Teacher:In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Teacher: Now, understand the problem? Solve this instance: We treat collaborative filtering as a univari ate time series problem: given a user's previ ous votes, predict the next vote. We describe two families of methods for transforming data to encode time order in ways amenable to off-the-shelf classification and density estima tion tools. Using a decision-tree learning tool and two real-world data sets, we compare the results of these approaches to the results of collaborative filtering without ordering infor mation. The improvements in both predic tive accuracy and in recommendation quality that we realize advocate the use of predictive algorithms exploiting the temporal order of
Student:
Using Temporal Data for Making Recommendations