Abstract— Grocery Shopping is considered a tedious and less interesting task by many yet a decisive activity to be undertaken as it is a vital part in human lifestyle. Simple techniques are used in assisting to carry out these activities, some of them being taking down the items to be purchased on a paper or on the mobile device or most commonly creating a mental list. This research introduces ‘The Smart Shopping List’, a mobile software solution which enables the users to perform their grocery shopping experience with the ease of overcoming the above complications. The Application consists of several modules; Interactive Shopping List where the user can add/remove/cross items, Shop Locator which assist the user to find the ideal supermarket to go to so that most of the items can be bought in one place, Items Recommender powered with Apriori algorithm to remind the user of any possible missing items or items he may be interested in and ‘BringMe!’, which is a text-to-app feature to share the shopping lists between the users. Upon the implementation, it was observed that very healthy results were developed from the data mining algorithm from the correlated data, as well as strong usability feedback given by the users showcasing 82% of beta users being benefited from the solution. Above discussed context has been a strong background which reasons for ‘The Smart Shopping List’ to become a challenging project and as a novel concept not only the in the field of Social lifestyle but also in terms of Data Mining, Geo-Location and mobile technology as well. Keywords— list-creation process; geolocation services; data mining; apriori algorithm; mobile technology; usability

I.INTRODUCTION

Software solutions based on mobile platforms have become very popular in today’s technology world in a massive scale [1] and are used in vivid range of world domains. Not only mobile applications have become a lifestyle [2] but also the technology related is evolving by the day and demonstrating the promising potential to undertake most of the human technological necessities in a comprehensive manner. Similarly, data mining is also a broad concept which is employed in almost every field in the world [3], heavily in data sciences. Whilst applying the practicality of data mining on sales and consumer data is prevalent, in identifying purchase and interest patterns, association and association rules, is widely taken into consideration. Although a substantial amount of applications have been produced in various mobile platforms for online grocery shopping [4][5] and comprehensively data mining techniques have been applied on sales data for interest mining, the requirement has always been present for a solution to enhance static grocery shopping experience of people. Up to the present time people use traditional ways of carrying out these activities such as writing on a piece of paper [6] or memorizing items to be bought [7] which are not reliable and eventually leads to time and money loss. Coming up with a solution which has a capacity of properly addressing the above issues would be much beneficial for the users in terms of both financially and lifestyle improvement. This research presents our work on a mobile-assisted software application namely ‘The Smart Shopping List’ for enabling the users to enrich their grocery shopping experience in to a new level. It allows the users to create shopping lists, find the ideal shop using geolocation services, receive item recommendations filtered using pattern matching recognition with higher level of accuracy using association algorithms, and share shopping lists via text-to-app service. The experimental runs and the comparisons with Weka software have shown promising results in recommending items to the user.

II.HUMAN GROCERY SHOPPING BEHAVIORS

Grocery shopping and human lifestyle has always been an interesting domain basis for many researches as the importance has significantly arisen over the past several decades to unveil the correlation between the human behavioral patterns and sales. Some statistical studies are based on data for a specific region while others discuss about concept in a broader magnitude. These behavioral analyses reveal fascinating facts which are useful information to identify process oriented requirements and possible improvements which can be made to enrich the user experience.

