This project has focused on buyer’s and seller’s convenience by providing a centralized system where sellers are allowed to apply discounts whenever they wish according to their sales and buyers have freedom of using discounts on any product they wish to buy. A network is formed by connecting shops, restaurants, online sellers and brand franchises and every customer will have a balance of credits which can be utilized for any product with discount /offer on our application. Restaurants can use our application to generate offers to increase revenue on odd days.
Our system could also help sellers to reach their potential customers.
For example: A person buys shirt from some store he will have some credits getting added to his account now he can use those credits to get discount at a burger corner. This inter-brand support is inducing people to buy products of their choice and also allowing sellers to manage those products which didn’t have a good reception from consumers.
Every day big companies and brands design new features and products to make new trending fashion available for people to buy. When a customer buys some product from the store, he/she gets messages regarding some discount/offers with some duration validity. The probability of customer to buy the product is very less and hence mostly such discounts are never used by the customers. New stock is launched for the new season and the old one is sent back to the factory store where big discounts and offers are applied.
Our system is designed to enable such franchises and outlets to sell the products directly by applying discounts on our app. Discounts once applied on the products would ask customer to utilize the credits in return.
Customers get these credits from new products they buy from our application. The major advantage of these credits facility is customers can conveniently use them for any product they want to buy. Another major advantage is sellers can apply credits on old stock of products or when their revenue generation is low. Hence even customers get products at cheaper rates and the sellers don’t need to send the products to factory store and so on. Our app is not only for online sellers but also for offline sellers. We would locate their shop on our app and make customers know when they have some offers going on i.e. when they apply credits on their products thereby apply discount on those products they wish to sell at lower price.
Shopping is undoubtedly one of the most frequent need of every family. However, as the life pace becomes faster and faster, people are less likely to spend time and energy on doing it. Moreover, people can use not only computers but also various types of handheld devices, e.g., PDAs, smartphones and tablets, to surf websites so as to do their shopping easily as information technology advances recently. As a result, shopping online becomes more and more popular. Under such circumstance, how to make online purchasing quick and efficient becomes a vital issue in e-commerce.
Blog provides a simple way for people to share personal experiences and ideas, and has already become an important tool for people to communicate with each other. Due to the vast amount of information on a particular blog, it is often time consuming for reviewing and finding the blog-article to suit the reader’s mind.
The goal of a Recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Suggestions for books on Amazon, or movies on Netflix, are real world examples of the operation of industry-strength recommender systems. The design of such recommendation engines depends on the domain and the particular characteristics of the data available. Recommender systems were developed to help close the gap between information collection and analysis by filtering all of the available information to present what is most valuable to the user.
Clustering is the collection an arrangement of items such that articles in a similar gathering are more comparable (in some sense or another) to each other than to those in different gatherings. It is a fundamental undertaking of exploratory information mining, and a typical strategy for statistical data examination, utilized as a part of numerous fields, including machine learning. Well known ideas of clustering incorporate gatherings with little separations between group individuals and thick zones of the information space. The suitable grouping calculation and parameter rely upon the individual informational index and planned utilization of the results. It is frequently important to adjust information preprocessing and show parameters until the point when the outcome accomplishes the coveted properties.
Collaborative filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one. In the newer, narrower sense, collaborative filtering is a technique for making programmed expectations about the interests of a client by gathering inclinations or taste data from numerous clients. In the more general sense, collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc.
Content-based filtering, also referred to as cognitive filtering, recommends items based on a comparison between the content of the items and a user profile. The content of each item is represented as a set of descriptors or terms, typically the words that occur in a document. The user profile is represented with the same terms and built up by analyzing the content of items which have been seen by the user. Items that are mostly related to the positively rated items are recommended to the user. CBF uses different types of models to find similarity between documents in order to generate meaningful recommendations. Content-based filtering technique does not need the profile of other users since they do not influence recommendation. Also, if the user profile changes, CBF technique still has the potential to adjust its recommendations within a very short period of time.
For example, if a user likes a web page with the words “mobile”, “pen drive” and “RAM”, the CBF will recommend pages related to the electronics world. Content-based filtering algorithms try to recommend items based on similarity count. The best-matching items are recommended by comparing various candidate items with items previously rated by the user. A content based recommender works with data that the user provides, either explicitly (rating) or implicitly (clicking on a link). Based on that data, a user profile is generated, which is then used to make suggestions to the user. As the user provides more inputs or takes actions on the recommendations, the engine becomes more and more accurate.
