While recommender systems are in widespread use, they still experience problems. Many recommender systems produce recommendations which the customers find unsatisfactory. Further, these systems often suffer from problems when there are not enough participants, or when new products enter the system. We perceive an opportunity for knowledge-based recommender systems to gain leverage on recommenda...

Social recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of items suited to their needs. To provide proper recommendations to users, social recommender systems require accurate models of characteristics, interests and needs for each user. In this paper, we introduce a new model capturing semantics of user-generated tag...

This paper deals the design and implementation of product recommender system on a Intemet shopping mall. In ecommerce application, sometimes potential buyers may be interested in receiving recommendations about what to purchase. The mainstream of automated recommender system is collaborative filtering. Recently knowledgebased approach is proposed. In this paper, we present a knowledge-based pro...

Recommender systems have become a popular technique and strategy for helping users select desirable products or services. Most research in this area focused on applying the method to help the customers in Business-to-Customer (B2C) electronic commerce (e-commerce), however, the participants in Businessto-Business (B2B) market can also get useful assistance from the recommender system. In this a...

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...

As the amount of data provided by various software systems increases, there is a need to offer a filtered set of items personalized to user's needs. To enhance user's comfort and thus to satisfy him, we call for recommender system. Recommender systems suggest a set of items that a user might be interested in or might find them useful. Basically, accomplishing recommendation task consists of two...

1 Introduction Recommender systems offer benefits to both consumers and firms. For consumers, recommender systems help individuals both become aware of new products as well as select desirable products among myriad choices (Pham & Healey, 2005). For firms, recommender systems have the potential to increase profits by converting browsers into buyers, cross-selling products, and increasing loyalt...

Learning distributed representations of documents has pushed the state-of-the-art in several natural language processing tasks and was successfully applied to the field of recommender systems recently. In this paper, we propose a novel content-based recommender system based on learned representations and a generative model of user interest. Our method works as follows: First, we learn represent...

During the last ten years numerous research instruments emerged for evaluating the service quality of Web-based information systems. B2C recommender systems are information systems that support users in selecting information, products, or services improving their decision making process. Unfortunately, the majority of these instruments do not acknowledge the full range of recommendation functio...

Recommender systems have become a valuable tool for successful e-commerce. The quality of their recommendations depends heavily on how precisely consumers are able to state their preferences. However, empirical evidence has shown that the preference construction process is highly affected by uncertainties. This has a negative impact on the robustness of recommendations. If users perceive a lack...