A Recommendation System Using Data Mining Techniques

CHAPTER ONE

1.0                                           I N T R O D U C T I O N

In a world where the number of choices can be overwhelming, recommender systems help users find  and  evaluate items of interest.   They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. Such systems have become powerful tools in domains from electronic commerce to digital libraries and  knowledge management.   For  example, a consumer of just about any major online retailer who expresses an interest in an item – either through viewing a product description or by placing the item in his “shopping cart” – will likely receive recommendations  for  additional  products.

Many different algorithmic approaches have been  applied to the  basic  problem of making accurate and efficient recommender systems. The earliest “recommender systems” were content filtering systems designed to fight information overload in textual domains. These were often based on traditional information-filtering and information-retrieval systems. Recommender systems that incorporate information- retrieval methods are frequently used to satisfy ephemeral needs (short-lived, often one-time needs) from relatively static databases. For example, requesting a recommendation for a book preparing a sibling for a new child in the family. Conversely, recommender  systems  that  incorporate information-filtering methods are frequently used to satisfy persistent information (long-lived, often frequent, and specific) needs from relatively stable databases in domains with a rapid turnover or frequent additions. For example,  recommending  AP stories  to a user  concerning the latest news regarding a senator’s re-election campaign.

Without computers, a person often receives recommendations by listening to what people around him have to say. If many people in the office state that they enjoyed a particular movie, or if someone he tends to agree with suggests a given book, then he may  treat  these  as recommendations.

These products can  be recommended based on the  top  overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the  consumer as a prediction for future buying behavior. This study will  address the technology used  to generate recommendations, focusing on the  application of data mining techniques.

1.2                                      STATEMENT OF THE PROBLEM

In this competitive world every product has its reviews and the ratings given by the users on the e-commerce sites they are using. The new users are always willing to go through the product reviews before buying that particular product (Nehete et al., 2014). Same is in the case of movies people will read the reviews of the movie they want to watch. Meanwhile, the increasing online information leads to the information overload problem. Different techniques has been used in the past but they have data-sparsity and poor performance accuracy challenge. To deal with this problem recommender system automatically suggests the item to the particular user according to the user’s profile or the ratings (Yang et al., 2014) and also, using data mining techniques overcome the data-sparsity drawback and improve the performance accuracy.

1.3                                     OBJECTIVES OF THE STUDY

This work aimed at making a comprehensive study of recommendation system using data mining techniques

The objectives of the study are:

1. To prioritize and personalize the
2. To determine the interest of the users and to help the users in making the search easier

• To generate an influential algorithm for data mining techniques.

1.4                             SCOPE AND LIMITATION OF THE STUDY

The scope of this work covers making a compressive study on recommendation system using data mining techniques. Recommendation systems are useful tools for the users as they provide the actual suggestions according to their likes and dislikes.

It can perform its task only when the user’s past information, his or her browsing history, previous purchases and the feedback is available.

Terms and Definitions

Association Rules: Used to associate items in a database sharing some relationship (e.g. co- purchase information). Often takes the for “if this, then that” such as “If the customer buys a handheld videogame then the customer is likely to purchase batteries.”

Collaborative Filtering: Selecting content based on the preferences of people with similar interests.

Meta-recommenders: Provide users with personalized control over the generation of a single recommendation list formed from the combination of rich recommendation data from multiple information sources and recommendation techniques.

Nearest-Neighbor Algorithm: A recommendation algorithm that calculates the distance  between  users based on the degree of correlations between scores in the users’ preference histories.

Predictions of how much a user will like an item are computed by taking the weighted average of the opinions of a set of nearest neighbors for that item.

Recommender Systems: Any system that provides a recommendation, prediction, opinion, or user-configured list of items that assists the user in evaluating items.

Social Data-Mining: Analysis and redistribution of information from records of social activity such as newsgroup postings, hyperlinks, or system usage history.

Temporal Recommenders: Recommenders that incorporate time into the recommendation process. Time can be either an input to the recommendation function, or the output of the function.