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Collaborative labeling portrays the process by which numerous users put in metadata in the form of keywords to shared data. Nowadays, collaborative labeling has grown in reputation on the web, on sites that permit users to label bookmarks, photographs and other details. It has been recently become useful and well known as one effective way of classifying items for future search, sharing information, and filtering. So, as to predict the future search of users, we propose a novel collaborative tagging-based page recommendation algorithm using fuzzy classifier. The method consists of three phases: Grouping, Rule Generation Phase and Page Recommendation Phase. In the proposed method, we calculate the resemblance of users in selecting tags and thereby, calculate the nearest neighbors of each user and cluster them. Then, the priority of tags and items for each user is calculated for constructing a Nominal Label Matrix and Nominal Page Matrix. Finally, the fuzzy rules are generated for page recommendation. The experimentation is carried out on delicious datasets and the experimental results ensured that the proposed algorithm has achieved the maximum hit ratio of 6.6% for neighborhood size of 20, which is higher than the existing technique which obtained only 5.5%.

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: As web sites grows the complexity increases in many websites due to huge data. To maintain these data it is very difficult because there are many number of users are increasing day by day. As per information of many websites, there is very insufficient data to get the accuracy or efficiency of web sites. To improve the quality of websites, Recommender Systems are introduced. On the basis of these recommender systems, user gives the ratings to an item and then the reviews are generated for each an item so that the user might know the information of items relevant to his preferences. Here the RS is classified into content-based and collaborative filtering. Later tagging also included in this collaborative filtering. The main issues of Collaborative Filtering are Scalability, cold-start user and sparsity problems. We propose and explore the benefits of collaborative filtering based on tagging for sparseness, scalability and Cold start user issues.