During the last few decades, with the rise of Youtube, Amazon, Netflix, and many other such web services, recommender systems have taken more and more place in our lives. From e-commerce (suggest to buyers articles that could interest them) to online advertisement (suggest to users the right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys.
In a very general way, recommender systems are algorithms aimed at suggesting relevant items to users (items being movies to watch, text to read, products to buy, or anything else depending on industries).
Recommender systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors. The main objective is to create a book recommendation system for users.
Personal recommendation systems have been emerged to conduct effective search which related booksbased on user rating and interest.The proposed system used the K-NN Cosine Distance function to measure distance and Cosine Similarity function to find Similarity between the book clusters also we implemented SVD system that give us good recommandation.
● Recommendation system is unturned to exist in the e-commerce businesses with the help of collaborative or content-based filtering to predict different items and yes, users are most satisfied with the products recommended to them.
● While performing Exploratory Data Analysis we observed that almost 42% of readers with age-34 read more books compared to other age group of readers.
● Books with publication years are somewhat between 1950 - 2005.
● Also the readers mostly give 8 ratings(on scale 1-10) to books followed by 10 and 7.
● There are more readers from locations London,england,united kingdom,toronto,ontario,canda cmpare to other locations.
● KNN model gives good recommendation for books.
● SVD(Singular value decompostion) with best accuracy on test data which give stronger recommendations.These results show that our proposed system can remove boring
books from the recommendation list more efficiently.
● Popularity based recommendation systems helpful to new users. we don't have data about new user so here popularity based recommendations are more useful
● Content based recommendation systems aslo perfoming well , they are give more accurate predictions.
● A hybrid recommendation system was built using the combination of both content-based filtering and collaborative filtering systems. A percentile score is given to the results obtained from both content and collaborative filtering models and is combined
● Most of the companies like Netflix , Amazon are using Hybrid recommendation search engines ,becuse they are more efficient..
● In Our case also Hybrid approch gives best recommendations...