Recommendation systems: a joint analysis of technical aspects with marketing implications

📝 Abstract

In 2010, Web users ordered, only in Amazon, 73 items per second and massively contribute reviews about their consuming experience. As the Web matures and becomes social and participatory, collaborative filters are the basic complement in searching online information about people, events and products. In Web 2.0, what connected consumers create is not simply content (e.g. reviews) but context. This new contextual framework of consumption emerges through the aggregation and collaborative filtering of personal preferences about goods in the Web in massive scale. More importantly, facilitates connected consumers to search and navigate the complex Web more effectively and amplifies incentives for quality. The objective of the present article is to jointly review the basic stylized facts of relevant research in recommendation systems in computer and marketing studies in order to share some common insights. After providing a comprehensive definition of goods and Users in the Web, we describe a classification of recommendation systems based on two families of criteria: how recommendations are formed and input data availability. The classification is presented under a common minimal matrix notation and is used as a bridge to related issues in the business and marketing literature. We focus our analysis in the fields of one-to-one marketing, network-based marketing Web merchandising and atmospherics and their implications in the processes of personalization and adaptation in the Web. Market basket analysis is investigated in context of recommendation systems. Discussion on further research refers to the business implications and technological challenges of recommendation systems.

💡 Analysis

In 2010, Web users ordered, only in Amazon, 73 items per second and massively contribute reviews about their consuming experience. As the Web matures and becomes social and participatory, collaborative filters are the basic complement in searching online information about people, events and products. In Web 2.0, what connected consumers create is not simply content (e.g. reviews) but context. This new contextual framework of consumption emerges through the aggregation and collaborative filtering of personal preferences about goods in the Web in massive scale. More importantly, facilitates connected consumers to search and navigate the complex Web more effectively and amplifies incentives for quality. The objective of the present article is to jointly review the basic stylized facts of relevant research in recommendation systems in computer and marketing studies in order to share some common insights. After providing a comprehensive definition of goods and Users in the Web, we describe a classification of recommendation systems based on two families of criteria: how recommendations are formed and input data availability. The classification is presented under a common minimal matrix notation and is used as a bridge to related issues in the business and marketing literature. We focus our analysis in the fields of one-to-one marketing, network-based marketing Web merchandising and atmospherics and their implications in the processes of personalization and adaptation in the Web. Market basket analysis is investigated in context of recommendation systems. Discussion on further research refers to the business implications and technological challenges of recommendation systems.

📄 Content

Recommendation systems: a joint analysis of technical aspects with marketing implications

Draft version Vafopoulos Michalis Mathematics Department, Aristotle University of Thessaloniki, Greece Oikonomou Michael Mathematics Department, Aristotle University of Thessaloniki, Greece

ACM: H.3.3; J.4
WSSC: webscience.org/2010/D.2.4; webscience.org/2010/E.1.1.1
AMS: 90B60; 91D30; 62P25;97M10; 97M70

Abstract In 2010, Web users ordered, only in Amazon, 73 items per second and massively contribute reviews about their consuming experience. As the Web matures and becomes social and participatory, collaborative filters are the basic complement in searching online information about people, events and products.
In Web 2.0, what connected consumers create is not simply content (e.g. reviews) but context. This new contextual framework of consumption emerges through the aggregation and collaborative filtering of personal preferences about goods in the Web in massive scale. More importantly, facilitates connected consumers to search and navigate the complex Web more effectively and amplifies incentives for quality. The objective of the present article is to jointly review the basic stylized facts of relevant research in recommendation systems in computer and marketing studies in order to share some common insights. After providing a comprehensive definition of goods and Users in the Web, we describe a classification of recommendation systems based on two families of criteria: how recommendations are formed and input data availability. The classification is presented under a common minimal matrix notation and is used as a bridge to related issues in the business and marketing literature. We focus our analysis in the fields of one-to-one marketing, network-based marketing Web merchandising and atmospherics and their implications in the processes of personalization and adaptation in the Web. Market basket analysis is investigated in context of recommendation systems. Discussion on further research refers to the business implications and technological challenges of recommendation systems.

Preface
Searching, social networking, recommendations in various forms, blogging and micro-blogging have become part of everyday life whilst the majority of business applications have migrated to the Web. Understanding and modeling this enormous impact of the Web in macro (e.g. [1]) and micro scale (e.g. [2], [3]) has become a major task for computer and social scientists. The trans-disciplinary field in this direction has been entitled “Web Science” and is focused in the significant reciprocal relationship among the social interactions enabled by the Web’s design, the scalable and open applications development mandated to support them, and the architectural and data requirements of these large-scale applications [4], [5], [6]. The Web “curves” physical time and space by adding flexibility, universality [7] and more available options [8], [9] and sources of risks [10]. At the current Web 2.0 era, Users can easily edit, interconnect, aggregate and comment text, images and video in the Web. Most of these opportunities are engineered in a distributed and self-powered level. In particular, recommendation systems have become mainstream applications in the Web with massive User participation affecting an important part of offline and online industries. During the last twenty years, research and practice on recommendation systems is growing in an increasing pace. This massification creates new business opportunities and challenging research issues in software development, data mining, design of better algorithms, marketing, management and related issues. User and business demands are now setting part of the research agenda in recommendation systems literature. Recently, new research communities (e.g. network analysis) from diverse fields have started to involve in the research of recommendation systems in order to understand the economic behavior of online consumers and its implications to business process and competition.
Computer science literature and related fields are often enriched by bibliographic reviews on the advancements of recommendation systems ([11] is the most recent). To the best of our knowledge, it does not exist an effort to jointly review the technical and business aspects of recommendation systems. Thus, the objective of the present article is to overview the main aspects of relevant research in recommendation systems both in computer and marketing studies in order to create a bridge and facilitate the sharing of common insights. The article is organized as follows. The first section is devoted in the description of the fundamental changes that the Web brings in the economy. Specifically, the role of recommendation systems is identified as an important part in the transition to more energetic and interdependent consumption patterns. The seco

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