Although data quality has been recognized as an important factor in the broad information systems research, it has received little attention in recommender systems. Data quality matters are typically addressed in recommenders by ad-hoc cleansing methods, which prune noisy or unreliable records from the data. However, the setting of the cleansing parameters is often done arbitrarily, without thorough consideration of the data characteristics. In this work, we turn to two central data quality problems in recommender systems: sparsity and redundancy. We devise models for setting datadependent thresholds and sampling levels, and evaluate these using a collection of public and proprietary datasets. We observe that the models accurately predict data cleansing parameters, while having minor effect on the accuracy of the generated recommendations.
|Title of host publication||RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||4|
|State||Published - 16 Sep 2015|
|Event||9th ACM Conference on Recommender Systems, RecSys 2015 - Vienna, Austria|
Duration: 16 Sep 2015 → 20 Sep 2015
|Name||RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems|
|Conference||9th ACM Conference on Recommender Systems, RecSys 2015|
|Period||16/09/15 → 20/09/15|
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