A framework of hybrid recommender system for web personalisation

Information overload is a significant issue of explosive growth of information on the web. The users are facing numerous problems to select and purchase interesting products online. Recommender systems are the software agents, which are helpful to reduce the problem of information overload. In this paper, architectural framework of hybrid recommender system, i.e., semantic enhanced personaliser (SEP) is proposed for web personalisation. The SEP comprised of three techniques of recommendation such as, original, semantic and category-based recommendation. The original recommendation consists of three components such as user-based collaborative filtering, item-based collaborative filtering and item-based contextual filtering. This recommendation is based on explicit feedback and contextual information provided by the web users while semantic and category-based recommendation is based on implicit feedback using data mining techniques such as, association-rule-mining (ARM), similarity measures and clustering. The SEP is capable to solve the problem of scalability, sparsity, quality of recommendation, synonymy, etc.