A Hybrid Cluster Based Collaborative Filtering with Tensor Factorization Approach for Recommendation System in Big Data

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Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Big Data
Format: PDF
Now-a-days, Collaborative Filtering (CF) is the most accepted recommendation technique, however many CF systems suffer from issues like data rating availableness and space dimensionality for neighborhood choice. Therefore, using clustering techniques is a way to reduce time needed for processing these correlations. In this paper, a hybrid Agglomerative Hierarchical Cluster based CF approach with Tensor Factorization (AHC-CF-TF) is projected to solve these issues, which exploits context variables to factorize users, items and domains into latent feature vectors. This approach hybrids clustering and a new tensor factoring based technique to reinforce the effectiveness of CF.
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