Proposed Approach for Book Recommendation Based on User k-NN
Large data repositories helped us in support systems but created a huge problem for meaningful information retrieval. Filtering of data based on user requirements solved this problem. This process of data filtering when combined with prediction developed
- PDF / 805,554 Bytes
- 16 Pages / 439.37 x 666.142 pts Page_size
- 4 Downloads / 222 Views
Abstract Large data repositories helped us in support systems but created a huge problem for meaningful information retrieval. Filtering of data based on user requirements solved this problem. This process of data filtering when combined with prediction developed recommendation systems. Initial work in recommendation systems can be listed in the areas of cognitive science, approximation theory, marketing models, and automatic text processing. This paper focuses on recommendation system for books. In this paper, training and testing models are designed to predict user ratings for new users. The predicted user ratings are used to propose three types of recommendations based on three different user attributes.
⋅
Keywords Recommendation system Collaborative filtering similarity Cosine similarity User k-nn
⋅
⋅
⋅
Pearson
1 Introduction In present world each person wants quick supplies for his requirements in every field of life including shopping or renting of books. Recommendation systems provide best possible solution to this problem. These are kind of expert systems which help in gathering the related information [1]. Most of recommendation systems work for almost similar purpose that is to recommend items which are most relevant to the users. To fulfill this purpose recommendation systems use different approaches including collaborative, item-based, and hybrid filtering. Rohit (✉) Department of CS&E, Amity University, Noida, India e-mail: [email protected] S. Sabitha ⋅ T. Choudhury Faculty, Department of CS&E, Amity University, Noida, India e-mail: [email protected] T. Choudhury e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 S.K. Bhatia et al. (eds.), Advances in Computer and Computational Sciences, Advances in Intelligent Systems and Computing 554, https://doi.org/10.1007/978-981-10-3773-3_53
543
544
Rohit et al.
Table 1 Output for recommendation User Id Prediction
Age
Author
Book Title
4017
48
A. Manette Ansey
Midnight Champagne: A Novel (Mysteries & Horror)
New Orleans, Louisiana, USA
4.102143
4017
48
A. Manette Ansey
Sister (Mysteries & Horror)
New Orleans, Louisiana, USA
3.450125
4017
48
A. Manette Ansey
Vinegar Hill (Oprah’s Book Club (Paperback))
New Orleans, Louisiana, USA
3.355193
4228
41
A. Manette Ansey
Unwanted Company
Austin, Texas, USA
3.151324
In this paper we are using collaborative filtering approach to provide recommendations to the users. We are training a book rating data with our training model. This trained data will be sent to testing model. The testing model will predict user ratings for new users. On the basis of these predicted values, a system is proposed to recommend books to new users on their personal attributes which are age, location, and interest. Using these three attributes we are proposing three different models. All models include dataset provided by our training and testing models. To create this training model we used a real-time dataset of books as described in Fig. 5. It has large number of entries which are feasi
Data Loading...
