=Paper=
{{Paper
|id=Vol-478/paper-1
|storemode=property
|title=Ma$$ive ─ An Intelligent Shopping Assistant
|pdfUrl=https://ceur-ws.org/Vol-478/paper1.pdf
|volume=Vol-478
|dblpUrl=https://dblp.org/rec/conf/um/ForsblomNFPS09
}}
==Ma$$ive ─ An Intelligent Shopping Assistant==
https://ceur-ws.org/Vol-478/paper1.pdf
Ma$$ive – An Intelligent Shopping Assistant
Andreas Forsblom1 , Petteri Nurmi1 , Patrik Floréen1 , Peter Peltonen2 , Petri
Saarikko2
1
Helsinki Institute for Information Technology HIIT
PO Box 68, FI-00014 University of Helsinki, Finland
firstname.lastname@cs.helsinki.fi
2
Helsinki Institute for Information Technology HIIT
P.O. Box 9800, FI-02015 Helsinki University of Technology TKK, Finland
firstname.lastname@hiit.fi
Abstract. Our intelligent shopping assistant, Ma$$ive, is a web appli-
cation for mobile phones that aims to help users with their everyday
grocery shopping by offering features such as natural language shop-
ping lists, product recommendations, special offers, recipes and in-shop
navigation. This short paper briefly describes the current and planned
features of Ma$$ive.
Introduction
Grocery shopping is one of the most fundamental everyday activities. For most
customers a shopping list is an integral part of the shopping experience. Studies
have suggested that shopping lists serve, e.g., as memory aids [1], as a tool for
budgeting and as a way to efficiently organize routine shopping visits [2]. The
central role of a shopping list is also highlighted by a study on mobile retailing
where potential customers assigned highest priority to features that help them
create and manage shopping lists [3].
Retailing provides an interesting domain for ubicomp applications, not least
because of the domain’s large business potential. Many proposed ubiquitous
retailing applications are based on instrumented shopping assistants (e.g., an
intelligent shopping cart) and also often rely on RFID product identification,
see e.g., [4, 5]. A user study by Newcomb et al. [3] suggests that users prefer ap-
plications they can use on their personal devices. Thus, our focus is on assistants
that run on a PDA or a mobile phone.
We have developed a mobile shopping assistant, Ma$$ive, that runs on mo-
bile phones. Ma$$ive is as a web application, and can thus in principle run on
any mobile device with an AJAX capable browser, but so far we have focused
our development on the Nokia E61i, which is a 3G mobile phone equipped with
a QWERTY keyboard and WiFi (Fig. 1). In the following sections, we briefly
describe the current and planned features of Ma$$ive.
Fig. 1. Photo of Ma$$ive’s shopping list feature in use on a Nokia E61i mobile phone.
Natural Language Shopping Lists
Contrary to previous shopping assistants, Ma$$ive allows users to write shop-
ping lists using natural language and without requiring any predefined product
taxonomy.
Whereas customers tend to use natural language for describing products,
grocery stores use product specific information. In order to provide information
about product offers, location of products etc., the natural language entries in
shopping lists need to mapped into products in a store. As part of Ma$$ive, we
have developed a grocery retrieval engine that supports this task. User evalua-
tions have indicated that our retrieval engine can determine appropriate products
for approximately 80% of shopping list entries. More details about the retrieval
engine and its evaluation are given in [6] and [7].
In order to speed up text entry on mobile devices, we have integrated pre-
dictive text input that uses association rules mined from a large amount of
shopping data in order to rank the suggestions made by the system, based on
the user’s current shopping list. For example, if the user’s shopping list already
contains ice cream, chocolate sauce might get ranked higher because of a rule
ice cream ⇒ chocolate sauce. This leads to the correct item being suggested
after fewer key presses when compared to traditional frequency based systems.
We conducted a user study, and found that text input speed increased by ap-
proximately 40%, while error rates dropped by over 80% compared to the non-
predictive version [8].
Product Recommendations
In order to make personalised product recommendations, we have combined our
grocery retrieval engine with generalized association rules mined from a large
set of shopping data. We first use the grocery retrieval engine to translate the
natural language items on the user’s shopping list into actual products in the
store. We then look for the actual products, and their parent categories, in the
antecedents of a large set of generalized association rules. The scores from the
grocery retrieval engine are combined with the interest of the association rules
in order to rank the recommendations. Details about the implementation and
user study results can be found in [9].
Special Offers
In a survey on feature preferences that we conducted in our partner supermarket,
features that could help the customer to save money, e.g., price comparison and
special offers, were ranked the highest. To this end, Ma$$ive can display all of
the current special offers in the supermarket. In the future, we plan to sort and
rank these based on the users’s interests and current shopping list. We could
also suggest recipes based on what’s on special offer.
Recipe Support
A common scenario is that a person is doing his or her grocery shopping after
work, but is unsure of what to cook. The person might have some left over ham
in the fridge, and would like to make use of it. In this scenario, the person could
use Ma$$ive ’s recipe search feature to look for recipes containing ham. The
ingredients can then easily be added to the shopping list if desired.
In the future, we plan to detect and recommend recipes based on the items
on the shopping list. If it seems from already selected items that a certain dish
is to be prepared, the missing ingredients could be suggested to the customers
as reminders: ”Do you also need eggs?”
Future Work
Shop Navigation
A common task for shoppers is to find a particular product. To facilitate this
task, we plan to integrate navigation support into Ma$$ive. We will implement
different ways of providing the information and evaluate these ways with real
customers: textual information about where to find the product (e.g., aisle num-
ber), a map view of the shop, pictures showing the direction of where to go,
and providing directions using landmarks (”go towards the meat desk”) with
optional voice output. The user is located using a commercial WiFi positioning
engine.
Location-Triggered Advertisements
Our plan is to integrate the product recommendations with advertisements. First
of all, matching the recommendations against a database of current product
offers makes it possible to rank the advertisements based on how interesting
they are to the user. Secondly, the location of the user can be used to trigger
the advertisements when the user is near the corresponding products.
Concluding Remarks
Developing Ma$$ive as a web application has proven to be challenging due to
unpredictable browser behaviour and very limited debugging options. So far we
have been able to work around these limitations, and we expect the situation to
improve rapidly as mobile web applications become more and more popular.
Acknowledgements
The authors are thankful to their present and past colleagues in the project.
This work was supported by the Finnish Funding Agency for Technology and
Innovation TEKES, under the project Personalised Ubiservices in Public Spaces.
The work was also supported in part by the ICT program of the European
Community, under the PASCAL2 network of excellence, ICT-216886-PASCAL2.
The publication only reflects the authors’ views.
References
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(2009) Accepted for publication.