E-commerce Recommendation System Using Improved Probabilistic Model

  • Rahul S. Gaikwad
  • Sandeep S. Udmale
  • Vijay K. Sambhe
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)


Recommender system is the backbone of e-commerce marketing strategies. Popular e-commerce websites use techniques like memory-based collaborative filtering approach based on user similarity with only rank as an attribute. This paper proposes a model-based collaborative filtering recommender system based on probabilistic model using improved Naive Bayes algorithm. Proposed system uses Naive Bayes algorithm with bigram language model to improve search query analysis. Therefore, search query, click time and query time are used as features for Naive Bayes algorithm model. This model is trained on 1.2 million customer data over a 3-month period for 1.8 million products. Proposed system predicts the probability of products and products will be recommended to the user to make top-N recommendations. Results of the proposed system show the model recommends products with 14% more accuracy as compared to simple Naive Bayes model.


Recommendation Naive Bayes Bigram language model Collaborative filtering Item-to-item based approach 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Rahul S. Gaikwad
    • 1
  • Sandeep S. Udmale
    • 1
  • Vijay K. Sambhe
    • 1
  1. 1.Department of Computer Enginering and Information TechnologyVJTIMumbaiIndia

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