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2018 | OriginalPaper | Buchkapitel

E-commerce Recommendation System Using Improved Probabilistic Model

verfasst von : Rahul S. Gaikwad, Sandeep S. Udmale, Vijay K. Sambhe

Erschienen in: Information and Communication Technology for Sustainable Development

Verlag: Springer Singapore

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Abstract

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.

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Vorheriges Kapitel High Dimensionality Characteristics and New Fuzzy Versatile Particle Swarm Optimization

Nächstes Kapitel Extraction of Top-k List by Using Web Mining Technique

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Titel: E-commerce Recommendation System Using Improved Probabilistic Model
verfasst von: Rahul S. Gaikwad
Sandeep S. Udmale
Vijay K. Sambhe
Verlag: Springer Singapore
Buch: Information and Communication Technology for Sustainable Development
Print ISBN: 978-981-10-3919-5

Electronic ISBN: 978-981-10-3920-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-981-10-3920-1_28

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