Background:
Due to enormous data in web sites, recommending users for every item is impossible. For this problem
Recommender Systems (RS) are introduced. RS is categorized into content-based (CB), collaborative Filtering (CF) and Hybrid RS.
Based on these techniques recommendations are done to user. In this, CF is the recent technique used in RS in which tagging features
also provided.
Objective:
Three main issues occur in RS are scalability problem which occurs when there is a huge data, sparsity problem occurs when
rating data is missing and cols start user or item problem occurs when new user or new item enters in the system. To avoid these issues
here we have proposed Tag and Time weight model with GA in Collaborative Tagging.
Method:
Here we have proposed a method Collaborative Tagging (CT) with Tag and Time weight model with real value genetic
algorithm which enhances the recommendation quality by removing the issues of sparsity and cold start user problems with the help of
missing value prediction. Here in this the sparsity problem can be removed using missing value prediction and cold start problems are
removed using tag and time weight model using GA.
Results:
Here we have compared the results of Collaborative Filtering with cosine similarity (CF-CS), Collaborative Filtering with
Diffusion Similarity (CF-DS), Tag and Time weight model with Diffusion similarity (TAW-TIW-DS) and Tag and Time weight model
using Diffusion similarity and Genetic algorithm (TAW-TIW-DS-GA).
Conclusion:
Here we have compare the proposed approach with the baseline approaches and the metrics are used MAE, prediction
percentage, Hit-rate and Hit-rank. Based on these metrics for every split TAW-TIW-DS-GA shown best results as compared to existing
approach.
Missing value prediction for qualitative information systems