Web Mining for Web Personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

Overcoming Incomplete User Models in Recommendation Systems Via an Ontology

To make accurate recommendations, recommendation systems currently require more data about a customer than is usually available. We conjecture that the weaknesses are due to a lack of inductive bias in the learning methods used to build the prediction models. We propose a new method that extends the utility model and assumes that the structure of user preferences follows an ontology of product attributes. Using the data of the MovieLens system, we show experimentally that real user preferences indeed closely follow an ontology based on movie attributes. Furthermore, a recommender based just on a single individual’s preferences and this ontology performs better than collaborative filtering, with the greatest differences when little data about the user is available. This points the way to how proper inductive bias can be used for significantly more powerful recommender systems in the future

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms

Preliminary Essay on the Effect of Foliar Treatment with the Fungicide Triadimenol on Barley Culture Infected by Scald

This study deals with the foliar treatment by the fungicide triadimenol against barley scald. Results have shown that two or three triadimenol treatments have practically stopped the infection evolution. The disease have slightly extended with only one treatment. Moreover, other assessment showed that one, two or three triadimenol treatments were significantly associated to the same increase in the yield

Antifungal Activity of Essential Oils of Origanum majorana and Lavender angustifolia against Fusarium Wilt and Root Rot Disease of Melon Plan

The objective of this study was to evaluate the antifungal activity of essential oils of marjoram (Origanum majorana) and lavender (Lavender angustifolia) against eleven isolates of Fusarium oxysporum f. sp. melonis and ten isolates of Fusarium solani, the causal agents of Fusarium wilt and root rot disease of melon. The effect of essential oils on disease development under in vivo conditions was also tested. GC-MS analysis of marjoram essential oils showed that terpinen-4-ol (34.94%) is the major component, followed by γ-terpinene (24.66%), α-terpinene (13.22%), β-terpinene (5.84%), αterpineol (3.98%), and β-phellandrene (3.16%). Chemical analysis of lavender essential oils showed that α-terpinene (48.76%) is the major component, followed by linalool (16.79%), γ-terpinene (7.00%), β-trans-ocimane (6.47%), β-caryophyllene (5.83%), and lavandulol (3.23%). All essential oils tested in vitro using the disk diffusion method revealed a significant antifungal effect against mycelium growth of all F. oxysporum f. sp. melonis and F. solani isolates. The volatile compounds of essential oils have completely inhibited spore germination of both pathogens. In vivo, the essential oils applied as biofumigant significantly reduced disease severity on melon plants 20 days post-incubation. Lavender essential oils significantly reduced disease severity by almost 60% as compared to control melon plants while Marjoram essential oils reduced disease severity by almost 23% under controlled conditions. These results showed that lavender essential oils may contribute to the development of new antifungal compounds to protect melon crops from Fusarium wilt and root rot diseas