Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

An Empirical Comparison of Dissimilarity Measures for Recommender Systems

Many content-based recommendation approaches are based on a dissimilarity measure based on the product attributes. In this paper, we evaluate four dissimilarity measures for product recommendation using an online survey. In this survey, we asked users to specify which products they considered to be relevant recommendations given a reference product. We used microwave ovens as product category. Based on these responses, we create a relative relevance matrix we use to evaluate the dissimilarity measures with. Also, we use this matrix to estimate weights to be used in the dissimilarity measures. In this way, we evaluate four dissimilarity measures: the Euclidean Distance, the Hamming Distance, the Heterogeneous Euclidean-Overlap Metric, and the Adapted Gower Coefficient. The evaluation shows that these weights improve recommendation performance. Furthermore, the experiments indicate that when recommending a single product, the Heterogeneous Euclidean-Overlap Metric should be used and when recommending more than one product the Adapted Gower Coefficient is the best alternative. Finally, we compare these dissimilarity measures with a collaborative method and show that this method performs worse than the dissimilarity based approaches

Розробка та дослідження моделей та програмних рішень для рекомендаційної системи вибору товарів масового вжитку

There have been proposed investigation of the problem of creating recommendations with technical description for building the Recommender System of consumer goods with help of modern algorithms, approaches, principles and contains the investigation of the most popular methods. It was defined, that the deployment of Recommender Systems is one of the rapidly developing areas for improving applied information technolog ies, tools for automatic generating offers service based on the investigation of the personal needs and profile of customers. It was investigated, that such systems have started to play a very important role in the fast growing Internet, as they help users to navigate in a large amount of information, because users are not able to analyze a large amount of information, because it is very difficult and takes a lot of time and effort, but due to such systems, namely Recommender Systems that are able to filter a large amount of information, and provide for users the information and recommendations their likes the problem can be solved and instead of providing the static information, when users search and, perhaps, buy products, Recommender Systems increase the degree of interactivity to expand the opportunities provided to the user. It was defined, that Recommendation systems form recommendations independently for each specific user based on past purchases and searches, and also on the basis of the behavior of other users with help of recommendation services, which collect different information about a person using several methods and at the same time all systems are shared. An overview of content-based, collaborative filtering and hybrid methods was performed. An overview of Alternating Least Squares and Singular Value Decomposition recommendation algorithms was performed. The design of the Recommender System of consumer goods software component was described. The main features of software implementation and programming tools for the system which is being developed were explained. The conclusions about the problems of Recommender Systems and the review of existing algorithms were made.Запропоновано дослідження проблеми створення рекомендацій, з технічним описом для побудови рекомендаційної системи для вибору товарів масового вжитку за допомогою сучасних алгоритмів, підходів, принципів і містить дослідження найбільш популярних методів. Було визначено, що впровадження рекомендаційних систем є однією з областей, які швидко розвиваються для вдосконалення прикладних інформаційних технологій, інструментів для автоматичного генерування пропозицій, заснованих на дослідженні особистих потреб і профілю клієнтів. Було досліджено, що такі системи почали грати дуже важливу роль в швидко зростаючому Інтернеті, оскільки вони допомагають користувачам орієнтуватися у великій кількості інформації, користувачі не можуть аналізувати великий обсяг інформації, адже це дуже складно і також вимагає багато часу і зусиль, але завдяки рекомендаційним системам, які можуть фільтрувати великий обсяг інформації і надавати користувачам інформацію і рекомендації, які їм подобаються, проблема може бути вирішена і замість надання статичної інформації, коли користувачі шукають, і можливо, купують продукти, такі системи збільшують ступінь інтерактивності для розширення можливостей, що надаються користувачеві. Було визначено, що рекомендаційні системи формують рекомендації самостійно для кожного конкретного користувача на основі минулих покупок і пошуків, а також на основі поведінки інших користувачів за допомогою служб рекомендацій, які збирають різну інформацію про людину, що використовує кілька методів, і в той же час всі системи є загальними. Було проведено огляд методів фільтрації на основі контенту, спільної фільтрації і гібридних методів. Було виконано огляд алгоритмів альтернативних найменших квадратів і сингулярного розкладання. Описана конструкція рекомендаційної системи програмного забезпечення для вибору товарів масового вжитку. Зроблено пояснення деяких можливостей програмної реалізації і інструментів програмування для розроблюваної системи. Зроблено висновки про проблеми рекомендаційних систем і огляд існуючих алгоритмів

The Role of the Mangement Sciences in Research on Personalization

We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,

Link Prediction in Complex Networks: A Survey

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure

Personalized Music Recommendation Based on Style Type

As Internet industry constantly develops and the computer penetration rate continues to grow, the number of online music platforms and music users has been able to increase year by year. With that comes more music choices, information overload has become a very prominent problem. Therefore, how to make users choose their favorite music more conveniently is one of the most challenging problems faced by online music recommendation systems. This paper bases on the existing recommendation system research and uses the collaborative filtering algorithm, proposes a music recommendation method from three perspectives: user attributes, music types and time migration. It is found that the online music recommendation from these three perspectives has a good effect, which can provide a reference for the construction of the current online music recommendation system and is also helpful to platform management practice

Recommendation Systems: Decision Support for the Information Economy

Information Systems Working Papers Serie