Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering

A NOVEL FUZZY-BASED SIMILARITY MEASURE FOR COLLABORATIVE FILTERING TO ALLEVIATE THE SPARSITY PROBLEM

Memory-based collaborative filtering is the most popular approach to build recommender systems. Despite its success in many applications, it still suffers from several major limitations, including data sparsity. Sparse data affect the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure based...

Data Sparsity Issues in the Collaborative Filtering Framework

With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user efficiency has emerged. Within the fields of user profiling and Web personalization several popular content filtering techniques have been developed. In this chapter we present one of such techniques – collaborative filtering. Apart ...

Cliques-based Data Smoothing Approach for Solving Data-Sparsity in Collaborative Filtering

Collaborative filtering (CF), as a personalized recommending technology, has been widely used in e-commerce and other many personalized recommender areas. However, it suffers from some problems, such as cold start problem, data sparsity and scalability, which reduce the recommendation accuracy and user experience. This paper aims to solve the data sparsity in CF. In the paper, cliquesbased data...

Sparsity Eliminated Collaborative Filtering Using Cluster-based Weighting

Reduction of Data Sparsity in Collaborative Filtering based on Fuzzy Inference Rules

Collaborative filtering Recommender system plays a very demanding and significance role in this era of internet information and of course e commerce age. Collaborative filtering predicts user preferences from past user behaviour or user-item relationships. Though it has many advantages it also has some limitations such as sparsity, scalability, accuracy, cold start problem etc. In this paper we...