Shopster – A Social Mobile App

Shopster would be an application capable of producing profit for its developers. After consideration of the possible revenue models, we decided that since we will categorise users to simple users and shop-owners, it would be a good idea to charge shop-owners with a membership fee. This would a sensible thing to do since, if a lot of users use Shopster, it would be a value for shop-owners to register as well and be able to advertise their offers in the system. Shop-owners would also have the ability to control a shop’s page within Shopster and therefore see which users “like” the shop.

Related to the previous point, Shopster developers could also charge shop-owners or potential companies for supplying them with anonymous statistics. For example, shop-owners might want to know whether more male or female users like their shops, the location of users who like their shops, what age groups are attracted by specific offers, etc. Of course, all these information would be given with privacy issues always in mind.

Finally, 3rd parties like websites, companies or other organisations might want to advertise their businesses in Shopster and this would generate extra revenue for Shopster developers.

It is therefore important that if we can find people to invest in Shopster, some of that money should be spend on advertising the application and attracting users to start with as well as contacting shop-owners, after a number of users start using the system, and convince them that it is worthwhile to become members in Shopster.

As explained in the features list published earlier, the Recommendation System will be a very important feature for Shopster. One of the kinds of recommendations that it will offer, will be offers indirectly recommended by a user’s top friends. To find a user’s top friends, we are planning to introduce a measure of similarity for calculating top similar friends. This can be done using the Pearson Correlation coefficient which is a neighbourhood based algorithm for comparing past ratings on the same offers by both users and producing a value between -1 and +1, which would be indicative of the strength of their relationship. After that, the top rated offers of each top friend will be recommended to the current user, considering that he/she has not already rated them. These recommendations will be available to the user when he clicks on the “Recommendations” tab in the Search screen, as shown in this mock-up screen.

If the user, however, does not have any friends yet or is a new user to the system, the recommendations offered in that tab will be the top ratings of the most trusted users in the system. A user will generally have a high trust rating if his ratings on offers are similar to the actual average rating of an offer but also on the amount of helpful comments he/she has posted in combination with the ratings he/she has received on offers posted by that user.

Another simpler type of recommendation will be the top rated recommendations of all users and this is also the 1st tab shown in the Search screen mock-up. Offers from shops that a user has “liked” (or in other words, subscribed to) can also be accessed in the 3rd tab of the search screen.

The next also very important recommendation algorithm will be the recommendations that a user will receive when viewing a specific offer about a product. We can use the Item-to-Item Collaborative Filtering approach for recommending similar or related products to the products described in the offer currently being viewed (just like Amazon’s item-to-item recommendation – users who bought this also bought that, etc).

Recommendation system is one of the most important features of Shopster.  Recommendations will be proposed to a user when he is viewing an offer and when he is searching for . There are many types of recommendations that our system will recommend. Some of them are (lets assume that the reader is viewing an offer about a laptop):

1. Offers about related products (e.g  the system will recommend offers about laptop cases, a mouse, etc. )
2. Offers about similar products (e.g the system will recommend other offers about laptops)
3. Other offers from the current shop
4. Offers liked by other friends (offers that friends of the user have rated highly)

A more detailed analysis of the recommendation system and algorithms to be used can be found here.

Every user will be able to rate the offers presented in our application. The combined rating from all offers of a user will be his/her rating, so other users will know if a he/she is a good/reliable one in order to trust him/her. The ratings will be out of 5. We can still make some simple categories to rate, like value for money or purchase happiness and then calculate the overall rating. This rating will show if an offer can be trusted or if is a good deal for other users. Furthermore with this rating system we will be able to rate each user who posts offers, making clear to other people who is a good/trusted user.

In order to make users contribute more to the application, rewards will be offered to them. Rewards could be in form of a coupon that gives a discount in a specific shop. Coupons will be given to users based on their contribution, for example how many friends they invited, on the number of their posts and comments and on their rating.

As we can see in this post (http://www.readwriteweb.com/archives/how_to_make_your_location-based_app_a_success_reward_people_for_activities.php), rewarding users in a location-based mobile applications is the new key to success.

Users could be categorised according to their number of posts. For example a user without any posts or a limited number of posts is considered novice. Users with many posts are categorised as expert users. More categories of users could exist between expert and novice. Also users can rate the comments or posts of other users, and the ranking of a user could be calculated based on posts rating, comments rating, number of posts and number of comments.

A user will be able to make an account and register to Shopster. The Then he/she will be able to login and use the application. The user will have to provide some basic personal information in order to create his/her account. These will be: username, password and email.

Except from that, a user will be able to connect to the application using his existing  Facebook or Twitter username and password. This is a good thing for many people who do not want to give their personal information to every single site the sign up.  We have already found some simple ways to implement this:

• For Facebook integration here : http://www.android10.org/index.php/articleslibraries/290-facebook-integration-in-your-android-application
• For Twitter integration here : http://www.android10.org/index.php/articleslibraries/291-twitter-integration-in-your-android-application

The users of the application are divided into two categories: simple users and shop owners. Simple users are ordinary application users who use the application for viewing and sharing offers and discounts from third party sources.

In contrast to simple users, shop owner user category is first-hand information source whose members can use the application to input the offers and discounts available at their stores thus generating new content and can share it with other users. Thus the application provides a platform which the shop owners can leverage to profit their business.

The application recognises the different categories of users, and provides features accordingly.