3,047 research outputs found
Evolutionary intelligent agents for e-commerce: Generic preference detection with feature analysis
Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customerâs generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customersâ generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising
Semantic and Syntactic Matching of Heterogeneous e-Catalogues
In e-procurement, companies use e-catalogues to exchange product infor-mation with business partners. Matching e-catalogues with product requests helps the suppliers to identify the best business opportunities in B2B e-Marketplaces. But various ways to specify products and the large variety of e-catalogue formats used by different business actors makes it difficult.
This Ph.D. thesis aims to discover potential syntactic and semantic rela-tionships among product data in procurement documents and exploit it to find similar e-catalogues. Using a Concept-based Vector Space Model, product data and its semantic interpretation is used to find the correlation of product data. In order to identify important terms in procurement documents, standard e-catalogues and e-tenders are used as a resource to train a Product Named Entity Recognizer to find B2B product mentions in e-catalogues.
The proposed approach makes it possible to use the benefits of all availa-ble semantic resources and schemas but not to be dependent on any specific as-sumption. The solution can serve as a B2B product search system in e-Procurement platforms and e-Marketplaces
On Recommendation of Learning Objects using Felder-Silverman Learning Style Model
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation
âWARESâ, a Web Analytics Recommender System
Il est difficile d'imaginer des entreprises modernes sans analyse, c'est une tendance dans les entreprises modernes, mĂȘme les petites entreprises et les entrepreneurs individuels commencent Ă utiliser des outils d'analyse d'une maniĂšre ou d'une autre pour leur entreprise. Pas Ă©tonnant qu'il existe un grand nombre d'outils diffĂ©rents pour les diffĂ©rents domaines, ils varient dans le but de simples statistiques d'amis et de visites pour votre page Facebook Ă grands et sophistiquĂ©s dans le cas des systĂšmes conçus pour les grandes entreprises, ils pourraient ĂȘtre shareware ou payĂ©s. Parfois, vous devez passer une formation spĂ©ciale, ĂȘtre un spĂ©cialiste certifiĂ©s, ou mĂȘme avoir un diplĂŽme afin d'ĂȘtre en mesure d'utiliser l'outil d'analyse. D'autres outils offrent une interface dâutilisateur simple, avec des tableaux de bord, pour satisfaire leur comprĂ©hension dâinformation pour tous ceux qui les ont vus pour la premiĂšre fois. Ce travail sera consacrĂ© aux outils d'analyse Web. Quoi qu'il en soit pour tous ceux qui pensent Ă utiliser l'analyse pour ses propres besoins se pose une question: "quel outil doit je utiliser, qui convient Ă mes besoins, et comment payer moins et obtenir un gain maximum". Dans ce travail je vais essayer de donner une rĂ©ponse sur cette question en proposant le systĂšme de recommandation pour les outils analytiques web âWARES, qui aideront l'utilisateur avec cette tĂąche "simple".
Le systĂšme WARES utilise l'approche hybride, mais surtout, utilise des techniques basĂ©es sur le contenu pour faire des suggestions. Le systĂšme utilise certains ratings initiaux faites par utilisateur, comme entrĂ©e, pour rĂ©soudre le problĂšme du âdĂ©marrage Ă froidâ, offrant la meilleure solution possible en fonction des besoins des utilisateurs. Le besoin de consultations coĂ»teuses avec des experts ou de passer beaucoup d'heures sur Internet, en essayant de trouver le bon outil. Le systĂšme luiâmĂȘme devrait effectuer une recherche en ligne en utilisant certaines donnĂ©es prĂ©alablement mises en cache dans la base de donnĂ©es hors ligne, reprĂ©sentĂ©e comme une ontologie d'outils analytiques web existants extraits lors de la recherche en ligne prĂ©cĂ©dente.It is hard to imagine modern business without analytics; it is a trend in modern business, even small companies and individual entrepreneurs start using analytics tools, in one way or another, for their business. Not surprising that there exist many different tools for different domains, they vary in purpose from simple friends and visits statistic for your Facebook page, to big and sophisticated systems designed for the big corporations, they could be free or paid. Sometimes you need to pass special training, be a certified specialist, or even have a degree to be able to use analytics tool, other tools offers simple user interface with dashboards for easy understanding and availability for everyone who saw them for the first time. Anyway, for everyone who is thinking about using analytics for his/her own needs stands a question: âwhat tool should I use, which one suits my needs and how to pay less and get maximum gainâ. In this work, I will try to give an answer to this question by proposing a recommender tool, which will help the user with this âsimple taskâ. This paper is devoted to the creation of WARES, as reduction from Web Analytics REcommender System. Proposed recommender system uses hybrid approach, but mostly, utilize contentâbased techniques for making suggestions, while using some userâs ratings as an input for âcold startâ search. System produces recommendations depending on userâs needs, also allowing quick adjustments in selection without need of expensive consultations with experts or spending lots of hours for Internet search, trying to find out the right tool. The system itself should perform as an online search using some preâcached data in offline database, represented as an ontology of existing web analytics tools, extracted during the previous online search
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and âenablersâ, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm usersâ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to âunannotatedâ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the âBag of Visual Wordsâ
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ânon-informative
visual wordsâ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
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