Effective recommendation model using social network for linking user pursuit to product content

P, Valarmathi


The ongoing advancement of data innovation and the rapid development of the internet has encouraged a blast of data which has highlighted the issue of data overload. In reaction to this issue, recommender programs have evolved and helped users find their fascinating content. With the progressively entangled social setting, how to satisfy customized demands effectively has become another development in customized proposal administration contemplates. To mitigate the sparse issue of recommendation systems, we suggest a new recommendation approach based on fuzzy theory to improve their consistency and flexibility in diverse contexts. The proposed method also employs social network to reflect multifaceted factors of users. In this strategy, we group clients and consider about assortment of complex variables. The results on amazon dataset indicate that the proposed method achieves better efficiency over current methods.


Recommender system, E-commerce, Diversification, Fuzzy logic, Amazon dataset

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