Aggregate Diversity Improvement in Recommender System through Ranking Approach

Recommender system gives recommendations for its user and one of the important quality aspects of the recommendations is diversity. In this paper, effort is put for improving the aggregate diversity, diversity across all users, in recommendations of the recommender system using ranking approach. In presented ranking approach, to improve diversity in recommendations, because to make user to interact with system consistently since it is ecommerce oriented system, work is done under predefined assumptions: 1) products already popular or declared as excellent will not be considered and 2) products already declared as bad will not be considered in recommendation process. The effort utilizes collaborative filtering technique for filtering purpose, in collaborative filtering both users based and item based methods are used one after the other. The proposed approach broadly works in two phases, First phase, it will list out all the similar products which are collected by the result of collaborative filtering and remove the products from list according the assumptions. In second, it assigns ranks to the products in the list. Products with higher rate count will get low rank and vice versa and from the ranked products list system will recommend the information items or products to the user and while recommending it should consider about similarity of the products also. Here, popular products are not considered hence, long tail products will get opportunity to be recommended and in turn improves the aggregate diversity in recommendations. Keywords— Recommender System, Collaborative Filtering, Diversity, User based, Item based, Ranking, Recommendations 142 Doddegowda B.J. et al Introduction Recommender system, an information filtering technology, commonly used on ecommerce web sites that use a collaborative filtering to present information on items and products that are likely to be of interest to the reader or user. To presenting the recommendations, the recommender system will use details of the registered user's profile and opinions and habits of their whole community of users and compare the information to reference characteristics to present the recommendations. Systems that attempt to predict items, e.g., movies, music, books, that a user may be interested in and Systems that help people find information that will interest them, by facilitating social / conceptual connections or other means. Recommender system is absolute necessity because of growth of the original information in the world every year. The size of the original information is in terms of Exabyte, the Fig . 1 shows the scenario why there is a need of recommender system and it is because of many options are present in the world. When a particular person wants to buy something but he do not able to make a decision among numerous options, there exactly the recommender system will recommend some products or information items to particular person and those recommended to the person are relevant to the his interest. By recommending products to person recommender system minimizes the effort for finding the relevant products. Fig.1 Necessity of Recommender System Businesses increases amount of information that customer must process before they are able to select which products meet their needs. Effective solution for this information overload problem is recommender system. To recommend products based on different constraints like, top sellers on site, past behaviour of the user and all these techniques are part of personalization on a site. To achieve personalization on site means, system adapts itself to each customer. Recommender system is needed to reach individual personalization for each user or customer. Usually presence of recommender system will make following things and more to them get happen.  Automates quotes like: I like this book; you might be interested in it, I saw this movie, you’ll like it, don’t go see that movie! Aggregate Diversity Improvement in Recommender System 143  Many of the top commerce sites use recommender systems to improve sales  Users may find new books, music, or movies that was previously unknown to them  Also can find the opposite for e.g.: movies or music that will definitely not be enjoyed Opportunity for Recommendations A good recommendation engine can make a difference for any online business. This is because there are two fundamental activities online Search and Browse. When a consumer knows exactly what he is looking for, he searches for it. But when he is not looking for anything specific, he browses. It is the browsing that holds the golden opportunity for a recommendation system, because the user is not focused on finding a specific thing – he is open to suggestions. The overview of the recommender system is shown in the Fig.2. Fig.2 Overview of Recommender systems