Implications of Perceived Utility on Individual Choice and Preferences: A New Framework for Designing Recommender System

ABSTRACT Consumer psychology and consumer behaviour has constantly been a field of interest for the researchers. Volumes of researches are available in this area, still new theories and concepts keep emerging in response to change in context and environment. Consumer psychology has large cross disciplinary implications. In this paper, a relationship between consumer psychology and recommender system has been explained and how implementing a consumer psychology model can improve recommender system performance. This paper also gives first level analysis of various filters that can be developed along with the design of recommender system in order to generate a more refined and relevant choice sets. There are attraction effects observed among different items and the attractiveness of a product is codependent on attractiveness of other options available in a choice set. In the present paper, we have explored the utility of defender model in the design of recommender systems. Different effects like decoy and asymmetric dominance are also analyzed to achieve better efficiency and effectiveness in the design of recommender system. Keywords: Decoy, Recommender, Defender Model, Asymmetric Dominance INTRODUCTION Recommender systems are the category of information systems which supports searching and suggesting products and services according to user's requirement and preferences (Resnick et al., 1997). The product range may vary from consumer durables such as a book in Amazon or digital goods such as song on lastfm. The task of recommending products proactively is performed by recommender systems which work backstage on E-commerce websites. Many recommendation approaches and methods are applied to target internet based consumers. Collaborative filtering was earliest approach to create user to user similarity and then recommend similar products. Recommendation approach was simple. Positively rated items by one set of user were recommended to similar users. However, this 'user to user' correlation lead to scarce data and cold start problems (Ahn, 2008). This problem was targeted by designing an 'item to item' based correlation and this category of algorithms were termed as content based filtering as volume of data available for products was much denser than user data. There were some specialized variations also for example demographic or knowledge based recommender system which includes domain based knowledge to analyze user preference and predict user choices. As it is evident that recommender system directly interact with consumer and for establishing a better communication it is essential that recommender systems should be intelligent enough to understand consumer's behavioral traits(Pu et al., 2011). Understanding consumer psychology and its phenomenon will actually present meaningful insights to improve recommendations and effectiveness of these systems which in turn can be translated into economical benefits. The purpose of the recommender system is not only to elicit preference and produce accurate search results but also to improve the decision making in an ecommerce context (Xiao et al., 2007). A good amount of research in the recommender system focuses on developing complex algorithms and software programs which improves the efficiency of recommender systems (Cosley et al., 2003). However, there is a need to identify other significant aspects which attempts to maximize the value derived from a recommender system experience (Xiao et al., 2007). Factors such as trust on online medium (Yaobin et al., 2007) and personal choices (Ho et al., 2008) are often ignored in the design and implementation process. In the present paper, we propose a conceptual framework based on established theories from the field of consumer psychology. The proposed framework allows the systems designer to incorporate the significance of derived value of a given product/service in order to achieve the single most important objective of any decision behaviour-maximizing utility. …