A Product Recommendation System Based On Mobile Context

For example, some commercial services such Facebook use cookies to track user behavior from several sites that the user visited before to display specific advertisements to individual user. On the other hand, Amazon predict or recommend products to user based on previously purchased products on the same categories or user rating. Several personalization solutions have also been developed to help user to find interested applications. For example, in Android platform, AppBrain uses Android API to monitor what applications a user has installed recently, and recommend other applications in the same category(AppBrain,

The mobile application is developed using Android Studio SDK. All the CRUD operations are performed in MySQL database present in Amazon cloud. To connect the UI with the database, PHP scripting language is used and the PHP files are accessed from the remote server via HTTP request methods.

Now our service is to acquire the user who has the most likelihood to be recommended. First we use the probability function $P(r,u)$ using the communication between the user and recommender. If the probability is higher than our defined threshold value then grouped the user. All the nodes in this group are our expected nodes. Now for the nearest one we run BFS and extract the best one for recommendation.

Nationwide, the Food Truck industry has rocketed, with Miami being the second place city with largest food truck scene. We plan on developing application which lets users connect to to their favorite local food trucks, and have the ability to track, locate the nearest, place an order and share reviews, and pictures through their social media. We noticed there was a lack of apps with these features in the market; as of now, only one application is available on the market, which has a very limited selection of 15 of the 300+ food trucks.

Recommender Systems are based on well-structured and incremental algorithms that have different strengths and weaknesses \cite{ricci2011introduction}. In general, the main existing RSs are concerned with improving accuracy with the premise of being useful to users. In this context, there is an increasing number of custom techniques that analyze the profile of the target user in order to better satisfy them \cite{bobadilla2013recommender}. In this sense, we conducted a broad study of the several metrics used in the literature to evaluate RSs. By organizational purposes, we divided these metrics into three groups: {\it Effectiveness-based}, {\it Complementary Dimensions of Quality} and {\it Domain Profiling}.\looseness=-1

The incredible pace of technological advancement is a challenge for all companies. In recent years, the greatest growth was seen in mobile devices, which challenges companies, like REI, to keep up with consumer demands. With mobile devices, customers want to interact with companies on their terms. Companies, like REI, can respond by offering personalized marketing directed at users of mobile devices. Mobile devices are also leading the charge for new payment methods, NFC enabled devices and mobile

Instead of various local chain stores, Netflix operated from a single virtual store that offered customers new rental features. The web services platform utilized customization and a user-friendly design to facilitate renting movies. “CineMatch,” a proprietary developed application, tailored the virtual store to each subscriber. This technology produced customized rental suggestions for customers while maximizing potential inventory. “CineMatch” incorporated a subscriber rating system of past rentals to generate more efficient and personalized future recommendations. The web-based services also improved selection through the ability to easily browse Netflix’s large inventory catalog and add selections to a personalized “Queue,” which lists movies customers have scheduled to receive. Instead of being forced to navigate through the traditional genre and alphabetized catalog, Netflix offered an easier movie selection process. Besides the selection efficiencies, members have the ability to share ratings, reviews, and movie selections with each other through the “Community” feature.

A linear formula idea will be used and the decision variables will be labeled as follow:

Smart phone apps and e-diaries have become much more accessible and easy to use throughout the years. We are currently experiencing a technological boom with the development of smart phone apps and user friendly e-diaries for data collection. Due to the transient nature of society,

- Macys is the world largest department store in the world, it recently announced the pilot of Macys “on call” , and this is basically a mobile tool that essentially allows customers to interact with an al-powered platform through their mobile devices. This tool is control and influenced by IBM watson and is set to be tested in 10 new locations around the U.S. This gadget allows customers input natural language questions regarding the products of each store regionally. They would then receive a response based on their inquiry. For example, if I was looking for a specific sports gear, and they did not have any available, I would then use this tool and it would locate exactly where to find them as well as indicating the quantity on hand. Macys

Location-Based Advertising only works if the user shares his location with the organization (Mobile Marketing Association, 2011). The number of applications that use the consumer’s location is rapidly

line with the existent nature of McDonald's premises and business model. Thus, it is recommendable to

Various techniques have been used in content-based models. Such systems try to find regularities in the descriptions that can be used to distinguish highly rated items from others [97]. Content-based approaches are based on objective information about the items. This information is automatically extracted from various sources (e.g., Web pages) or manually introduced (e.g., product database). However, selecting one item or another is based mostly on subjective attributes of the item (e.g., a well-written document or a product with a spicy taste). Therefore, these attributes, which better influence the user’s choice, are not taken into account. In the rest of this section, we discuss three technique of content-based filtering technique including keyword-based models, semantic techniques, and probabilistic models. The first systematic evaluation of the impact of applying perturbation-based privacy technologies on the usability of content-based recommendation systems proposed by Puglisi, S., et al. (2015) [98]. The primary goal of their work is to investigate the effects of tag forgery to the content-based recommendation in a real-world application scenario, studying the interplay between the degree of privacy and the potential degradation of the quality of the recommendation. In other paper, Rana, C. and S.K. Jain [23] have developed a book recommendation system that is based on content-based recommendation technique and takes into account the choices of not an

Mobile computing has infiltrated every area of our planet and personal lives. We have become so dependent upon our mobile devices they are often referred to as our third limb. Regardless of race, color, creed or location, mobile technology has shrunk the entire world into the palm of our hands. Mobile technology has made research, communication with our friends and family, education, entertainment and even banking possible to do on the go.

But now, McDonald 's was combined with Yahoo establish the website in order to solve the problem of large logistics management, that is to provide information system access for its staff, chain stores and suppliers in all over the world. This website cover 120 countries, and at the same time when customers into the website, they can check every country and every outlets. In conclusion, the success of McDonald