Nevena Dragovic, Ion Madrazo Azpiazu and Maria Soledad Pera. 2016. “Is sven seven?’:’ A search intent module for children”. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR 2016).
The Internet is the biggest data-sharing platform, comprised of an immeasurable quantity of resources covering diverse topics appealing to users of all ages. Children shape tomorrow’s society, so it is essential that this audience becomes agile with searching information. Although young users prefer well-known search engines, their lack of skill in formulating adequate queries and the fact that search tools were not designed explicitly with children in mind, can result in poor outcomes. The reasons for this include children’s limited vocabulary, which makes it challenging to articulate information needs using short queries, or their tendency to create queries that are too long, which translates to few or irrelevant retrieved results. To enhance web search environments in response to children’s behaviors and expectations, in this paper we discuss an initial effort to verify well-known issues, and identify yet to be explored Behaviorism is the currently-dominant paradigm for building and evaluating recommender systems. Both the operation and the evaluation of recommender system applications are most often driven by analyzing the behavior of users. In this paper, we argue that listening to what users say about the items and recommendations they like, the control they wish to exert on the output, and the ways in which they perceive the system and not just observing what they do will enable important developments in the future of recommender systems. We provide both philosophical and pragmatic motivations for this idea, describe the various points in the recommendation and evaluation processes where explicit user input may be considered, and discuss benefits that may result from considered incorporation of user preferences at each of these points. In particular, we envision recommender applications that aim to support users’ better selves: helping them live the life that they desire to lead. For example, recommender-assisted behavior change requires algorithms to predict not what users choose or do now, inferable from behavioral data, but what they should choose or do in the future to become healthier, fitter, more sustainable, or culturally aware. We hope that our work will spur useful discussion and many new ideas for recommenders that empower their users.