In daily life we need many things to be searched over the internet, for search purpose there are many search engines available. Whenever we search something we try to get the most relevant results, and this can be achieved using Recommender systems.In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users w...

Social media platforms are a rich source of information these days, however, of all the available information, only a small fraction is of users’ interest. To help users catch up with the latest topics of their interests from the large amount of information available in social media, we present a relevant content filtering based framework for data stream summarization. More specifically, given ...

One of the most important tasks in every office is document management which includes the subtasks of creating, archiving, retrieving, dispatching, updating and processing documents. In the last decades and in many different ways, there have been attempts to automate these tasks with positive results. Technological advances allow these tasks to be fully automated through the use of computers an...

Combining collaborative filtering with some other technique is most common in hybrid recommender systems. As many recommended items from collaborative filtering seem to be similar with respect to content, the collaborative-content hybrid system suffers in terms of quality recommendation and recommending new items as well. To alleviate such problem, we have developed a novel method that uses a d...

Recommender Systems (RSs) are garnering a significant importance with the advent of e-commerce and ebusiness on the web. This paper focused on the Movie Recommender System (MRS) based on human emotions. The problem is the MRS need to capture exactly the customer’s profile and features of movies, therefore movie is a complex domain and emotions is a human interaction domain, so difficult to comb...

While both the data volume and heterogeneity of the digital music content is huge, it has become increasingly important and convenient to build a recommendation or search system to facilitate surfacing these content to the user or consumer community. Most of the recommendation models fall into two primary species, collaborative filtering based and content based approaches. Variants of instantia...

OSN plays a vital role in day to day life. User can communicate with other user by sharing several types of contents like image, audio and video contents. Major issue in OSN(Online Social Network) is to preventing security in posting unwanted messages. Ability to have a direct control over the messages posted on user wall is not provided. Unwanted post will be directly posted on the public wall...

Content-Based Multicast is a type of multicast where the source sends a set of different classes of information and not all the subscribers in the multicast group need all the information. Use of filtering publish-subscribe agents on the intermediate nodes was suggested [5] to filter out the unnecessary information on the multicast tree. However, filters have their own drawbacks like processing...

Internet is a powerful source of information. However, some of the information that is available in the Internet, cannot be shown to every type of public. For instance, pornography is not desirable to be shown to children. To this end, several algorithms for text filtering have been proposed that employ a Vector Space Model representation of the webpages. Nevertheless, these type of filters can...

Recommender System are new generation internet tool that help user in navigating through information on the internet and receive information related to their preferences. Although most of the time recommender systems are applied in the area of online shopping and entertainment domains like movie and music, yet their applicability is being researched upon in other area as well. This paper presen...