Collaborative filtering | Recommendations include suggestions for knowledge base, or to find model service requests. In order to make a recommendation, first a group sharing similar taste is found and then the preferences of the group is used to make a ranked list of suggestions. This technique is called collaborative filtering. A common data structure that helps with keep tracking of people and their preferences is a nested dictionary. This dictionary could use a quantitative ranking say on a scale of 1 to 5 to denote the preferences of the people in the selected group. To find similar people to form a group, we use some form of a similarity score. One way to calculate this score is to plot the items that the people have ranked in common and use them as axes in a chart. Then the people who are close together on the chart can form a group. | Several approaches mentioned earlier provide a perspective to solving a problem. This is different from those in that opinions from multiple participants in a group is taken to determine the best set of articles or service requests to recommend. |
Algorithm Implementations:
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