Abstract
One of the key challenges in large information systems such as online shops and digital libraries is to discover the relevant knowledge from the enormous volume of information. Recommender systems can be viewed as a way of reducing large information spaces and to personalize information access by providing recommendations for information items based on prior usage.
Collaborative Filtering, the most commonly-used technique for this task, which applies the nearest-neighbor algorithm, does not make use of object attributes. Several so-called content-based and hybrid recommender systems have been proposed, that aim at improving the recommendation quality by incorporating attributes in a collaborative filtering model.
In this paper, we will present an adapted as well as two novel hybrid techniques for recommending items. To evaluate the performances of our approaches, we have conducted empirical evaluations using a movie dataset. These algorithms have been compared with several collaborative filtering and non-hybrid approaches that do not consider attributes. Our experimental evaluations show that our novel hybrid algorithms outperform state-of-the-art algorithms.
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Tso, K., Schmidt-Thieme, L. (2006). Attribute-aware Collaborative Filtering. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_75
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DOI: https://doi.org/10.1007/3-540-31314-1_75
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