Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Evaluating Top-k Selection Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
RankSQL: query algebra and optimization for relational top-k queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
What am I gonna wear?: scenario-oriented recommendation
Proceedings of the 12th international conference on Intelligent user interfaces
Supporting top-K join queries in relational databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Probabilistic top-k and ranking-aggregate queries
ACM Transactions on Database Systems (TODS)
Clothes matching for blind and color blind people
ICCHP'10 Proceedings of the 12th international conference on Computers helping people with special needs
Recommender Systems: An Introduction
Recommender Systems: An Introduction
Fashion coordinates recommender system using photographs from fashion magazines
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Current recommender systems are focused largely on recommending items based on similarity. For instance, Netflix can recommend movies similar to previously viewed movies, and Amazon can recommend items based on ratings of similar users. Although similarity-based recommendation works well for books and movies, it provides an incomplete solution for items such as clothing or furniture which are inherently used in combination with other items of the same type, e.g., shirt with pants, and desk with a chair. As a result, the decision to buy a clothing or furniture item depends not only on the item itself, but also on how well it works with other items of that type. Recommending such items therefore requires a combination-based recommendation system that given an item, can suggest interesting and diverse combinations containing that item. This problem is challenging because features affecting combination quality are often difficult to identify; quality, being a function of all items in the combination, cannot be computed independently; and there are an exponential number of combinations to explore. In this demonstration, we present CHIC, a first-of-its-kind, combination-based recommendation system for clothing. The audience will interact with our system through the CHIC mobile app which allows the user to take a picture of a clothing item and search for interesting combinations containing the item instantly. The audience can also compete with CHIC to create alternate ensembles and compare quality. Finally, we highlight via visualizations the core modules of CHIC including model building and our novel search and classification algorithm, C-Search.