%0 Journal Article %1 x.10_12umuai %A Felfernig, Alexander %A Burke, Robin %A Pu, Pearl %D 2012 %J User Modeling and User-Adapted Interaction %K v1205 springer paper ai user adaptive interface assist design zzz.th.c4 %N 4-5 %P 313-316 %R 10.1007/s11257-012-9120-5 %T Preface to the Special Issue on User Interfaces for Recommender Systems %U http://link.springer.com/journal/11257/22/4/ %V 22 %X Recommender systems provide a valuable support for users who are searching for products and services that match their preferences and needs. There are three basic approaches to the recommendation of products and services. Collaborative techniques calculate recommendations by determining nearest neighbors whose rating behaviors are similar to the one of the active user. In this context, items are recommended which are not known by the current user but have been rated positively by the nearest neighbors. Content-based recommendations are determined on the basis of the similarity between the preferences of a user (stored in a user profile) and the corresponding item descriptions. Finally, knowledge-based recommendation determines items of relevance for the user either by interpreting an explicitly defined set of filtering rules (constraints) or by taking into account the similarity between a set of explicitly defined user requirements and the elements of the underlying item set. The practical relevance of recommender systems is shown by a variety of commercial applications such as Amazon, Netflix, Trip Advisor, and Yahoo. Recommender systems research has long focused on optimizing the predictive accuracy of recommendation and filtering algorithms, a trend epitomized by the recent Netflix prize. An extremely important dimension very often not captured in this attention on predictive accuracy is the nature and perceived qualities of recommender systems from the user's point of view. The user interface (UI) of a recommender system can have a critical and decisive effect on factors such as the overall system usability, system acceptance, item rating behavior, selection behavior, trust, willingness to buy, willingness to reuse the recommender system, and willingness to promote the system to others. Exactly such aspects are the focus of this special issue on UIs for Recommender Systems.

@article{x.10_12umuai, abstract = {Recommender systems provide a valuable support for users who are searching for products and services that match their preferences and needs. There are three basic approaches to the recommendation of products and services. Collaborative techniques calculate recommendations by determining nearest neighbors whose rating behaviors are similar to the one of the active user. In this context, items are recommended which are not known by the current user but have been rated positively by the nearest neighbors. Content-based recommendations are determined on the basis of the similarity between the preferences of a user (stored in a user profile) and the corresponding item descriptions. Finally, knowledge-based recommendation determines items of relevance for the user either by interpreting an explicitly defined set of filtering rules (constraints) or by taking into account the similarity between a set of explicitly defined user requirements and the elements of the underlying item set. The practical relevance of recommender systems is shown by a variety of commercial applications such as Amazon, Netflix, Trip Advisor, and Yahoo. Recommender systems research has long focused on optimizing the predictive accuracy of recommendation and filtering algorithms, a trend epitomized by the recent Netflix prize. An extremely important dimension very often not captured in this attention on predictive accuracy is the nature and perceived qualities of recommender systems from the user's point of view. The user interface (UI) of a recommender system can have a critical and decisive effect on factors such as the overall system usability, system acceptance, item rating behavior, selection behavior, trust, willingness to buy, willingness to reuse the recommender system, and willingness to promote the system to others. Exactly such aspects are the focus of this special issue on UIs for Recommender Systems.}, added-at = {2012-06-20T16:11:09.000+0200}, author = {Felfernig, Alexander and Burke, Robin and Pu, Pearl}, biburl = {https://www.bibsonomy.org/bibtex/21a2e6083cf4186502212e4c8a789e251/flint63}, doi = {10.1007/s11257-012-9120-5}, groups = {public}, interhash = {42f01874be46b6239c5e2d2b523cac71}, intrahash = {1a2e6083cf4186502212e4c8a789e251}, issn = {0924-1868}, journal = {User Modeling and User-Adapted Interaction}, keywords = {v1205 springer paper ai user adaptive interface assist design zzz.th.c4}, month = {#oct#}, number = {4-5}, pages = {313-316}, timestamp = {2013-03-21T17:31:37.000+0100}, title = {Preface to the Special Issue on User Interfaces for Recommender Systems}, url = {http://link.springer.com/journal/11257/22/4/}, username = {flint63}, volume = 22, year = 2012 }