  Today, intelligent user interfaces on the web often come in form of
recommendation services tailoring content to individual users. Recommendation
of web content such as news articles often requires a certain amount of
explicit ratings to allow for satisfactory results, i.e., the selection of
content actually relevant for the user. Yet, the collection of such explicit
ratings is time-consuming and dependent on users' willingness to provide the
required information on a regular basis. Thus, using implicit interest
indicators can be a helpful complementation to relying on explicitly entered
information only. Analysis of reading behavior on the web can be the basis for
the derivation of such implicit indicators. Previous work has already
identified several indicators and discussed how they can be used as a basis for
user models. However, most earlier work is either of conceptual nature and does
not involve studies to prove the suggested concepts or relies on meanwhile
potentially outdated technology. All earlier discussions of the topic further
have in common that they do not yet consider mobile contexts. This paper builds
upon earlier work, however providing a major update regarding technology and
web reading context, distinguishing between desktop and mobile settings. This
update also allowed us to identify a set of new indicators that so far have not
yet been discussed. This paper describes (i) our technical work, a framework
for analyzing user interactions with the browser relying on latest web
technologies, (ii) the implicit interest indicators we either revisited or
newly identified, and (iii) the results of an online study on web reading
behavior as a basis for derivation of interest we conducted with 96
participants.
