Relevant Words Extraction Method in Text Mining

Nowadays, E-commerce is very popular because of information explosion. Text mining is also important for information extraction. Users are more preferable to use the convenience system from many sources such as through web pages, email, social network and so on. This system proposed the relevant words extraction method for car recommendation system from user email. In relevant words extraction, this system proposed the Rule-based Technique based on Compiling Technique. Context- free grammar is very suitable for relevant words extraction. The extracted keys will be used in recommendation system. Recommendation System (RS) is a most popular tool that helps users to recommend according to their interests. In recommendation, this system proposed Content-based Filtering approach with Jaccard Coefficient that will help the users who want to buy the car by providing relevant car information

Relevant Words Extraction Method in Text Mining

Nowadays, E-commerce is very popular because of information explosion. Text mining is also important for information extraction.  Users are more preferable to use the convenience system from many sources such as through web pages, email, social network and so on. This system proposed the relevant words extraction method for car recommendation system from user email. In relevant words extraction, this system proposed the Rule-based Technique based on Compiling Technique. Context- free grammar is very suitable for relevant words extraction. The extracted keys will be used in recommendation system. Recommendation System (RS) is a most popular tool that helps users to recommend according to their interests. In recommendation, this system proposed Content-based Filtering approach with Jaccard Coefficient that will help the users who want to buy the car by providing relevant car information

Relevant Words Extraction Method for Recommendation System

Nowadays, E-commerce is very popular because of information explosion. Text mining is also important for information extraction. Users are more preferable to use the convenience system from many sources such as through web pages, email, social network and so on. This system proposed the relevant words extraction method for car recommendation system from user email. In relevant words extraction, this system proposed the Rule-based approach in Compiling Technique. Context- free grammar is the most suitable for relevant words extraction. Recommendation System (RS) is a most popular tool that helps users to recommend according to their interests. This system implements efficient recommendation system by using proposed key extraction algorithm, Content-based Filtering (CBF) method and Jaccard Coefficient that will help the users who want to buy the car by providing relevant car information

Relevant Words Extraction Method for Recommendation System

Nowadays, E-commerce is very popular because of information explosion. Text mining is also important for information extraction.  Users are more preferable to use the convenience system from many sources such as through web pages, email, social network and so on. This system proposed the relevant words extraction method for car recommendation system from user email. In relevant words extraction, this system proposed the Rule-based approach in Compiling Technique. Context- free grammar is the most suitable for relevant words extraction. Recommendation System (RS) is a most popular tool that helps users to recommend according to their interests. This system implements efficient recommendation system by using proposed key extraction algorithm, Content-based Filtering (CBF) method and Jaccard Coefficient that will help the users who want to buy the car by providing relevant car information

Interest aware peoplerank: towards effective social-based opportunistic advertising

Various emerging context aware social-based applications and services assume constant non-disruptive connectivity. Mobile advertisers in such environments want to reach potentially interested users in a given proximity and within a specified short duration, whether these users are connected to the network or not. While opportunistic forwarding algorithms can be leveraged for forwarding these advertisements, there is little incentive for those not interested in the ad to act as forwarders. Our goal in this paper is to leverage explicit interest, gathered from a user’s social profile, and integrate it with social-based opportunistic forwarding algorithms in order to enable soft real time opportunistic ad delivery in intermittently connected mobile networks. We propose IPeR, a fully distributed interest-aware forwarding algorithm that integrates with PeopleRank to reduce the overall cost and delay while reducing the number of contacted uninterested candidates. Our results, obtained via simulations and validated with real mobility traces coupled with user social data, are promising. In comparison to interest-oblivious socially-aware protocols such as PeopleRank, the IPeR approach reduces the cost to 70% to reach the same delivery ratio, and reduces the ratio of contacted uninterested forwarders by 23%. It also achieves an extra 70% recall and 107% accuracy with only 2% less precision