Fuzzy Content Mining for Targeted Advertisement

Content-targeted advertising system is becoming an increasingly important part of the funding source of free web services. Highly efficient content analysis is the pivotal key of such a system. This project aims to establish a content analysis engine involving fuzzy logic that is able to automatically analyze real user-posted Web documents such as blog entries. Based on the analysis result, the system matches and retrieves the most appropriate Web advertisements. The focus and complexity is on how to better estimate and acquire the keywords that represent a given Web document. Fuzzy Web mining concept will be applied to synthetically consider multiple factors of Web content. A Fuzzy Ranking System is established based on certain fuzzy (and some crisp) rules, fuzzy sets, and membership functions to get the best candidate keywords. Once it is has obtained the keywords, the system will retrieve corresponding advertisements from certain providers through Web services as matched advertisements, similarly to retrieving a products list from Amazon.com. In 87% of the cases, the results of this system can match the accuracy of the Google Adwords system. Furthermore, this expandable system will also be a solid base for further research and development on this topic

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Matching Contextual Ads and Web Page Contents through Computational Advertising: Getting the Best Match

The technological transformation and automation of digital content delivery has revolutionized the media industry. What is more, the Internet is rapidly turning into an advertising channel. Just in the United States, Internet advertising revenues hit $7.3 billion for the first quarter of 2011, representing a 23 percent increase over the same period in 2010 (iab.net, 2011). Beneficiaries of this investment and growth are search engines such as Google, Yahoo, and MSN. Also, Malaysian advertising landscape is gradually shifting its traditional media forms to the emergent of Internet advertising but still at a budding stage. The latter shows much room for growth, as the industry fuels to content digitization on Web applications. In this project, the types of Internet advertising that is going to be discussed on are Contextual Ads and Sponsored Search Ads, but the major scope will be on Contextual Advertising. Given that, these types of advertising have the central challenge of finding the “best match” between a given context and a suitable advertisement, through principled way of computational methods. Hence, it is also referred as Computational advertising. Furthermore, there are four main players that exists in the Internet advertising ecosystem that are going to be discussed in this study, which are; Users, Advertisers, Ad Exchange and Publishers. Hence in order to find ways to counter the centre challenge, this research study will mainly address two objectives, which are to successfully make the best Contextual Ads selections that match to the Web Page contents through the concept of Computational advertising, and to ensure that there is a valuable connection between the Web pages and the Contextual Ads. Thus, the scope of the study will be mainly on discussing about the theory of Computational advertising itself, besides elaborating on Contextual Ads, matching Contextual Ads and Web pages and also, finding the most feasible way in creating the valuable connection between Contextual Ads and the Web pages. Moreover, at the end of every discussion in every subtopic, some insights on the Internet advertising in Malaysian context are discussed as per related issue. v Consequently, this study employed two main methods to address the research questions rose. Those methods include extensive research and analysis on previous literature works and journals, and also in depth surveys to collect related data and information in real-life situations. Every part of gathered data and findings will then be analyzed accordingly. All discussions, conclusion and future recommendations are presented as per sections. Hence in order to prove the working mechanism of matching Contextual Ads and Web pages by using Computational advertising approach, Web pages together with the ads matching system, will then be developed through FYP-II timeline, as the final product of the study

Exploring The Value Of Folksonomies For Creating Semantic Metadata

Finding good keywords to describe resources is an on-going problem: typically we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well populated source of unstructured tags describing web resources. This paper explores the value of the folksonomy tags as potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexer’s mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources

Peer - Mediated Distributed Knowledge Management

Distributed Knowledge Management is an approach to knowledge management based on the principle that the multiplicity (and heterogeneity) of perspectives within complex organizations is not be viewed as an obstacle to knowledge exploitation, but rather as an opportunity that can foster innovation and creativity. Despite a wide agreement on this principle, most current KM systems are based on the idea that all perspectival aspects of knowledge should be eliminated in favor of an objective and general representation of knowledge. In this paper we propose a peer-to-peer architecture (called KEx), which embodies the principle above in a quite straightforward way: (i) each peer (called a K-peer) provides all the services needed to create and organize "local" knowledge from an individual's or a group's perspective, and (ii) social structures and protocols of meaning negotiation are introduced to achieve semantic coordination among autonomous peers (e.g., when searching documents from other K-peers). A first version of the system, called KEx, is imple-mented as a knowledge exchange level on top of JXTA