336 ROBIN BURKE New Item: Similarly, a new item that has not had many ratings also cannot be easily recommended: the 'new item'problem. This problem shows up in domains such as news articles where there is a constant stream of new items and each user only rates a few. It is also known as the early rater' problem, since the first person to rate an item gets little benefit from doing so: such early ratings do not improve a user's ability to match against others(Avery Zeckhauser, 1997). This makes it necessary for recommender systems to provide other incentives to encourage users to provide ratings Collaborative recommender systems depend on overlap in ratings across users and have difficulty when the space of ratings is sparse: few users have rated the same tems. The sparsity problem is somewhat reduced in model-based approaches, such as singular value decomposition(Strang, 1988), which can reduce the dimensionality of the space in which comparison takes place(Foltz, 1990; Rosenstein& Lochbaum 2000). Still sparsity is a significant problem in domains such as news filtering, since there are many items available and, unless the user base is very large, the odds that another user will share a large number of rated items is small These three problems suggest that pure collaborative techniques are best suited to oblems where the density of user interest is relatively high across a small and stati universe of items. If the set of items changes too rapidly, old ratings will be of little value to new users who will not be able to have their ratings compared to those of the existing users. If the set of items is large and user interest thinly spread, then the probability of overlap with other users will be small Collaborative recommenders work best for a user who fits into a niche with many neighbors of similar taste. The technique does not work well for so-called 'gray sheep'(Claypool et al., 1999), who fall on a border between existing cliques of users This is also a problem for demographic systems that attempt to categorize users on personal characteristics. On the other hand, demographic recommenders do not have the 'new user'problem, because they do not require a list of ratings from user.Instead they have the problem of gathering the requisite demographic information. With sensitivity to on-line privacy increasing, especially in electronic commerce contexts (USITIC, 1997), demographic recommenders are likely to remain rare: the data most predictive of user preference is likely to be information that users are reluctant to disclose Content-based techniques also have a start-up problem in that they must accumu- late enough ratings to build a reliable classifier. Relative to collaborative filtering, content-based techniques also have the problem that they are limited by the features that are explicitly associated with the objects that they recommend. For example, content-based movie recommendation can only be based on written materials about a movie: actors' names, plot summaries, etc. because the movie itself is opaque to the stem.This puts these techniques at the mercy of the descriptive data available Collaborative systems rely only on user ratings and can be used to recommend items without any descriptive data. Even in the presence of descriptive data, some exper- Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission