  When a user requests a web page from a web archive, the user will typically
either get an HTTP 200 if the page is available, or an HTTP 404 if the web page
has not been archived. This is because web archives are typically accessed by
URI lookup, and the response is binary: the archive either has the page or it
does not, and the user will not know of other archived web pages that exist and
are potentially similar to the requested web page. In this paper, we propose
augmenting these binary responses with a model for selecting and ranking
recommended web pages in a Web archive. This is to enhance both HTTP 404
responses and HTTP 200 responses by surfacing web pages in the archive that the
user may not know existed. First, we check if the URI is already classified in
DMOZ or Wikipedia. If the requested URI is not found, we use ML to classify the
URI using DMOZ as our ontology and collect candidate URIs to recommended to the
user. Next, we filter the candidates based on if they are present in the
archive. Finally, we rank candidates based on several features, such as
archival quality, web page popularity, temporal similarity, and URI similarity.
We calculated the F1 score for different methods of classifying the requested
web page at the first level. We found that using all-grams from the URI after
removing numerals and the TLD produced the best result with F1=0.59. For
second-level classification, the micro-average F1=0.30. We found that 44.89% of
the correctly classified URIs contained at least one word that exists in a
dictionary and 50.07% of the correctly classified URIs contained long strings
in the domain. In comparison with the URIs from our Wayback access logs, only
5.39% of those URIs contained only words from a dictionary, and 26.74%
contained at least one word from a dictionary. These percentages are low and
may affect the ability for the requested URI to be correctly classified.
