What Am I Gonna Wear Today?

::Initiative

It happens before dinners, meetings, movies, visits, or even only a walk. We all ask ourselves the same questions almost everyday, and for all the activities we participate - "What am I gonna wear?" . It tends to be tricky or even bothersome for many of us to make the decisions, because what we wear reflects many clues that are important to our social interactions, such as manners, tastes, and how people we meet mean to us. Viewing this process computationally, we claim that deciding what to put on is in fact striding across the gap and searching for the match between two concepts: a) how the clothes express our characters, and b) how the events or people we are to meet mean to us. In this project, we attempt to apply commonsense computing techniques to achieve this concept-matching goal, and provide users helpful suggestions.

::Scenario

The scenario of using this system goes as follows. After providing a set of descriptions about the owned clothes and accessories, the user only needs to type in any sentences he/she wishes (e.g., "I am going to a dinner with my girlfriend's parents." or "I feel relaxing today."), and the system will find the most suitable choices accordingly. The fuller the wearables as well as the input sentences are described by the user, the more accurate the system can catches his/her needs, and the more helpful the suggestions will be.

::You Gotta Have Commonsense...

We use a Commonsense reasoning system to map between the goals stated by the user, and possible characteristics of the product that might be relevant. For example, the boss's birthday party suggests a higher value for the "formal vs. casual" attribute, than say, a child's birthday party. The five dimensions built particularly for describing the styles or functions of clothes/accessories are: luxurious<->simple, formal<->casual, sexy<->conservative, modern<->classic, and elegant<->vulgar. We weighed several common brands (e.g., sisley, nike) and wearables (e.g., jacket, glasses) in these dimensions, and also a word list that contains tens of words about events, places, and social relationships.Using the spreading activation technique, the concepts carried by the words in the word list are spread to all the other terms in ConceptNet, a semantic network that is developed based on an 800,000-sentence Common Sense knowledge base (OMCS), such that the system is capable of providing suggestions under general cases.

::A Different Recommender

Putting this idea in a broader perspective, online shoppers in all kinds of product areas are potentially benefited.

Although Electronic Commerce on the Web is thriving, they still have trouble finding products that will meet their needs and desires. AI has offered many kinds of Recommender Systems, but they are all oriented toward searching based on concrete attributes of the product (e.g. price, color) or the user (as in Collaborative Filtering). Our fashion recommendation system serves as a different recommender technique, namely, Scenario-Oriented Recommendation, which works even when users don’t necessarily know exactly what product characteristics they are looking for. It breaks down boundaries between products' categories, finds the "first example" for existing techniques like Collaborative Filtering, and helps promote independent brands.




::Demo

What am I gonna wear today?




::Paper

Edward Shen, Henry Lieberman, and Francis Lam (2007). What am I gonna wear?: Scenatio-Oriented Recommendation. In the proceedings of the International Conference on Intelligent User Interface, IUI 2007, Jan 28-31, 2007, Honolulu, Hawaii, USA. (.pdf)




::Press

"Program Assists the Fashion-Challenged." In Discovery News Channel (.html)




::Related Projects

EmpathyBuddy
Synaesthetic Recipes
ConceptNet




::Contact

edward or francis or lieber at media dot mit dot edu