommendation results. For evaluation, I introduce several metrics for
measuring the exposure fairness for items and suppliers, and show that these
metrics better capture the fairness properties in the recommendation results. I
perform extensive experiments to evaluate the effectiveness of the proposed
solutions. The experiments on different publicly-available datasets and
comparison with various baselines confirm the superiority of the proposed
solutions in improving the exposure fairness for items and suppliers.Comment: Doctoral thesi
Towards reinventing the statistical system of the central bank of nigeria for enhanced knowledge creation
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementThe Central Bank of Nigeria (CBN) produces statistics that meet some of the data needs of
monetary policy and other uses. How well this is fulfilled by the CBN is consequent on the
quality of its statistical system, which has direct implication for knowledge creation through
processed mass of statistical information. Questionnaires based on IMF Data Quality
Assessment Frameworks (DQAFs) for BOP & IIP Statistics, and monetary statistics are applied
to evaluate the quality of the CBN statistical system. Extant sound practices and deficiencies
of the statistical system are identified; while improvement measures and statistical
innovations are suggested.
Enabled by relevant organic laws, the CBN compiles statistics in a supportive environment
with commensurate human and work tool resources that meet the needs of statistical
programs. Statistics production is carried out impartially and professionally, in broad
conformity with IMF statistics manuals and compilation guides, regarding concepts, scope,
classification and sectorization; and in compliance with e‐GDDS periodicity and timeliness for
dissemination. Other observed sound statistical practices include valuation of transactions
and positions using market prices or appropriate proxies; and recording, generally, of flows
and stocks on accrual basis; while compiled statistics are consistent within datasets and
reconcilable over a time period; etc.
Some of the generic weaknesses are absence of statistics procedural guide; lack of routine
evaluation and monitoring of statistical processes; inadequacy of branding to distinctively
identify the bank’s statistical products; non‐disclosure of changes in statistical practices;
non‐conduct of revision studies; and metadata concerns. The BOP & IIP statistics weaknesses
comprise coverage inadequacies, sectorization/classification issues, lack of routine
assessment of source data and inadequate assessment and validation of intermediate data
and statistical output; while for monetary statistics, non‐compilation of the OFCS is identified
apart from the generic. Recommendations include broadening source data, developing useroriented
statistical quality manuals, establishing comprehensive manuals of procedures and
their corresponding statistical compilation techniques, integrating statistical auditing into
the statistical system, enhancing metadata and conducting revision studies, among others
A Semantic Web approach to ontology-based system: integrating, sharing and analysing IoT health and fitness data
With the rapid development of fitness industry, Internet of Things (IoT) technology is becoming one of the most popular trends for the health and fitness areas. IoT technologies have revolutionised the fitness and the sport industry by giving users the ability to monitor their health status and keep track of their training sessions. More and more sophisticated wearable devices, fitness trackers, smart watches and health mobile applications will appear in the near future. These systems do collect data non-stop from sensors and upload them to the Cloud. However, from a data-centric perspective the landscape of IoT fitness devices and wellness appliances is characterised by a plethora of representation and serialisation formats. The high heterogeneity of IoT data representations and the lack of common accepted standards, keep data isolated within each single system, preventing users and health professionals from having an integrated view of the various information collected. Moreover, in order to fully exploit the potential of the large amounts of data, it is also necessary to enable advanced analytics over it, thus achieving actionable knowledge. Therefore, due the above situation, the aim of this thesis project is to design and implement an ontology based system to (1) allow data interoperability among heterogeneous IoT fitness and wellness devices, (2) facilitate the integration and the sharing of information and (3) enable advanced analytics over the collected data (Cognitive Computing). The novelty of the proposed solution lies in exploiting Semantic Web technologies to formally describe the meaning of the data collected by the IoT devices and define a common communication strategy for information representation and exchange
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Ontology learning for Semantic Web Services
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 18/10/2010.The expansion of Semantic Web Services is restricted by traditional ontology engineering methods. Manual ontology development is time consuming, expensive and a resource exhaustive task. Consequently, it is important to support ontology engineers by
automating the ontology acquisition process to help deliver the Semantic Web vision.
Existing Web Services offer an affluent source of domain knowledge for ontology
engineers. Ontology learning can be seen as a plug-in in the Web Service ontology
development process, which can be used by ontology engineers to develop and maintain
an ontology that evolves with current Web Services. Supporting the domain engineer
with an automated tool whilst building an ontological domain model, serves the purpose
of reducing time and effort in acquiring the domain concepts and relations from Web
Service artefacts, whilst effectively speeding up the adoption of Semantic Web Services, thereby allowing current Web Services to accomplish their full potential. With that in mind, a Service Ontology Learning Framework (SOLF) is developed and
applied to a real set of Web Services. The research contributes a rigorous method that
effectively extracts domain concepts, and relations between these concepts, from Web
Services and automatically builds the domain ontology. The method applies pattern-based
information extraction techniques to automatically learn domain concepts and
relations between those concepts. The framework is automated via building a tool that implements the techniques. Applying the SOLF and the tool on different sets of services results in an automatically built domain ontology model that represents semantic knowledge in the underlying domain. The framework effectiveness, in extracting domain concepts and relations, is evaluated
by its appliance on varying sets of commercial Web Services including the financial domain. The standard evaluation metrics, precision and recall, are employed to determine both the accuracy and coverage of the learned ontology models. Both the
lexical and structural dimensions of the models are evaluated thoroughly. The evaluation results are encouraging, providing concrete outcomes in an area that is little researched
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