Archive for February 18th, 2011
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.
Web-Enabled Online Network Effects
The network effects that our is going to utilize are the following [1]:
- Direct network effect: The more users start using our system the better recommendation will provide back to them.
- Indirect network effect: The more users start using Facebook the more people are likely to user our system.
- Cross network effect:
- The more users use our service the more actors and movie companies will use it to gain access to movie groups
- The more users use our service the more advertisers will found our service for advertisements.
- Social Network Effect: Users are influences by their friends which movie are going to see.
References
- A. Shuen, “Web 2.0: A Strategy Guide: Business thinking and strategies behind successful Web 2.0 implementations”, O’Reilly, 2008, pp. 41-42.