A.Family household and Grocery Store A survey has been conducted by Bassett R., Beagan B. and Chapman G. observing the connection between family household and grocery store [8]. They discuss about how women are more involved in a grocery shopping process and how shopping lists mask a host of activities and tasks that are undervalued because they are unseen and unrecognized. Their findings include that most of the families create a shopping list while over half of them wrote a list and took it with them on their grocery trips, and some individuals maintain a mental list instead of a physical list. There were also a set which made use of a combination of a written list and a mental list and also a set of non-list members. B.Grocery Shopping Trends in the U.S. A very statistical group of analysis have been made by the joint effort of Hartman Group and FMI (Food Marketing Institute) on the grocery shopping trends in the United States. One of their recent report, ‘U.S. Grocery Shopping Trends 2014 Overview’ provides key insights on American grocery shopping behavior which most of them can be applied in a global scale [9]. The survey indicates that the shoppers are becoming less likely to choose any one store to satisfy all their needs. Shoppers are optimizing their satisfaction store by store and by department. This is a strong point which hints out of a clear deviation from the ‘primary store’ concept which had been the de facto for last few decades. Shoppers now are considering different options to carry out their grocery shopping rather than sticking to one go-to store. Having a mechanism to find new stores nearby would greatly help the shoppers and improve the quality of the process as well. It further finds that women are the major part of the household grocery shopping but men are actively engaged in the process as well. The study further converses shoppers grocery list making habits. While many shoppers build shopping lists, it was evident that the young adult shoppers wait until the last minute to build their grocery lists. This information clearly demonstrates that if a grocery shopping assisting solution is to be implemented, accessibility and efficiency have to be thoroughly considered as its core feature. If a shopper was to create a shopping list in the last minute and the suggested software application does not allow them to perform their tasks quickly and efficiently with a very accessible manner, the likelihood of the proposed solution becoming successful is slim. It was also explored by the same study that Millennials (or Gen Y) would not only make a grocery list in the last minute but also are not particularly interested in relying on a fixed set of list items. For younger generations in particular, planning for a shopping trip is much more likely to be about building a meal or other eating occasion rather than stocking up the pantry with a list of basics and trusted items that a meal can be built from later. Hence it was apparent that they are willing to broaden their grocery criteria with a variety of items as long as they are useful to their needs. Item suggestion or recommendation assisting method would certainly help in a case similar to this. C.Cost vs Quality Another study by Arnaud, A. and the team, also shares a similar proposition in terms of the shopping behavior of the millennials [10]. Their study reveals that the Gen Y prefers to buy cheaper items but is willing to pay for fresh and quality items. This explains that most individuals may be interested in good deals or promotions available at the store. Arnaud further explains his findings on the reasons as for why millennials would use a shopping list. Just to name a few; to save time in the store, to save as memory aids, to control the expenditure and as a goal achievement purpose.

III.RELATED WORK

There have been several technological developments in mobile-assisted shopping in the past few decades. Today’s reach of mobile smartphones and the availability of the mobile development technology have certainly contributed these innovative advancements. Below are some notable studies and improvements carried out on mobile-assisted shopping-lists creation. A.The Hybrid Shopping List Heinrichs F., Schreiber D. and Schöning J. under the patronage of Prof. Dr. Antonio Krüger, worked on a project to create a prototype for a hybrid mobile application combining the advantages of paper and electronic shopping lists [11]. An initial study was carried out to understand the creation and usage of paper based shopping lists. Then a secondary investigation was made to identify the properties of electronic shopping lists. Findings which were extracted from the initial study put together with the secondary analysis, a functional matrix was created to design the prototype. Prototype was a system which uses Client-server architecture with a Mobile GUI. Users would write the grocery list using a digital pen and the activity would be captured and be available at the mobile GUI. The project laid paved the way for mobile assisted grocery shopping creation process, however further studies were much required to investigate how many other modalities than pen and paper can be applied and facilitated in the shopping list creation process. B.Intelligent Shopping List Marcus Liwicki and the three team members (Sandra Thieme, Gerrit Kahl, and Andreas Dengel) developed a system which automatically extracts the intended items for purchasing from a handwritten shopping list [12]. This intelligent shopping list relies on a categorization of the products which is provided by the supermarket. The system identifies handwritten items in a shopping list by the use of a digital paper. Using the data transmitted to computer the handwritten data is understood by matching the data against an ontology. Promising results were shown however since the ontology is provided by the store and very specific and narrow, it has to be enlarged so that the users are able to any items they prefer. C.Multimodal shopping lists Another interesting development would be the work concluded by Jain and the team regarding a developing a prototype for creating a shopping list from multiple source devices like desktop, smart phones, landline or cell phone in different formats, essentially structured text, audio, still images, video, unstructured text and annotated media [13]. An evaluation was done with 10 participants in two week. Their goal was to further analyze the shopping list creation and management process. Based on their findings they give the recommendations to develop interactive features for the systems made for managing shopping lists. D.Grocery Retrieval System & Mobile services Similar to Marcus Liwicki’s study, Nurmi and the group introduce a product retrieval system that maps the content of shopping lists written in natural language into the relevant real world products in a supermarket [14]. The system was developed having shopping basket data as the base which they had gathered from a large local Finnish supermarket. Furthermore the new architecture designed by Wu H. and Natchetoi Y. enabling efficient integration between mobile phone applications and Web Services with the help of XML compression features [15]. Using this architecture, they have implemented a mobile shopping assistant which has multiple input modes such as camera, voice and Bluetooth. While there are still more promising work to be done to further improve the framework, they conclude their study with their plan of releasing the work to the public as a generic library. Interestingly, most of the applications focused on improving the input method to effectively create a shopping list, while none of them have taken a look at the broader view on the shopping list creation process and the consumer patterns to further enrich the shopping list creation experience, such as applying data mining techniques for interest mining.