Abstract
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 data-dependent 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.
Original language | English |
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Title of host publication | Proceedings of the 9th ACM Conference on Recommender Systems |
Subtitle of host publication | RecSys '15 |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 257-260 |
Number of pages | 4 |
ISBN (Electronic) | 9781450336925 |
DOIs | |
Publication status | Published - 16 Sept 2015 |
Externally published | Yes |
Event | 9th ACM Conference on Recommender Systems, RecSys 2015 - Vienna, Austria Duration: 16 Sept 2015 → 20 Sept 2015 |
Conference
Conference | 9th ACM Conference on Recommender Systems, RecSys 2015 |
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Country/Territory | Austria |
City | Vienna |
Period | 16/09/15 → 20/09/15 |