Abstract—

In recent years, recommender systems have become increasingly popular and are applied to a diverse range of applications, due to development of items and its various types available, that leaves the users to choose from abundant provided options. Recommendations typically speed up searches and make it easier for users to access content that they are interested in, and also surprise them with offers they would have never searched for. By using filtering methods for pre-processing the data, recommendations are provided either through collaborative filtering or through content-based filtering. In this paper, we demonstrate a recommendation model that involves Matrix Factorization as a collaborative filtering solution used for providing recommendations. And with further application of artificial intelligence over the previously obtained results from collaborative filtering, the final precise list of top recommended items is listed for the user thereby using a hybrid approach in recommendation. Thus, the recommender model provides personalised recommendations

INTRODUCTION

Recommender Systems are software tools and techniques of machine learning that provides suggestions for items to an individual user. Recommender systems enable an improved access to relevant products and information by making personalized suggestions based on the examples of a similar user’s likes and dislikes. Recommendation systems emerge into intelligent algorithms, which can generate results in the form of recommendations to the users. The popular suggestions are related to various decision-making processes, such as what items to buy, what music to listen to, or what online news to read. Whatever the system suggests the user, is termed as, the “item”. A system normally focuses on a specific type of item, say product or utility range and accordingly its design, its graphical user interface, and the core recommendation technique used to provide the recommendations are all trained to generate useful and effective suggestions for that unique type of item. 

Recommendation Systems

Recommendations typically speed up searches thereby making it easier for users to access content that they are interested in, and surprise them with offers they would have never searched for. Recommender systems in recent years have become extremely common and are applied in a variety of popular applications. The most famous ones are probably movies, music, news, books and products in general. Over the years, collaborative filtering had emerged as the most prominent approach for recommendations. There has been an explosion of methods that are introduced in the area of recommendations, in the recent years. Moreover, the development of recommender systems has also increased the complexity of the modern systems when compared to the traditional or basic systems that utilize methods such as collaboration and content based filtering. 

LITERATURE SURVEY

The paper [1] by Dheeraj Bokde, et al, discusses various factorization models such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Probabilistic Matrix Factorization (PMF). They attempt to present a comprehensive survey constituting Matrix Factorization model like SVD so as to address the challenges of Collaborative Filtering algorithms, which can be served as a road map for research and to practice in that specific area.

Aditya Krishna Menon and Charles Elkan in their research [2], propose to solve the link prediction problem in graphs using a supervised matrix factorization approach. The model learns about the latent features from the topological structure of a (possibly directed) graph, and is proved to make better predictions than popular unsupervised scores. They show how these latent features may be combined with optional explicit features for nodes or edges that yield better performance than using either type of feature exclusively. Finally, a novel approach is put forward to address the class imbalance problem which is common in link prediction by directly optimizing for a ranking loss. The model has been optimized with stochastic gradient descent and scales to large graphs.

Yu Wang et al, in their work [3], they propose a two-way multinomial logistic model for recommender systems for categorical ratings. Specifically, as we are treating the possible ratings as mutually exclusive events, whose probability is determined just by the latent factor of the users and the items through a two-way multinomial logistic function. This proposed method has a compatibility with categorical ratings and the advantage of incorporating both the covariate information and the latent factors of the users and items uniformly.

Ruiqin Wang et al, [4] discuss about Collaborative Filtering (CF) algorithms that have been widely used to provide personalized recommendations in e-commerce websites and social network applications and Among which, Matrix Factorization (MF) is one of the most popular and efficient techniques. However, most MF-based recommender models rely only on the past transaction information of users, hence there is inevitably a data sparsity problem. So they propose a novel recommender model based on matrix factorization and semantic similarity measure. Initially, a new semantic similarity measure is created based on the semantic information in the Linked Open Data (LOD) knowledge base, which is a hybrid measure that is based on feature and distancemetrics.

Ritu Rani et al, in their paper [5], variety of algorithms such as k-mean clustering, collaborative filtering are used for the information suggestion. With the increase in demand of items amongst customer the growth is enhanced in information technology and ecommerce websites. The proposed algorithm mainly involves K-mean clustering and CF, as they first explain about this involved algorithm and then describe the specific purpose of the used algorithm in theirstudy. 

METHODOLOGY

A. Collaborative filtering method The usual method analyzes the nature of each item. In our case, recommending books to a user by performing Natural Language Processing on the content of each book. Collaborative Filtering, on the other hand, does not require any information about the items or the users themselves. It recommends books based on users’ past behaviour. Among the various types of collaborative filtering techniques, this system uses model-based method. An efficient model-based CF method is matrix factorization. Matrix Factorization Book recommendation system has been developed at a great rate due to the emerging web technology and library modernization. The purpose of this project is to demonstrate the development of book recommendation systems through the usage of Matrix Factorization. Recommender systems typically produce a list of recommendations either through collaborative filtering or through content-based filtering. Matrix Factorization is a collaborative filtering solution for recommendations. Using the data set of user-item pairings, one can create a matrix.

PROCESS MODEL

We use a book recommender system for this specific research. The book recommendation consists of a model that involves three major phases or steps in its process of getting the input from the user and using the book information stored in the database, to provide the desired result as output, which is the final recommended book-list for every individual user of the system. The three phases are namely; Information collection, learning and prediction or final recommendation phase Information collection phase The initial process of data collection is performed where, the user details are collected. Details include the user profile data of each individual in order to provide personal recommendations. Every user has to create a simple profile in order to login to the system. The user is asked for the profile name, email id, age, and country in order to be used for providing a more personal set of recommendations with best matching books. The other important information to be collected from the user should include the reviews marked by the user for the viewed and read books. As it is really essential in knowing the reading taste of every individual user. The collected review will be used further in listing a set of similar and favorable books for that reader. With this set of information, a matrix made of user-item pairings is created, one can create a matrix.

RESULT AND CONCLUSION

This book recommendation uses one of the filtering techniques known as collaborative filtering (CF) and content- based filtering, making the system a hybrid recommender system. The first method we consider is the collaborative filtering under which, is a model base CF method called matrix factorization that is mainly used in this system to provide a personalised recommendation. Thus as a result a list of books that are not yet visited or viewed by a specific user is predicted.Initially, the respective rating values are presented to the reader as the recommended set of books. Next is where, each of the books in the top ‘n’(here, 5 is considered as ‘n’) book- list is known of its lexile score for reading where, the books with context similarity is listed. Accordingly, two or more books with similar context and lexile measurement is identified relatively. And this step does not require the ratings and reviews. Consequently, a list of efficient and closely relating books of favour is provided for each user. This hybrid recommendation technology used has thus proved to be effective in suggestions, and useful in providing personalized recommendations. This system can be used in websites and book stores to be applied for user interaction and service providence.

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