Information Technology Reference
In-Depth Information
•
In most of the methods introduced in this chapter, the number of parameters (to
learn) is much larger than that for the conventional ranking methods. As a result,
the learning process in these methods might be easy to over fit. It is critical to
control the risk of over-fitting.
•
In the above descriptions, we have mainly focused on the algorithmic challenges
for query-dependent ranking. In addition to this, there are also many engineering
challenges. For example, in many methods, one needs to extract query features on
the fly, and then generate a ranking model or select the most appropriate ranking
model from a large number of candidates based on query features. This obviously
increases the response time of search engines. If one cannot find a highly efficient
way to solve this problem, the user experience will be hurt.
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