Abstract
Recommendation agents employ prediction algorithms to provide users with items that match their interests. In this paper, we describe and evaluate several prediction algorithms, some of which are novel in that they combine user-based and item-based similarity measures derived from either explicit or implicit ratings. We compare both statistical and decision-support accuracy metrics of the algorithms against different levels of data sparsity and different operational thresholds. The first metric evaluates the accuracy in terms of average absolute deviation, while the second evaluates how effectively predictions help users to select high-quality items. Our experimental results indicate better performance of item-based predictions derived from explicit ratings in relation to both metrics. Category-boosted predictions can lead to slightly better predictions when combined with explicit ratings, while implicit ratings (in the sense that we have defined them here) perform much worse than explicit ratings.
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Papagelis, M., Plexousakis, D. (2004). Qualitative Analysis of User-Based and Item-Based Prediction Algorithms for Recommendation Agents. In: Klusch, M., Ossowski, S., Kashyap, V., Unland, R. (eds) Cooperative Information Agents VIII. CIA 2004. Lecture Notes in Computer Science(), vol 3191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30104-2_12
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DOI: https://doi.org/10.1007/978-3-540-30104-2_12
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