Extracting Interests of Users from Web Log Data Log

The knowledge on the cobweb is growing expressively. Without a recommendation theory, the clients may come through lots of instance on the network in finding the knowledge they are stimulated in. Today, many web recommendation theories cannot give clients adequate symbolized help but provide the client with lots of immaterial knowledge. One of the main reasons is that it can't accurately extract users interests. Therefore, analyzing users' Web Log Data and extracting users' potential interested domains become very important and challenging research topics of web usage mining. If users' interests can be automatically detected from users' Web Log Data, they can be used for information recommendation and marketing which are useful for both users and Web site developers. In this paper, some novel algorithms are proposed to mine users' interests. The algorithms are based on visit time and visit density which can be obtained from an analysis of web users' Web Log Data. The experimental results of the proposed methods succeed in finding users interested domains

Mining User Interests from User Search by Using Web Log Data

Web Usage Mining (WUM) is a kind of data mining method that can be used to discover user access patterns from Web log data. A lot of work has been done already about this area and the obtained results are used in different applications such as recommending the Web usage patterns, personalization, system improvement and business intelligence. WUM includes three phases that are called preprocessing, pattern discovery and pattern analysis. There square measure totally different techniques for WUM that have their own benefits and downsides. We tend to initial describe a way for extracting a worldwide linguistics illustration of a pursuit question log then show, however, we are able to use it to semantically extract the user interests. During this paper extraction of users interest from journal knowledge will be done, that square measure supported visit time and visit density which might be get from an analysis of internet users journal knowledge

FARS: Fuzzy Ant based Recommender System for Web Users

Recommender systems are useful tools which provide an adaptive web environment for web users. Nowadays, having a user friendly website is a big challenge in e-commerce technology. In this paper, applying the benefits of both collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on collaborative behavior of ants (FARS). FARS works in two phases: modeling and recommendation. First, user’s behaviors are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of “Information and Communication Technology Center” of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations

Web Mining Functions in an Academic Search Application

This paper deals with Web mining and the different categories of Web mining like content, structure and usage mining. The application of Web mining in an academic search application has been discussed. The paper concludes with open problems related to Web mining. The present work can be a useful input to Web users, Web Administrators in a university environment.Database, HITS, IR, NLP, Web mining

Deriving query suggestions for site search

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T