Online book recommendation system using Collaborative filtering

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Recommendation systems were evolved as intelligent algorithms, which can generate results in the form of recommendations to users. They reduce the overhead associated with making best choices among the plenty. Now, Recommender systems can be implemented in any domain from E-commerce to network security in the form of personalized services. They provide benefit to both the consumer and the manufacturer, by suggesting items to consumers, which can’t be demanded until the recommendations. Every recommender system comprises of two entities, one is user and other is item. A user can be any customer or consumer of any product or items, who get the suggestions. Input to recommendation algorithm can be a database of user and items and output obliviously will be the recommendations. Input for this system is customers and book data and output of this book denotes the book recommendations. This paper presents a new approach for recommending books to the buyers. This system consists of content filtering, collaborative filtering and association rule mining to produce efficient recommendations.



Advantages
  • This system saves the precious time of customer and very efficient to use.
  • Provides large number of choices for books & also recommend for books.
  • User can buy book easily by making online payment
  • The system recommending algorithm scale well with co-rated items.
Disadvantages
  • Dependent on human ratings for books
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