Archive for category Related works
Our aim is to make this project a commercial product. To achieve this, firstly we have to improve the client side providing better user experience. At the moment has been implemented using multiple static pages. Further development might be to provide a single page static page where all the user dialogs will appear in the pages this will enable a the provision of a better user experience with smooth transitions from dialog to dialog.
In terms of the technology we need to optimize our servlet algorithm by providing simultaneous (using Java threads) requests to social network APIs eliminating in that way the latency. Further improvement is to provide a buffer for each request for example the last 20 movie searches can be cashed on the servlet so in case the same query-string is requested again then the will return the information directly without making the request to the third party. This happens frequently because the user type a name on the search bar and the presses the backspace to correct it. The string after pressing the backspace is the string had previously requested.
We consider vital the integration of you system with foursquare because our system is mostly targeted foursquare type of users. This kind of integration will have benefit all parties (user, Facebook and MovieIt) because the user gets better recommendation (from MovieIt) and badges (from foursquare) the Foursquare and MovieIt will get information from the user activity the former for places and the later to provide better recommendation.
Finally there is a need of automatic composition, integration, and execution of Web Services. We can achieve this by extending our system to use Semantic Web Services based on RESTfull Web Services. This Semantic web technology will facilitate integration of other systems without ours gaining in that way more information for other sources and therefore more value.
Thanks to the astonishing growth of the internet, having millions of users is no longer a dream for new applications. It used to take applications years to get more than a hundred thousand users, let alone millions. But nowadays it’s not very unusual for applications to attract millions of users in couple of months (or even in a few days). So that, being prepared for this huge number of users and having an effective structure for dealing with this large numbers is essential for designing social applications. After all, nothing kills an application’s growth more than being down for a couple of days (or even a few hours).
Therefore we have designed our application with the scalability issue in mind. Scalability issues that we have considered are in two categories of technical issues and development issues.
The main issue that we have investigated in the technical issue is the issue of database. We have decided that MySQL is an effective DBMS for starting the application and gaining the first few thousands users. However, we are aware that as our user base grow beyond those numbers, MySQL becomes less suitable. Therefore we have decided to move from Relation database management solutions to database systems which offer eventual consistency, at that time.
The development issue that we have considered is that when we attract more users to our system, we would need more programmers to work on the application and develop it. Therefore we have used standard development techniques in order to minimise the effort the new programmer’s effort to get to know the system.
Om Malik (senior writer for Forbes.com, Red Herring and Business 2.0), in his recent blog post, explains his views on the next generation of social networks.
He predicts that, in the future, there will be lots of specific social graphs, instead of one big social graph. He explains that our friends are not necessarily the best source of recommendations in all aspects. We might have a friend that we really like his/her taste in music, but we don’t like his/her sense in movies.
Therefore he suggests that the next generation of social applications are those that build their interest-oriented networks on top of large social networks. Furthermore, he thinks that these kind of networks have more potential for making purchases happen (something which social networks advertising is not currently very good at, in compare with other online industries such as search engine advertising).
For entrepreneurs, I believe there are opportunities to create unique experiences around the concept of “interest graphs” that can be built off the backs of uber-networks such as Facebook and Twitter. These networks can help find the right kind of audience to build a viable channel for new commerce experience. (Om Malik)
Our movie application (MovieIt) has been built on the very same idea. Users in our application have the opportunity to choose the people they want to follow, and connect with the ones they trust, instead of following all their friends. Furthermore,our application has a large potential for making purchases happen ( which in our application is buying cinema tickets).
Last week we ran an open discussion of our project. The focus group of four people critically evaluated our idea and expressed their opinions how to extend and improve proposed system. Invited reviewers considered presented suggestion tool from different points of view: starting from required functionality and technical details and ending with social, legal and economic aspects. Below is a short description of the most interesting ideas and critics expressed during the meeting.
As mentioned before, our proposed system is a movie recommendation system that tells its users what movies to watch and also who has suggested that movie. It also suggests its users the best place for them to watch the movie using geo-location info. Another feature of our system is that it lets its users to know who from their friends might be interested in watching the movie.
In order to estimate the usability of our system, we conducted a survey to check other people`s opinions about our system. What the results show, encourage us to work harder on our system.
About 30 people took part in our survey. 94% of them are interested in watching movies. 42.31% of them prefer to watch a movie with other people at social places and 33.33% of them prefer cinema instead of home or any other places. 59.26% of them go to the movies with their friends. Internet is the best way for 59.26% of them to choose a movie to watch and 25.93% of them use their friend recommendations. More than 95% of them show interest in telling or recommending their favourite movies to their friends. Almost all of them have Facebook accounts and more than half of them want to use Facebook as a way to tell their friends about the movies they have watched and enjoyed. Some of them use their mobile phones to search for a movie or cinema. More than 80% believe that the location of the cinema is very important for them and they want to tell their friends about the location and quality of the cinema they have been to.
To check the results click on the rest of this entry…
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.
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.
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