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Pruning and aging for user histories in collaborative filtering | IEEE Conference Publication | IEEE Xplore

Pruning and aging for user histories in collaborative filtering


Abstract:

In this paper, we introduce algorithms for pruning and aging user ratings in collaborative filtering systems, based on their oldness, under the rationale that aged user r...Show More

Abstract:

In this paper, we introduce algorithms for pruning and aging user ratings in collaborative filtering systems, based on their oldness, under the rationale that aged user ratings may not accurately reflect the current state of users regarding their preferences. The aging algorithm reduces the importance of aged ratings, while the pruning algorithm removes them from the database. The algorithms are evaluated against various types of datasets. The pruning algorithm has been found to present a number of advantages, namely (1) reducing the rating database size, (2) achieving better prediction generation times and (3) improving prediction quality by cutting off predictions with high error. The algorithm can be used in all rating databases that include a timestamp and has been proved to be effective in any type of dataset, from movies and music, to videogames and books.
Date of Conference: 06-09 December 2016
Date Added to IEEE Xplore: 13 February 2017
ISBN Information:
Conference Location: Athens, Greece

References

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