“International Journal of Science, Engineering and Technology Research (IJSETR)” (E-ISSN: 2278 – 7798) Volume 4 Issue 5 May 2015 · May 20, 2015 Paper ID: IJSETR-4662 1 Abstract—Exponential growth of the world-wide-web and emergence of e-commerce as a platform within reach of common customers had led to the development of numerous recommender systems that defines a personalized information retrieval technique used to identify set(s) of items that will be of interest to a certain user. Most of these researches relate item recommender on the basis of user profile and item-oriented recommendation. But here we are exploring scope of frequent item set based recommendation by implementing Apriori algorithm. Apriori is mainly used to find frequently purchased items/products. The key idea behind this recommendation is that any item set that occurs frequently together must have each item (or any subset) occur at least as frequently. Index Terms— Recommendation System, Apriori Algorithm, Association Rule, Frequent Item set. I. INTRODUCTION Recommendation systems have become extremely common in recent years. This system is applied in a variety of applications. In definition, goal of a Recommender System is to generate significant recommendations to a collection of users for items or different products. Recommendation systems usually produce a list of recommendations in one of two ways - through collaborative filtering or content-based filtering. In Collaborative filtering, it approaches building a model from a user's past activities (items that are previously purchased and/or numerical ratings given to those items) as well as similar decisions made by other users; then use that model to projection items (or ratings for items) that the user may have an concern in. The most popular ones are probably books, research articles, search queries, movies, music, news, social tags, and products in general. There are also recommendation systems for life insurance companies, jokes, experts, restaurants, financial services, and Twitter followers. Technically a recommendation system is software evolved from a new class of data analysis which applies knowledge discovery techniques to the misfortune of making product recommendations during live customer dealings. In this work, we are dealing of frequent item set based recommendation using Apriori Algorithm which works on concept of eliminating most large sets as candidates by looking first at smaller sets and recognizing that a large set cannot be frequent unless all its subsets are. II. EXISITING RECOMMENDATION SYSTEM The majority of existing approaches to recommender systems focus on recommending the most relevant items to individual users without taking consideration of any contextual information, such as time, place and the company of other people (e.g., for watching movies or dining out). In other words, traditionally recommender systems deal with applications having only two types of entities, users and items, and do not put them into a context when providing recommendation. It also provides recommendations that are based on the user’s area of interests, customer searches and also suggests products based on it. For e.g. Amazon uses user view data. If any customer is searching a product from a particular category the system suggests a product form the same category. It is also based on the current search by the user, the site recommends products. [7] E-commerce recommendation algorithms often operate in a challenging environment. For example: [3] • A large retailer might have huge amounts of data, tens of millions of customers and millions of distinct catalog items. • Many applications require the results set to be returned in real-time, in no more than half a second, while still producing high-quality recommendations. • Older customers can have a glut of information, based on thousands of purchases and ratings. • Customer data is volatile: Each interaction provides valuable customer data, and the algorithm must respond immediately to new information. However, in many applications, such as recommending a vacation package, personalized content on a Web site, or a movie, it may not be sufficient to consider only users and items – it is also important to incorporate the contextual information into the recommendation process in order to recommend items to users in certain circumstances. For example, using the temporal context, a travel recommender system would provide a vacation recommendation in the winter that can be very different from the one in the summer. Similarly, in the case of personalized content delivery on a Web site, it is important to determine what content needs to be delivered (recommended) to a customer and when. Every user who visits the site may not buy a product. They can just go through it and based on those search results the site recommends a product. III. ASSOCIATION RULES To extract the frequent sets of items from large amount of data and to gather this information used the association rules. The form of an association rule is I → j, where I is a set of items and j is an item. The implication of this association rule is that if all of the items in I appear in some basket, then j is “likely” to appear in that basket as well. Frequent Item Set Based Recommendation using Apriori Abhishek Saxena, Navneet K Gaur