Further, collaborative filtering can also recommend articles that are not similar in content to items rated in the past as long as like-minded users have rated the items. Unfortunately, collaborative filtering is not effective when there are too few users that have rated an item or for users that do not have a strong history or correlation with other users. Content-based systems use content to filter or recommend items. These perform well when users know and specify topics in which they are interested. Recommendations for a user are based solely on a profile built by analyzing the content of the items which that user has rated in the past.
Content based filters face problems of over-specialization.
When the system can only recommend items scoring highly against a user's profile, the user is restricted to seeing items similar to those she has already seen. Also, it is often difficult for content-based filters to understand the meaning of text or even the actual content of complex items. We combine the strengths of content-based filtering techniques with collaborative filtering to provide more accurate recommendations. We use thresholds to improve the accuracy of traditional filtering algorithms, and design and implement a way to apply content-based filtering to an online newspaper. We compare our improved algorithms to current algorithms using both off-line and online experiments and show that these result in more effective filters that can help manage the massive amount of information that is confronting us today.