Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model

     This paper discusses matrix factorization methods that combine different methods including collaborative filtering with the neighborhood models. Since both of these methods focus on different aspects of selection, it would be to ones advantage to combine them and use their strengths and help hide the weaknesses of those algorithms.

     This paper did not go into much detail about all of the implementation, it does explain some of the vocabulary used. It also says that whenever a user gives a movie a rating, regardless if it is good or bad, it actually improves the recommendation. This paper also discusses the new neighborhood model and discusses its benefits. We are still in the process of building a recommendation systems and trying to merge different systems that we feel would be best.

http://dl.acm.org/citation.cfm?id=1401944

Group Recommendation: Semantics and Efficiency

     This paper discusses the problem that is apparent in group recommendation, creating a recommendation based on a group's overall tastes. This paper covers a summary of different methods for single users and talks about extending these functions to work for groups of users.

     The first approach is score aggregation. This method uses different scores obtained from analysis of single users and uses it to create a list of suggestions for the group. They do this by average and least misery to hopefully better predict the group's tastes. The other method used is the threshold algorithm. This gives you an implementation for the top k query processing using a variation of the common Threshold Algorithm.

     Both of these approaches could be used in our final implementation. Once we get a system that works on one algorithm, then we can keep adding more and more till it gets the best suggestion.


http://dl.acm.org/citation.cfm?id=2063576.2063839

Client-server computing in mobile environments

     This paper is pretty straight forward. It goes into detail about the complexities of client server computing in the mobile environments. It is much different that the traditional client server model and since this paper was written in 1999, it predicts some of the problems that could arise using a mobile device that needs to connect to a server.    


     It talks about 3 different approaches to a client-server and some of the pros and cons of all of them. One big problem is that a mobile user may be on the move. This is a problem because you will have connection problems when they lose connection or switch to a different network. The server must robust enough to compensate for a client that is disconnecting and reconnecting all the time. Another problem discussed is battery life and computation power. Mobile devices have a set battery life and you do not want to do much computation on the actual device, but the wireless connection is generally slower than a wired one, so you must balance these two factors. 


Client-server computing in mobile environments

http://dl.acm.org/citation.cfm?id=319806.319814