RECOMMENDATION SYSTEMS

Akbarzhon Madaminov
Faculty of Informatics and Computer Engineering Innopolis University
Innopolis, Russia a.madaminov@innopolis.university

Abstract—This document describes Recommendation system, their types and why it is important to integrate such a system to the applications that we use in our daily life. And this paper is about how to help people to make choices they will not regret.
Index Terms—Recommendation system, Collaborative filtering, User based, Item based, Hybrid

I. DESCRIPTION
Nowadays, Recommendation System is a part of daily life in which people rely on knowledge to make decisions that are in their best interests. A recommendation system integrated to any kind of applications helps users to filter the information and predicts user preferences for items they use. There are several ways to implement such a system. It could be user based, which recommend items only from the similar users or using the relation between the items. Different algorithms and approaches are provided, which may use rating or content information.
II. ORIGINALITY AND IMPACT
Despite the fact that many approaches have been developed in the past, investigation continues to exist due to its frequent use in many applications that personalize a recommendations. As the system rely on users implicit or explicit feedback, it can make more precise prediction that we can observe in [1]. It is important because people spend their scarce resources, like time, money etc., to find an appropriate option and making choices. Also there is a big benefit not only for users, but for suppliers too. If recommendation system allows users to be more productive while making purchases and finding some needs, then producers will be able to exactly know what consumers want and finding more suitable product for them.
III. DESCRIPTION OF PAPER
Traditional collaborative filtering recommendation is one of the most widely used algorithms in today’s recommendation systems, and it’s also the industry standard in e-commerce. There is a descriptions of a traditional recommendation system and their different improvements in the referenced papers [1],
[2] and [3].
IV. WHY IT IS SUITABLE FOR OUR PROBLEM
Users with similar interests will have common interest bias in the future, according to the core idea of collaborative filtering. The classic collaborative filtering algorithm, on the other hand, has several issues: (1) insufficient confidence based

on user interest bias similarity; (2) failure to account for the time component; (3) absence of correlation between behaviors. Hence, in this paper described algorithms improved from the traditional solution of our problem. According to the Pigeon- hole principle, considering that all items can be classified into small groups and having user’s history of choices made, there are always a group of people with similar requirements and needs for the products. And our problem now is converted to find like-minded users in order to make a suggestion more precise.

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