Ashley Twichell
Computer Science 49S
Discussion Report
This report summarizes the class discussion on “Personalization”. There were two
assigned readings for this class; the first was “Personalized Search” by James Pitkow, and
the second reading was “Experience with Personalization on Yahoo” by Udi Manber.
Personalized Search
This article begins by discussing contextual computing, which is the development and
improvement of a user’s interactions with a computer. In order for contextual computing
to occur, the user, the context and the applications and information being used must be
understood. This allows for a shift from consensus relevancy, which is when relevancy
for entire population is used for every individual, to personal relevancy, which is when
relevancy is determined for each individual. This shift decreases the time it takes people
to find information, since the computer is able to better understand what a specific
individual is looking for. The discussion then moved on to a review of information
retrieval (which the article also talks about). This review made it easier to better
understand the techniques that are used in personalization that were discussed later. The
three main techniques are content-based approaches, author-relevancy techniques, and
usage rank. Content-based approaches use language to match a query with results, while
author-relevancy uses citation and hyperlinks to match a query with results. Usage rank is
computed from the frequency, recency, and duration of interaction by users. For example,
a search engine will look at how often users click on a particular result, and how long
they stay on the site, to try to rank its relevancy. All of these techniques use the same
relevancy for all users, which does not acknowledge that “relevance is relative” for each
user. The article states that there needs to be a way to “take into account that different
people find different things relevant and that people’s interests and knowledge change
over time”. In class, we talked about how the meaning of the statement that people’s
interests and knowledge change over time. For example, I may be interested in something
now, that I won’t be interested in ten years from now, and won’t be relevant for me
anymore. The class then moved onto talking about two different techniques that are
needed in order to personalize search, as well as the Outride Approach. The first is
contextualization, which is the nature of the information and the information and
applications being used. We talked about how this is looking at the information a user
looks at, and comparing and contrasting when different information and applications are
used. The other technique is individualization, which is the characteristics of a particular
individual that may help determine relevancy of results. For example, knowing the user’s
goals and past information-seeking behaviors can be very helpful. The discussion then
moved onto talking about query augmentation and result processing. Query augmentation
is basically refining the query in an attempt to include more terms. The example we
discussed in class is if a user searches for the term “contour”, the search engine may add
the terms “car” and “ford” to the query, which will allow for results about the Ford
Contour car to be shown. This result processing deals with the results being
individualized; that is, the information is filtered based upon a particular user’s model
and/or context. The user model can “re-rank search results based upon the similarity of
the content of the pages in the results and the user’s profile”. In our class discussion, we
drew a picture of how the individual submits a query, which then is augmented using
Outride, then the results are processed. It is almost as though Outride is in between the
user and the search engine. The discussion then moved on to briefly talk about “Have
Seen, Have Not Seen”, which is a feature that allows new information to be identified
and for users to return information that they have already seen. We talked about how this
is beneficial, because a user can make clear if they want to see all new information, or
information they have already previously been shown. The discussion then moved onto
the Outride Personalized Search System. It is often shown as a sidebar of the Internet,
and it is split up into four separate information spaces. The first is Personal, which is a
personal hierarchy of each user’s links, the next is Directory, which is a catalog/listing of
links (split up into categories), the next is History, which is a user’s past surfing history,
and the last is Web, which is just search results from the entire Web. We then briefly
talked about they tested if Outride was effective and successful. They had participants
perform twelve search tasks with Outride and a different search engine (each task was to
be able to find something specific that they had been asked to find). The two things they
based their results on were the time it took to successfully complete a search as well as
the number of interface actions, such as the number of mouse click and the number of
different queries entered). The results showed that participants were able to find the
answers much more quickly with Outride than with any other search engine – on average,
participants took 39 seconds to complete the tasks using Outride and 75 seconds using
Google. As well, participants used 11 actions when using outride, and 21 when using a
different search engine. We then briefly discussed some of the issues and problems with
the experiments, which were that default profiles were used, as opposed to individualized
profiles. As well, Outride had a clear advantage against other search engines for some of
the tasks. We ended the discussion on this article by talking about the future direction of
personalized search. One of the problems is that a user’s interests change over time. As
well, privacy is an issue, since user’s personal information is available, as well as user’s
interactions with information.
Experience with Personalization on Yahoo!
We opened up the discussion on this article by talking about My Yahoo!, which is a
customized personal copy of Yahoo!. Users are able to select from various different
models, such as news, stock prices, weather and sports scores to put on the Web page.
My Yahoo! provides users with the latest information on every subject, but with only the
specific items they want to know about. In class, we gave the example of how someone
can get the weather for their particular location, sports updates about their favorite teams,
or movie times at theatres in their area. The discussion then moved on to the different
features of My Yahoo!. The first one is personalization, and we talked about the specific
example of users choosing certain TV channels to put in their TV Guide. The next is
Customized Content, and we discussed the example of a sports module that lists the
teams in the user’s area, after getting that information (of the user’s location) from the
user’s profile. Another feature is Automatic Updates, which allows a page to
automatically update at any time interval from 15 minutes to several hours, according to
the user’s preference. The last feature we talked about was Original Module Ability,
which is just the idea of a user having many different modules on their web page. Each
module has an edit and remove button, which makes it much easier for users to easily
change their pictures directly, without needing to visit a separate edit/layout page. The
next Yahoo personalization example we discussed was Yahoo! Companion. Emily
explained that it is a browser’s embedded toolbar from which a user can directly access
most of Yahoo! features from anywhere on the Web. It is like a mini My Yahoo! that
takes a small space at the top of the page, and is always with you. We discussed that a
benefit to Yahoo! Companion is that bookmarks are kept on the server, rather than a
specific computer; therefore, a user’s toolbar will stay with them even if they change
computers. As well, users are able to choose from several different toolbars, and change
them at any time. Emily gave the example of a stock market toolbar. The next Yahoo!
personalization tool is Inside Yahoo!. Emily explained how Yahoo! attempts to
personalize searches using information it can obtain from the user; however, it would be
impossible for Yahoo! to customize every search. Emily then gave the example of a user
looking at show times on a page previously, and had entered a zip code, Yahoo! can now
use that information to show the user movie times in his or her own area. The discussion
then moved onto privacy problems. As Emily stated, any company that collects private
information must “guard that information with its life”. Personal information about
Yahoo! is maintained in a specially designed User Database, which was built on
Yahoo!’s own customized software. Yahoo! also has data replication and distribution
capabilities which allows them to replicate and distribute the UDB over secure links to
remote locations in Asia and Europe. This replication not only makes things faster, but
also ensures that there will be backup in case one system crashes. We finished up the
discussion with observations and lessons learned. First o all, most users take what is
given to them and never customize. As well, people generally don’t understand the
concept of customization. However, it is important to keep customization, since those
who actually do customize use it a lot. Lastly, the main lesson was to learn from the
users.