CHIC: A Combination-based Recommendation System

Manasi VartakCSAIL, Massachusetts Institute of Technology

mvartak@mit.edu

Samuel MaddenCSAIL, Massachusetts Institute of Technology

madden@csail.mit.edu

ABSTRACTCurrent recommender systems are focused largely on recommend-ing items based on similarity. For instance, Netflix can recom-mend movies similar to previously viewed movies, and Amazoncan recommend items based on ratings of similar users. Althoughsimilarity-based recommendation works well for books and movies,it provides an incomplete solution for items such as clothing or fur-niture which are inherently used in combination with other itemsof the same type, e.g., shirt with pants, and desk with a chair. As aresult, the decision to buy a clothing or furniture item depends notonly on the item itself, but also on how well it works with otheritems of that type. Recommending such items therefore requires acombination-based recommendation system that given an item, cansuggest interesting and diverse combinations containing that item.This problem is challenging because features affecting combinationquality are often difficult to identify; quality, being a function of allitems in the combination, cannot be computed independently; andthere are an exponential number of combinations to explore. In thisdemonstration, we present CHIC, a first-of-its-kind, combination-based recommendation system for clothing. The audience will in-teract with our system through the CHIC mobile app which allowsthe user to take a picture of a clothing item and search for interest-ing combinations containing the item instantly. The audience canalso compete with CHIC to create alternate ensembles and comparequality. Finally, we highlight via visualizations the core modules ofCHIC including model building and our novel search and classifi-cation algorithm, C-Search.

Categories and Subject DescriptorsH.4.0 [Information Systems]: Information Systems Applications-General

General TermsDesign

KeywordsRecommendation, Combination

1. INTRODUCTIONRecommendation systems are widely used by e-commerce web-

sites with the goals of reducing information overload, convertingbrowsers to buyers and cross-selling by suggesting additional itemsto buy [10]. Most state-of-the-art recommender systems are fo-cused on recommending items based on item or user similarity.For example, Netflix can recommend movies similar to previously-viewed movies, Amazon can recommend items based on ratings ofsimilar users, and Pandora suggests music based on music youveliked before. Although these systems work well for items such asbooks, music and movies, they provide an incomplete solution foritems such as clothing or furniture that are used in combination withother items of the same type. In such cases, the aesthetic appeal ofan item depends on the items it is paired with, e.g., a shirt looksgood with matching jacket and pants, while a sofa may go with amatching chair. As a result, the decision to buy a clothing or furni-ture item depends not only on the item itself, but also on how wellit works with other items of that type. Therefore, recommendingsuch items requires, in addition to similarity-based recommenda-tion, a combination-based system that given an item, can suggestinteresting and diverse combinations containing that item.

1.1 Motivating Examples

Example 1. Alice is shopping on Amazon.com for a new sweater,and has identified a few sweaters that meet her criteria of color, sizeand price. While current recommendations shown to Alice (Figure1) offer her other options based on top-selling sweaters, similarsweaters and items bought by similar customers, Alice is offered noinsight into how the sweater could be paired with other items fromthe store (or items she owns) or if she could buy accessories for amore aesthetically appealing combination.

While it might seem that Items frequently bought together, i.e.frequent item-sets, would be able to provide combination-basedrecommendations, as highlighted in Figure 1, the recommenda-tions are merely of similar clothing items instead of interestingensembles of clothes. Without a clear demonstration of how thesweater might add to her existing set of clothes, Alice may decidenot to purchase the item, leading to lost potential sales of not onlythe sweater but also accessories that would have been cross-soldthrough ensembles.

Example 2. James is choosing furniture for this new home andvisits Ikea to choose a sofa and coffee table. He has some fixedcriteria such as material, price and dimensions while buying theseitems. However, even when an item meets the above criteria, the de-cision to buy the furniture strongly depends on whether the new sofa

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.SIGMOD13, June 2227, 2013, New York, New York, USA.Copyright 2013 ACM 978-1-4503-2037-5/13/06 ...$15.00.

981

Figure 1: Recommendations for similar items, highly-rateditems and items bought together

and coffee table will match existing furniture like chairs and car-pets, and co-ordinate with wall colors. As a result, combination-based recommendation can successfully be used to help James iden-tify the right furniture items.

In this demonstration, we focus on the problem of combination-based recommendation for clothing items.

1.2 Problem DescriptionE-commerce websites can significantly increase the conversion

rate from browsers to buyers and increase cross-selling by imple-menting a combination-based recommendation system. We dividethe problem of recommending combinations or ensembles into twosub-problems: (a) model building, and (b) searching and classify-ing combinations based on quality. The goal of the former prob-lem is to build an accurate model predicting the interesting-nessa combination of items. For the purpose of this work, we frameproblem (a) as a binary classification problem where we model thequality of a combination as a function of the hybrid features of thatcombination (such as degree of color matching and contrast, degreeof texture diversity etc). We can then formally describe the searchand classification problem (b) as follows: given an item, find k highquality combinations containing that item, where quality is deter-mined by the classification model built in part (a).

Combination-based recommendation is a challenging problemfor several reasons. First, it is extremely difficult to accurately iden-tify features affecting combination quality. Second, since combina-tion quality is a function of all constituent items of the combination(e.g., degree of color match, weather appropriateness), it cannotbe computed independently for each item. And third, there are anexponential number of combinations to explore, making the naivesolution computationally intractable.

1.3 State-of-the-Art ApproachesThe problem of processing images of clothing items to find simi-

lar items has been explored in the computer vision literature throughwork such as [4]. Similarly, there has been some work on perform-ing matching of clothing items on a limited scale. [3, 6] proposetechniques based on tags to identify matches of clothes, while [13]proposes a technique to perform clothes matching (based on colorand texture) for the blind. These systems rely on simple item-matching models, and it is unclear how they could be scaled to

large datasets or multiple categories of items. Finally, [9] focuseson identifying the latest clothing trends by comparing pictures forsimilar features. [11] designed a system that suggests outfits basedon occasion. Polyvore1 is a popular fashion platform that allowsusers to manually mix and match diverse items to create interestingsets. However, Polyvore does not actually perform recommenda-tions of matched sets of items.

Combination-based recommendation is related to traditional rec-ommendation algorithms such as collaborative filtering, knowledge-based recommendation and content-based recommendations [7].Similarly, there has been seminal work in the database commu-nity on mining frequent itemsets [1], but as noted above, these al-gorithms do not correctly address the problem of producing high-quality combinations. Finally, combination-based recommendationis also related to the top-k ranking problem. While several efficienttop-k algorithms have been proposed [5, 8, 2], including one forgroups [12], these algorithms do not address the unique challengesposed by combination-based recommendations.

In this demonstration, we present CHIC, a first-of-its-kind, com-bination based recommendation system for clothing.

2. CHIC OVERVIEWCHIC produces high-quality recommendations through a two

step process: first, it uses a crowd-sourced and web-scraped datasetto learn a predictive model for computing the quality of combina-tions; then, it combines the predictive model with our novel algo-rithm, called C-Search, to efficiently compute and classify combi-nations. Figure 2 shows the system architecture of our system.

Model Building

Process individual images

Select, compute combina8on features

C-Search

Clothing Items DB

Pre-computa8on of item-pairs

Itera8ve Search and Classifica8on

Classifica3on Model

User Constraints: Price,type, color

iQ = { }

Clothing Items Ra3ngs for combina3ons

Train Classifica3on Model

High-quality combina3ons with iQ

Figure 2: CHIC: System Architecture

2.1 Model BuildingThe model underly