Collaborative Filtering Techniques in Recommendation Systems
Recommendation system is the tool to user preferences over a given set of items. It takes help of the previous auxiliary information in terms of feedback or ratings. The main purpose of a recommender system is to engage users and enhance their experience
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1 Introduction Recommendation systems are information filtering systems that urge to predict preferences that user might have for an item over other. Recommendation systems are very popular in applications like movies, books, research articles, search queries, social tags, product, financial services, restaurants, twitter pages, job, university, friends and what not. To increase product sales is the primary goal of recommendation system by bringing a relevant item to the user and thus increasing the overall profit, which covers the functional goal of recommendation system such as [1]—relevancy, serendipity and diversity. Most popular recommender systems of today are Group Lens recommender system, Amazon.com recommender system, Netflix Movie recommender system, Google News personalisation system, Facebook friend recommendations, link prediction recommender system [1]. First recommendation system was developed in 1992 by Goldberg, Nichols, Oki and Terry. This was called Tapestry which allows users to rate an item good or bad and further used keyword filtering for recommendation [2–4]. Thus, recommendation system works on available information in any form and then applies different filtering techniques to find the most appropriate choice (like the movie, show, web page, scientific literature and news that a user might have interest in). The recommendation system makes use of data mining techniques [4, 5] and prediction algorithm to find out user’s interest in information, item and their other interests. Later on, several recommendation systems developed which use different filterings to lure their customers and make them feel more attended (Fig. 1). S. K. Raghuwanshi (B) · R. K. Pateriya Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, M. P, India e-mail: [email protected] R. K. Pateriya e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 R. K. Shukla et al. (eds.), Data, Engineering and Applications, https://doi.org/10.1007/978-981-13-6347-4_2
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S. K. Raghuwanshi and R. K. Pateriya
Fig. 1 Recommendations and recommender system
The reason for many companies care about recommendation system is to deliver actual value to their customer. Recommender systems provide a scalable way of personalising content for users in scenarios with many items. It engages many scientists, since it is a major problem of data science, a perfect intersection of software engineering, machine learning and statistics. Recommender systems are an effective tool for personalisation. Since it is based on actual user behaviour, users can make decisions directly based on the results. These systems work on unstructured and dynamically changing data because of which predictions are more specific and up to date. Although recommender systems are application-specific and require specific filtering process, few properties must be addressed by all of them [6] like user preference, prediction accuracy, confidence score, user’s trust on a recommendation system. The rest of this paper is
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