Page 29 |
Save page Remove page | Previous | 29 of 171 | Next |
|
small (250x250 max)
medium (500x500 max)
Large (1000x1000 max)
Extra Large
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
|
17
105]. Model-based collaborative filtering uses training examples to generate a model
that predicts the preferences for the new items. Different models, such as decision
trees [16], latent semantic models [58], and factor analysis models [20], are used in the
model-based filtering research.
Collaborative filtering has been used in many commercial applications,
particularly in sales and marketing. However, collaborative filtering requires a
relatively large number of individual opinions to be effective, more than is typically
on a small design team.
This research introduces a new approach to extract preference information in
terms of preferential probabilities from the transcribed discussion of design teams that
does not require individual designers to explicitly state their preferences as in other
methods. It assumes that designers’ preferences are somewhat related to what
designers discuss during the design process, and this kind of relationship can be
modeled in probabilistic ways. In this way, the process of eliciting preferential
probabilities will be less disruptive to the team, and may potentially be more accurate.
2.3.2 Group Preference Aggregation
Designer preferences are often considered for a whole team rather than the
individual member alone, and several approaches exist to aggregate group preferences.
Arrow’s Theorem [3, 4] demonstrates that there is no guarantee of consistency in a
group. It is reasonable that the team members may have different preferences, and it is
more important to consider the group preference rather than the individual preferences
Object Description
| Title | Extraction of preferential probabilities from early stage engineering design team discussion |
| Author | Ji, Haifeng |
| Author email | haifengj@usc.edu; haifeng.ji@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Industrial & Systems Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2008-08-19 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-10-07 |
| Advisor (committee chair) | Yang, Maria C. |
| Advisor (committee member) |
Lu, Stephen Jin, Yan |
| Abstract | Activities in the early stage of engineering design typically include the generation of design choices and selection among these design choices. A key notion in design alternative selection is that of preference in which a designer or design team assigns priorities to a set of design choices. However, preferences become more challenging to assign on both a practical and theoretical level when done by a group of individuals. Preferences may also be explicitly obtained via surveys or questionnaires in which designers are asked to rank the choices, rate choice with values, or select a "most-preferred" choice. However, these methods are typically employed at a single point of time; therefore, it may not be practical to use surveys to elicit a team’s preference change and evolution throughout the process.; This research explores the text analysis on the design discussion transcripts and presents a probabilistic approach for implicitly extracting a projection of aggregated preference-related information from the transcripts. The approach in this research graphically represents how likely a choice is to be "most preferred" by a design team over time. For evaluation purpose, two approaches are established for approximating a team's "most preferred" choice in a probabilistic way from surveys of individual team members. A design selection experiment was conducted to determine possible correlations between the preferential probabilities estimated from the team's discussion and survey ratings explicitly stated by team members. Results suggest that there are strong correlations between extracted preferential probabilities and team intents that are stated explicitly, and that the proposed methods can provide a quantitative way to understand and represent qualitative design information using a low overhead information extraction method. |
| Keyword | preferences; probabilities; concept selection; design process; design decision-making |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m1635 |
| Contributing entity | University of Southern California |
| Rights | Ji, Haifeng |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Ji-2413 |
| Archival file | uscthesesreloadpub_Volume14/etd-Ji-2413.pdf |
Description
| Title | Page 29 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 17 105]. Model-based collaborative filtering uses training examples to generate a model that predicts the preferences for the new items. Different models, such as decision trees [16], latent semantic models [58], and factor analysis models [20], are used in the model-based filtering research. Collaborative filtering has been used in many commercial applications, particularly in sales and marketing. However, collaborative filtering requires a relatively large number of individual opinions to be effective, more than is typically on a small design team. This research introduces a new approach to extract preference information in terms of preferential probabilities from the transcribed discussion of design teams that does not require individual designers to explicitly state their preferences as in other methods. It assumes that designers’ preferences are somewhat related to what designers discuss during the design process, and this kind of relationship can be modeled in probabilistic ways. In this way, the process of eliciting preferential probabilities will be less disruptive to the team, and may potentially be more accurate. 2.3.2 Group Preference Aggregation Designer preferences are often considered for a whole team rather than the individual member alone, and several approaches exist to aggregate group preferences. Arrow’s Theorem [3, 4] demonstrates that there is no guarantee of consistency in a group. It is reasonable that the team members may have different preferences, and it is more important to consider the group preference rather than the individual preferences |
