Idea evaluation
In previous posts (literature survey, existing applications) devoted to a field of interestsâ investigation, we published an overview of existing applications and researches related to our initial idea. The survey showed that the demand in the web market and research community for recommendation systems, has led to its mass production in the past few years. Some of these suggestion tools are quite successful (Jinni, Filmaster Blog, Faves for Facebook, etc.) as they provide rich functionality and a good quality of recommendations. Â However, in this sketch we will differentiate our idea from existing solutions and also outline strengths of the proposed system.
Overview of GUI Frameworks for mobile Web
Conventionally, our project implementation can be split into two parts: server side development and front-end development. This post is devoted entirely to front-end development (interface design). In particular, we will talk about current RIA technologies for mobile Web and benefits which these technologies can bring to our project.
In order to provide a user with an interactive and rich interface we considered many GUI frameworks for mobile browsers and, finally, stopped our selection on âjQuery mobileâ, âjQTouchâ and âModernizrâ.
The Implementation (first steps)
This week we spent efforts to implement the basic architecture components. More specifically build an HTML web page for authenticating the user and authorizing our application. The result of this procedure is the provision of an âaccess tokenâ. This token is passed to our servlet using the HTTP GET method. The servlet application is now able to have access to all features provided by the Facebook Graph API.
As you can see in the following figure, we tested our web application by checking-in some friends in Athens Greece. This feature will be embedded to our application so people to be able to inform their friends about a movie they watched, the  place and their comments they have.
A Check-in post using the Facebook Graph API
Time managment
As a group, we understand that its very important to have a top level plan of the tasks which need to be performed. In order to display this plan, and keep track of our progrees, we have created a gantt chart project using the Microsoft project. The following image is an screenshot of our gantt chart.
Existing applications
Posted by fs7g10 in Related works on 21st February 2011
Dozens of movie recommendation engines and applications exist. Some require little or no input before they give you titles, while others want to find out exactly what your interests are. Some examples of these engines and applications are mentioned below.
– Jinni: Jinni is a Taste Engine. It looks at film through the lens of what makes you love or hate anything you watch. With a Taste Engine, you don’t search by what you’re looking for, you search by what you like. And recommendations are based on analysing your preferences, not statistics. Registration is required for social networking aspects of sites.
Application requirements
Having a clear understanding of what we want to build, is an essential step for building it. For this reason, we have written a list of application requirements that should be achieved by the end of this project.
Literature survey (part 1)
Posted by Kristina in Related works on 18th February 2011
Our area of interests is focused on geo-based recommendation systems which exploit relations between users in social networks. In order to evaluate, adjust and extend our initial idea we performed a survey looking for both commercial products and academic researchers related to these topics. However, this post is devoted entirely to academic works. In our investigation we focused, first of all, on location-based recommendation systems on top of social networks, suggestion algorithms exploiting social network relations and the level of trust in recommendation systems and trust factors. Furthermore, we considered materials about the level of anonymity and privacy issues in social networks, legal issues with user-generated content, social and economic impact of recommendation systems.
The proposed system
Movie selection is a taste-related domain where friends (Social Groups and Group Members) influence one another by providing evaluation and advices[1]. In real world, it is time and cost consuming to find friends that they have already seen the movie (to request their opinion) or they have not and you want to propose to them to watch it together.
Our proposed system utilizes the infrastructure provided by Facebook, allowing users to state:
- Movies watched
- Comment movies, cinemas
- The movie they watched
- Suggest movies to friends
- The place they watched the movie (Using Facebook Places Service)
- Make movie groups
- Friend watched the movie with
By using the aforementioned information, the system will be able to:
- Provide movie recommendations
- Narrow it down to specific friends, movie groups, dates, area
- See the movie activity of friends
- Narrow it down to specific friends, movie groups, dates, areas
- See his friends movie recommendation
System Architecture
References
- W. Woerndl, G. Groh, “Utilizing Physical and Social Context to Improve Recommender Systems“, IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2007.