Customer behavior and analysis is important for every e-business in order to provide best services and satisfaction to the customers. Security aspect is equally important for any payment-based system. The following data is produced from papers we referred in order to have different perspective related to design and performance of our project:
The consumers will save in monetary when there are price promotions on specific products. Their study resulted that consumers with a shopping goal are more responsive towards promotional messages such as “pay less” and “discount” while consumers without shopping goal are responsive towards promotional messages such as “save more” and “free gift”. They cited that price promotion has several benefits such as to increase demand, adjust fluctuations in supply and demand, and increasing consumers purchasing over time. [1]
Now a day’s electronic transaction has become a common, everyone purchases products over the internet and also pay the bill, which is becoming an easy and everyday necessity, secure methods of payment gateway for such online purchase product have become very important so that its need to protect from unauthorized use. Therefore, here Security for such transaction is necessary because its confidential information such as credit card numbers, pin numbers and other information for that security is required. This research paper review security in electronic transaction using credit card and payment applications which used in mobile application like Pay through Mobile application. [2]
It is necessary to know how consumers perceive online shopping environments. The conceptual model proposes that consumers perceive these environments in terms of their sense making and exploratory potential, and it considers the influence of these on user involvement with the web site, shopping value and intention to revisit. Findings. Sense making and exploratory potential are distinct constructs; exploratory potential mediates the relationship between sense making potential and involvement. Furthermore, involvement is essential in producing shopping value and intention to revisit. [3]
It is also important to know how secure consumer feel while making digital payments and online shopping and which buying behaviour is found suitable for consumers according to the different gender and age and income. And what is their attitude perception about online buying behaviour and what makes them prefer online shopping. Simple percentage analysis and chi-square test methods are used as statistical tool for this analysis. At the end of this research, researcher has identified that Majority of consumers belonging to teen age and below 40 ages prefers online shopping.
Educated consumer are comfortable shopping online as it saves time and they say that they get more discount and can compare more products at one place. Majority of consumer still don’t feel secured of making digital payment and still prefer cash on delivery mode of payment and it’s found out that though E-shopping is being on rise consumer trust on E-transaction is comparatively not high they still don’t feel secured of making digital payments. The public lack of confidence in online IT is not merely about security of value, but also about trust in the in Society. Privacy and security concerns are the number one reason that still sways in consumers buying perception towards online shopping. [4]
Increased demand of restaurant-goers generated the need for much attention for the hospitality industry. Providing much option with ease of ordering and delivering is the need of the hours. Technological interference has become mandatory to improve the quality of the service and business in this industry. Evidences are already existed for partial automation of food ordering process in the country; most of these technologies implemented are based on wireless technologies.
This manuscript reports implementation and integration of web-based technology for restaurants. A dynamic database utility system was designed to fetch all the information from a centralized database. User utility was given importance during the development of this interface and efficiency, accuracy was the priority for better results and services and to reduce the majority of the human error. It was observed that this system was successful in overcoming the shortcomings found in the previously developed similar systems. Moreover, this system was very cost effective in development as well as during use. [5]
Android is an operating system developed for smartphones and tablets. It is based on Linux kernel and uses Dalvik Virtual Machine (DVM) for executing Java byte code . Absence of GNU C Library and some functions differentiate it from being Pure Linux. Android’s source code is released by Google under open source licenses.
Some features of Android are:
Android software environment consists of:
This system will be created to introduce a credit-based system which will allow online and offline sellers to apply a certain no. of credits on new products so that customers who buy those products would earn them and use them to get discounts on other products available on the app. Here sellers can apply discounts over their products depending on their sales by asking customers to utilize credits. Application also includes a feature of sharing the products with your friends and thereby generating a fashion score. Fashion bloggers also would get a chance to showcase their work on the app.
It is basically a breakthrough which will help brands, shops, restaurants, independent sellers to support their own old products and others too as the system is a centralized one and credits are a way to do so. Basic implementation of splash screen, bottom navigation bar, using frame layout to change the options and recycler-view along-with card-view were used. We also developed Login activity and Registration activity for the android application.
We have also designed a bottom navigation bar with five major utilities/features which are mentioned below:
In general, today’s businesses must always strive to create the next best thing that consumers will want because consumers continue to desire their products, services etc. to continuously be better, faster, and cheaper. In this world of new technology, businesses need to accommodate to the new types of consumer needs and trends because it will prove to be vital to their business success and survival. E-commerce is continuously progressing and is becoming more and more important to businesses as technology continues to advance.
Collaborative algorithm uses “User Behavior” for recommending items. They exploit behavior of other users and items in terms of transaction history, ratings, selection and purchase information. Other user’s behavior and preferences over the items are used to recommend items to the new users. In content-based filtering we have to know the content of both user and item. Usually we construct user-profile and item-profile using the content of shared attribute space. For example, for a movie, you represent it with the movie stars in it and the genres.
For user profile, you can do the same thing based on the users likes some movie stars/genres etc. To calculate how good a movie is to a user, we use cosine similarity. Here, you have product attributes like image (Size, dimension, color etc.) and text description about the product then it is Content Based Recommendation
Thus, we have developed a centralized system for inter-brand support and promotion and also increasing the sales of sellers on our website by offering discounts on old and unsold products by having a flow of credits and through our mobile application we will be allowing our customers to share the credits and make use of them according to their convenience which will also give a broader behavioral analysis of customer’s purchasing habits.
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