Al Borchers

Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system

Collaborative filtering systems help address information overload by using the opinions of users in a community to make personal recommendations for documents to each user. Many collaborative filtering systems have few user opinions relative to the large number of documents available. This sparsity problem can reduce the utility of the filtering system by reducing the number of documents for which the system can make recommendations and adversely affecting the quality of recommendations. This paper defines and implements a model for integrating content-based ratings into a collaborative filtering system. The filterbot model allows collaborative filtering systems to address sparsity by tapping the strength of content filtering techniques. We identify and evaluate metrics for assessing the effectiveness of filterbots specifically, and filtering system enhancements in general. Finally, we experimentally validate the filterbot approach by showing that even simple filterbots such as spell checking can increase the utility for users of sparsely populated collaborative filtering systems.

Ganging up on information overload

When information is abundant, the knowledge of which information is useful and valuable matters most. We all use our network of family, friends, and colleagues to recommend movies, books, cars, and news articles. Collaborative filtering technology automates the process of sharing opinions on the relevance and duality of information. Collaborative filtering is one technique among many information filtering techniques that range from unfiltered to personalized and from effortless to laborious. Libraries or the Web are good examples of unfiltered information sources. E-mail directed to one recipient is a good example of a filtered information source. A best-seller list requires little effort fur the user, but provides the same recommendations to all users. Filters based on demographics, such as age, sex, or marital status, require some effort from the user in providing the demographics, and provide some level of personal filtering, so they are near the middle of the chart. Collaborative filtering requires relatively little effort from the user, and provides individually targeted recommendations, so it is in the upper right of the chart. Effort, of course, can be reduced via automation. While collaborative filtering is not necessarily effortless, it requires a relatively small amount of effort on the part of the user and provides very individualized recommendations. The collaborative filtering systems that we discuss here each offer a high degree of personalization, but each system takes a different approach to automation, attempting to find the best trade-off between the amount of work the users must put into the system and the perceived value and benefits they receive in return.

The k -Steiner Ratio in Graphs

A Steiner minimum tree (SMT) is the shortest-length tree in a metric space interconnecting a set of points, called the regular points, possibly using additional vertices. A k-size Steiner minimum tree (kSMT) is one that can be split into components where all regular points are leaves and all components have at most k leaves. The k-Steiner ratio,

The k-Steiner ratio in graphs

\rho_{k}

Extending the quadrangle inequality to speed-up dynamic programming

, is the infimum of the ratios SMT/kSMT over all finite sets of regular points in all possible metric spaces, where the distances are given by a complete graph. Previously, only

Thek-Steiner Ratio in the Rectilinear Plane

\rho_{2}

Optimal transmission schedules for lightwave networks embedded with de Bruijn graphs

and

An algorithmic framework for performing collaborative filtering

\rho_{3}

Combining collaborative filtering with personal agents for better recommendations

were known exactly in graphs, and some bounds were known for other values of k. In this paper, we determine

Recommender Systems : A GroupLens Perspective

\rho_{k}