Mining Associations for Interface Design

Abstract

Abstract. Consumer research has indicated that consumers use compensatory and non-compensatory decision strategies when formulating their purchasing decisions. Compensatory decision-making strategies are used when the consumer fully rationalizes their decision outcome whereas non-compensatory decision-making strategies are used when the consumer considers only that information which has most meaning to them at the time of decision. When designing online shopping support tools, incorporating these decision-making strategies with the goal of personalizing the design of the user interface may enhance the overall quality and satisfaction of the consumer's shopping experiences. This paper presents work towards this goal. The authors describe research that refines a previously developed procedure, using techniques in cluster analysis and rough sets, to obtain consumer information needed in support of designing customizable and personalized user interface enhancements. The authors further refine their procedure by examining and evaluating techniques in traditional association mining, specifically conducting experimentation using the Eclat algorithm for use with the authors' previous work. A summary discussing previous work in relation to the new evaluation is provided. Results are analyzed and opportunities for future work are described.

Evaluation of a Dominance-Based Rough Set Approach to Interface Design

“…The goal of the first phase was to use CRSA to discover the minimal amount of consumer information required to discern contrasting consumer groups [10,11]. The primary idea was to observe the possibility of reducing the amount of information required by consumers before personalized user interfaces are theoretically possible [11,13]. Results obtained in our original procedure are given in Table 3.…”

“…The primary idea of the second phase of our procedure was to gather information in support of designing enhanced search functionality to aid consumer decisionmaking strategies by designing display aids for compensatory (fully rationalized) and non-compensatory (bounded rationality [17]) decision-making processes [13]. Compensatory decision-making strategies are used when consumers fully rationalize their decision, evaluating all features regardless of their perceived importance.…”

“…Of the 56 participants, only 42 participants provided complete questionnaires. The information for these 42 participants formed the empirical foundations for the procedure designed [10,11,13].…”

“…CRSA, introduced by Pawlak [15], and traditional data mining algorithms, such as Apriori or Eclat [3], could assist in conducting these analyses and in developing a procedure to design, develop, and deploy an effective and highly efficient online shopping support system quickly so that consumers can benefit from enhancements sooner, without having to spend time developing their profiles [13]. However, CRSA and Apriori/Eclat may not always provide the best results, as consideration of preference-ordered data is not taken into account.…”

Data Mining System and Applications: A Review

“…In another application to design effective user interfaces for consumer information system data mining can be used effectively [35]. Consumers use compensatory and non-compensatory decision strategies when formulating their purchasing decisions.…”