Abstract
Recommender systems suggest objects to users. One form recommends documents or other objects to users searching information on a web site. A recommender system can use data about a user to recommend information, for example web pages. Current methods for recommending are aimed at optimising single recommendations. However, usually a series of interactions is needed to find the desired information.
Here we argue that in interactive recommending a series of normal, ‘greedy’, recommendings is not the strategy that minimises the number of steps in the search. Greedy sequential recommending conflicts with the need to explore the entire space of user preferences and may lead to recommending series that require more steps (mouse clicks) from the user than necessary. We illustrate this with an example, analyse when this is so and outline when greedy recommending is not the most efficient.
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van Someren, M., Hollink, V., ten Hagen, S. (2004). Greedy Recommending Is Not Always Optimal. In: Berendt, B., Hotho, A., Mladenič, D., van Someren, M., Spiliopoulou, M., Stumme, G. (eds) Web Mining: From Web to Semantic Web. EWMF 2003. Lecture Notes in Computer Science(), vol 3209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30123-3_9
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DOI: https://doi.org/10.1007/978-3-540-30123-3_9
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