An Advanced Recommender System for Intelligent B2B e-Services.

E-services is a next phase of e-commerce and e-business which provides business services to a wide range of customers using web. Recommender System is one of the most important tools in the e-services. The ultimate aim of the recommender system is to generate the recommendations about the products or items accurately for the customers for which they are interested in. Even though in these recommender system the user ratings and user profiles are not represented accordingly. It is very hard for this system to extract the data properly and generate recommendation about the items accurately. The proposed work is to generate a system which uses fuzzy preference algorithm. Initially, not only the user ratings but also other parameters such as low price, high discount and maximum number of purchase of the product is also considered. These are used to construct the tree structure for the recommendation. The tree structures are then merged together using the similarity analysis algorithm and fuzzy preference algorithm in which all the attributes about the items or products are taken into consideration. This work also generates the group recommendations about the items from a large amount item sets for various applications includes biological domain.