29-12-2012, 03:02 PM
An Open Architecture for Collaborative Filtering of Netnews
1Open Architecture.docx (Size: 133.57 KB / Downloads: 23)
Advantages:
1. Better bit bureau used
2. Some time Separate BBB used
3. One major advantage of the separate BBB is that it can pre-fetch ratings and pre-compute predictions rather than computing them when the user starts the news client. Thus, user time should remain roughly constant as Group Lens grows, even if it takes more CPU time to compute scores.
Disadvantages:
1. Further research is needed to understand how performance will change as the scale increases. In the case of Group Lens, there are several relevant performance measures: prediction qualities, user time, Better Bit Bureau compute time and disk storage, and network traffic.
2. The first measure is the quality of score predictions. We expect prediction quality to increase as the number of users increases, since more data will be available to the prediction algorithm.
3. Another measure is how long users have to wait to post ratings and receive predictions. In an earlier version of Group Lens, the functions of the BBB were incorporated in the news client itself.
4. For many possible prediction formulas CPU time will grow even faster than linearly with increases in the number of users. To reduce CPU time, BBBs could use only a part of the ratings matrix, trading off compute time against quality of predictions.
Even though each rating is short, each news article might be read and rated by many raters, so the total volume of ratings could exceed the volume of news. To minimize storage requirements, BBBs may employ algorithms that use and discard ratings as they arrive, rather than storing them.
1. Three basic techniques could reduce network traffic: reduce the size of the ratings, reduce the number of ratings, and reduce the number of places where each rating is sent. Our BBBs batch several ratings in a single article, a first step toward reducing the amount of storage per rating, but further compression is possible.