World Wide Web is the biggest source of information. Though the World Wide Web contains a tremendous amount of data, most of the data is irrelevant and inaccurate from users’ point of view. Consequently it has become increasingly necessary for users to utilize automated tools such as recommender systems in order to discover, extract, filter, and evaluate the desired information and resources. M...

Recommendation system becomes an essential in web applications that provide many services and suggest some services automatically as per user’s interest. To develop a recommender system, the collaborative filtering approach is the well-known approach. Collaborative filtering has a major issue called cold start that is how to recommend new user. The performance of this kind of system is depended...

Due the success of emerging Web 2.0, and different social network Web sites such as Amazon and movie lens, recommender systems are creating unprecedented opportunities to help people browsing the web when looking for relevant information, and making choices. Generally, these recommender systems are classified in three categories: content based, collaborative filtering, and hybrid based recommen...

The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data...

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web2.0” recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly f...

In customer relationship management (CRM), online recommender assumes an important role of suggesting the right product or information to the right customer automatically. Hence customers are empowered with the choices that are predicted to be preferred by the system. The underlying technique is often a collaborative filtering (CF) algorithm that harvests both information from similar products ...

Personalized recommender system has attracted wide range of attention among researchers in recent years. These recommender systems suggest products or services depending upon user‟s personal interest. There has been a huge demand for development of web search apps for gaining knowledge pertaining to user‟s choice. A strong knowledge base, type of approach for search and several other factors ma...

In this paper, we describe a personalized recommender system that uses web mining techniques for recommending a student which (next) links to visit within an adaptable educational hypermedia system. We present a specific mining tool and a recommender engine that we have integrated in the AHA! system in order to help the teacher to carry out the whole web mining process. We report on several exp...

In this paper, we present a Web recommender system for recommending, predicting and personalizing music playlists based on a user model. We have developed a hybrid similarity matching method that combines collaborative filtering with ontology-based semantic distance measurements. We dynamically generate a personalized music playlist, from a selection of recommended playlists, which comprises th...

Human beings, not machines, are the ultimate experts for information retrieval tasks, including recommender systems. Consequently, computers are most useful when they combine information about people’s judgments. Collaborative filtering systems make use of this observation by having users explicitly rate items, such as Web pages, with the system making recommendations to other users based on ov...