With the fleetly development of the internet, discovering useful knowledge from the World Wide Web became a censorious issue. With the huge volume of information present in the internet, user needs a help via recommendation system. From the user’s log data lot of recommender systems developed to predict the user’s next request when they view the web pages. However, each recommender system has i...

The paper identifies possibilities of using Web 2.0 tools and ELARS (E-learning Activities Recommender System) in e-learning. Changes that are present in e-learning due to influence of Web 2.0 incited an approach of combining an LMS with Web 2.0 tools and educational recommender system. Some preliminary results of the research project "E-learning Recommender System" are presented. One of the ma...

Web recommender systems anticipate the needs of web users and provide them with recommendations to personalize their navigation. Such systems had been expected to have a bright future, especially in ecommerce and e-learning environments. However, although they have been intensively explored in the Web Mining and Machine Learning fields, and there have been some commercialized systems, the quali...

With the explosive growth of resources available through the Internet, information mismatching and overload have become a severe concern to users. Web users are commonly overwhelmed by huge volume of information and are faced with the challenge of finding the most relevant and reliable information in a timely manner. Personalised information gathering and recommender systems represent state-of-...

  One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-ba...

Web service mining has become one of the promising areas of Service Oriented Architecture. Web service discovery methods include syntactic based system and semantic based system. Recommender system plays a vital role in recommending a product to customer, seldom used for web service recommendation. A variety of techniques have been proposed for performing recommendation namely, content-based, c...

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain kn...

The use of relevant metrics of software systems could improve various software engineering tasks, but identifying relationships among metrics is not simple and can be very time consuming. Recommender systems can help with this decision-making process; many applications have utilized these systems to improve the performance of their applications. To investigate the potential benefits of recommen...