%0 Journal Article %1 gedikli2010rating %A Gedikli, Fatih %A Jannach, Dietmar %D 2010 %J Systems and the Social Web at ACM %K collaborative rating recommendation tag %T Rating items by rating tags %X Dierent proposals have been made in recent years to exploit Social Web tagging data to improve recommender systems. The tagging data was used for example to identify similar users or viewed as additional information about the recommendable items. In this work we propose to use tags as a means to express which features of an item users particularly like or dislike. Users would therefore not only add tags to an item but also attach a preference or rating to the tag itself, expressing, for example, whether or not they liked a certain actor in a given movie. Since rating data is in general sparse in commercial recommender applications we also present how to infer the user opinion regarding a certain feature (tag) for a given item automatically. In contrast to previous works, we not only infer the user's general preference for a tag but rather determine this preference in the context of a certain item. An evaluation on the MovieLens data set reveals that our new tag-enhanced recommendation algorithm is slightly more accurate than a recent tag-based recommender even when the explicit tag rating data is 100% sparse, that is, if only derived information can be used.

@article{gedikli2010rating, abstract = {Dierent proposals have been made in recent years to exploit Social Web tagging data to improve recommender systems. The tagging data was used for example to identify similar users or viewed as additional information about the recommendable items. In this work we propose to use tags as a means to express which features of an item users particularly like or dislike. Users would therefore not only add tags to an item but also attach a preference or rating to the tag itself, expressing, for example, whether or not they liked a certain actor in a given movie. Since rating data is in general sparse in commercial recommender applications we also present how to infer the user opinion regarding a certain feature (tag) for a given item automatically. In contrast to previous works, we not only infer the user's general preference for a tag but rather determine this preference in the context of a certain item. An evaluation on the MovieLens data set reveals that our new tag-enhanced recommendation algorithm is slightly more accurate than a recent tag-based recommender even when the explicit tag rating data is 100% sparse, that is, if only derived information can be used.}, added-at = {2014-03-01T06:49:23.000+0100}, author = {Gedikli, Fatih and Jannach, Dietmar}, biburl = {https://www.bibsonomy.org/bibtex/2e7380137d10bd6a765897ea54bd05a31/inmantang}, interhash = {7a4e1b28558c54b576678146c5a614fe}, intrahash = {e7380137d10bd6a765897ea54bd05a31}, journal = {Systems and the Social Web at ACM }, keywords = {collaborative rating recommendation tag}, timestamp = {2014-03-01T06:49:23.000+0100}, title = {Rating items by rating tags}, year = 2010 }