ABSTRACT
Many websites use tags as a mechanism for improving item metadata through collective user effort. Users of tagging systems often apply far more tags to an item than a system can display. These tags can range in quality from tags that capture a key facet of an item, to those that are subjective, irrelevant, or misleading. In this paper we explore tag selection algorithms that choose the tags that sites display. Based on 225,000 ratings and survey responses, we conduct offline analyses of 21 tag selection algorithms. We select the three best performing algorithms from our offline analysis, and deploy them live on the MovieLens website to 5,695 users for three months. Based on our results, we offer tagging system designers advice about tag selection algorithms.
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Index Terms
Learning to recognize valuable tags
Recommendations
The quest for quality tags
GROUP '07: Proceedings of the 2007 ACM International Conference on Supporting Group WorkMany online communities use tags - community selected words or phrases - to help people find what they desire. The quality of tags varies widely, from tags that capture akey dimension of an entity to those that are profane, useless, or unintelligible. ...
Tagommenders: connecting users to items through tags
WWW '09: Proceedings of the 18th international conference on World wide webTagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navigate vast collections of items. Algorithms combining tags with recommenders ...
Visualizing Tags with Spatiotemporal References
IV '11: Proceedings of the 2011 15th International Conference on Information VisualisationNowadays, a great amount of data is created and distributed on the Internet. Tagging has become common practice to structure these data for easy access. Often the data and the associated tags contain spatial and temporal information. In this paper, we ...
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