Design Preference Elicitation, Identification and Estimation.

Understanding user preference has long been a challenging topic in the design research community. Econometric methods have been adopted to link design and market, achieving design solutions sound from both engineering and business perspectives. This approach, however, only refines existing designs from revealed or stated preference data. What is needed for generating new designs is an environment for concept exploration and a channel to collect and analyze preferences on newly-explored concepts. This dissertation focuses on the development of querying techniques that learn and extract individual preferences efficiently. Throughout the dissertation, we work in the context of a human-computer interaction where in each iteration the subject is asked to choose preferred designs out of a set. The computer learns from the subject and creates the next query set so that the responses from the subject will yield the most information on the subject's preferences. The challenges of this research are: (1) To learn subject preferences within short interactions with enormous candidate designs; (2) To facilitate real-time interactions with efficient computation. Three problems are discussed surrounding how information-rich queries can be made. The major effort is devoted to preference elicitation, where we discuss how to locate the most preferred design of a subject. Using efficient global optimization, we develop search algorithms that combine exploration of new concepts and exploitation of existing knowledge, achieving near-optimal solutions with a small number of queries. For design demonstration, the elicitation algorithm is incorporated with an online 3D car modeler. The effectiveness of the algorithm is confirmed by real user tests on finding car models close to the users' targets. In preference identification, we consider designs as binary labeled, and the objective is to classify preferred designs from not-preferred ones. We show that this classification problem can be formulated and solved by the same active learning technique used for preference estimation, where the objective is to estimate a preference function. Conceptually, this dissertation discusses how to extract preference information effectively by asking relevant but not redundant questions during an interaction.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91578/1/yiren_1.pd

Tag based Bayesian latent class models for movies : economic theory reaches out to big data science

For the past 50 years, cultural economics has developed as an independent research specialism. At its core are the creative industries and the peculiar economics associated with them, central to which is a tension that arises from the notion that creative goods need to be experienced before an assessment can be made about the utility they deliver to the consumer. In this they differ from the standard private good that forms the basis of demand theory in economic textbooks, in which utility is known ex ante. Furthermore, creative goods are typically complex in composition and subject to heterogeneous and shifting consumer preferences. In response to this, models of linear optimization, rational addiction and Bayesian learning have been applied to better understand consumer decision- making, belief formation and revision. While valuable, these approaches do not lend themselves to forming verifiable hypothesis for the critical reason that they by-pass an essential aspect of creative products: namely, that of novelty. In contrast, computer sciences, and more specifically recommender theory, embrace creative products as a study object. Being items of online transactions, users of creative products share opinions on a massive scale and in doing so generate a flow of data driven research. Not limited by the multiple assumptions made in economic theory, data analysts deal with this type of commodity in a less constrained way, incorporating the variety of item characteristics, as well as their co-use by agents. They apply statistical techniques supporting big data, such as clustering, latent class analysis or singular value decomposition. This thesis is drawn from both disciplines, comparing models, methods and data sets. Based upon movie consumption, the work contrasts bottom-up versus top-down approaches, individual versus collective data, distance measures versus the utility-based comparisons. Rooted in Bayesian latent class models, a synthesis is formed, supported by the random utility theory and recommender algorithm methods. The Bayesian approach makes explicit the experience good nature of creative goods by formulating the prior uncertainty of users towards both movie features and preferences. The latent class method, thus, infers the heterogeneous aspect of preferences, while its dynamic variant- the latent Markov model - gets around one of the main paradoxes in studying creative products: how to analyse taste dynamics when confronted with a good that is novel at each decision point. Generated by mainly movie-user-rating and movie-user-tag triplets, collected from the Movielens recommender system and made available as open data for research by the GroupLens research team, this study of preference patterns formation for creative goods is drawn from individual level data