Related Work next up previous
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Related Work

In this section we briefly present some of the research literature related to collaborative filtering, recommender systems, data mining and personalization.

Tapestry [10] is one of the earliest implementations of collaborative filtering-based recommender systems. This system relied on the explicit opinions of people from a close-knit community, such as an office workgroup. However, recommender system for large communities cannot depend on each person knowing the others. Later, several ratings-based automated recommender systems were developed. The GroupLens research system [19,16] provides a pseudonymous collaborative filtering solution for Usenet news and movies. Ringo [27] and Video Recommender [14] are email and web-based systems that generate recommendations on music and movies respectively. A special issue of Communications of the ACM [20] presents a number of different recommender systems.

Other technologies have also been applied to recommender systems, including Bayesian networks, clustering, and Horting. Bayesian networks create a model based on a training set with a decision tree at each node and edges representing user information. The model can be built off-line over a matter of hours or days. The resulting model is very small, very fast, and essentially as accurate as nearest neighbor methods [6]. Bayesian networks may prove practical for environments in which knowledge of user preferences changes slowly with respect to the time needed to build the model but are not suitable for environments in which user preference models must be updated rapidly or frequently.

Clustering techniques work by identifying groups of users who appear to have similar preferences. Once the clusters are created, predictions for an individual can be made by averaging the opinions of the other users in that cluster. Some clustering techniques represent each users with partial participation in several clusters. The prediction is then an average across the clusters, weighted by degree of participation. Clustering techniques usually produce less-personal recommendations than other methods, and in some cases, the clusters have worse accuracy than nearest neighbor algorithms [6]. Once the clustering is complete, however, performance can be very good, since the size of the group that must be analyzed is much smaller. Clustering techniques can also be applied as a "first step" for shrinking the candidate set in a nearest neighbor algorithm or for distributing nearest-neighbor computation across several recommender engines. While dividing the population into clusters may hurt the accuracy or recommendations to users near the fringes of their assigned cluster, pre-clustering may be a worthwhile trade-off between accuracy and throughput.

Horting is a graph-based technique in which nodes are users, and edges between nodes indicate degree of similarity between two users [1]. Predictions are produced by walking the graph to nearby nodes and combining the opinions of the nearby users. Horting differs from nearest neighbor as the graph may be walked through other users who have not rated the item in question, thus exploring transitive relationships that nearest neighbor algorithms do not consider. In one study using synthetic data, Horting produced better predictions than a nearest neighbor algorithm [1].

Schafer et al., [26] present a detailed taxonomy and examples of recommender systems used in E-commerce and how they can provide one-to-one personalization and at the same can capture customer loyalty. Although these systems have been successful in the past, their widespread use has exposed some of their limitations such as the problems of sparsity in the data set, problems associated with high dimensionality and so on. Sparsity problem in recommender system has been addressed in [23,11]. The problems associated with high dimensionality in recommender systems have been discussed in [4], and application of dimensionality reduction techniques to address these issues has been investigated in [24].

Our work explores the extent to which item-based recommenders, a new class of recommender algorithms, are able to solve these problems.

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Next: Contributions Up: Introduction Previous: Introduction
Badrul M. Sarwar