Definition: 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.
Input: Collaborative filtering or recommender sys­ tems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, in­ cluding techniques based on correlation coef­ ficients, vector-based similarity calculations, and statistical Bayesian methods. We com­ pare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evalua­ tion metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second esti­ mates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommen­ dation in an ordered list. Experiments were run for datasets associ­ ated with 3 application areas, 4 experimen­ tal protocols, and the 2 evaluation met­ rics for the various algorithms. Results indicate that for a wide range of con­ ditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector­ similarity methods. Between correlation and Bayesian networks, the preferred method de­ pends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other con­ siderations include the size of database, speed of predictions, and learning time.

Empirical Analysis of Predictive Algorithms for Collaborative Filtering