Tag Archives: recommendation

Blog Google information-retrieval information design keywords last.fm learnability music search-engines site-navigation spatial maps tag clouds Teoma trust Usability Web2.0 Writing

How mainstream is your music taste, and which reviewer should you believe?

Here’s something interesting: the Last.fm mainstream-o-meter. Apparently my music tastes are 41.48% mainstream, at least within the Last.fm community. The biggest boost to my mainstreamness is Radiohead, which is listened to by an astounding 103.56% of Last.fm users.

Last.fm no doubt attracts a skewed population, but I do have to say I’m surprised that it continues to differ from radio playlists and CD sales. Radiohead is a perfect example – from my sampling of commercial radio over the past few years I would say they are almost completely absent. Yet a large number of people listen to Radiohead on their PCs.

Next up is a page that tells you which movie reviewer has tastes that best match yours. I’m sure we’ve all read reviews online or in the local paper and wondered if the reviewer saw the same movie. With sites like Rotten Tomatoes and Metacritic, you’re n longer limited to the opinions of a few writers. The average scores on those sites are interesting, but still don’t always match my tastes or your tastes. This will give you some names to look out for.

Notes: Helping people find what they don’t know

Nicolas J. Belkin, Helping people find what they don’t know, Communications of the ACM, v.43 n.8, p.58-61, Aug. 2000

In this article, Belkin argues that since people generally start searching for information when they don’t know much about a subject. It is therefore problematic that many search systems require knowledge of the domain in order to get good results, for example when users do not know either the specific keywords or controlled vocabulary of the system. His group feels that the best way around this is for the system to make suggestions along the way. There are two techniques that can be used: system-controlled, where the user’s query is enhanced automatically by the system using algorithms like word frequency, and user-controlled, where the user is given the results of their query along with suggestions to make it more effective. The author’s team found that suggestions were most effective when the user was able to control which suggestions were used and when the user knew how the suggestions were generated and was comfortable with the results.

The author’s findings seem both intuitive and promising. It makes sense that in an interactive structured searching system giving the user suggestions and allowing them to take them or leave them would work well, and the suggestions should neither be bizarre or mysterious. But with the rise of the World Wide Web, I think it’s pretty clear that users with less domain knowledge prefer less-structured searching environments. In my experience, users who are new to a system will type unstructured, keyword queries into anything that even looks like a search box, even if it is clearly labeled as a field for author name, product code, or start date. Power users, on the other hand, often have more knowledge about the data then the system’s programmers—so for these sorts of suggestions to be useful, the algorithm would need to do more than just call up synonyms. The article makes it clear that these findings are early, so I would be interested to see what they have come up with since 2000.

These ideas could be applied to both structured and unstructured searching environments, though my guess is that they would be easier to implement in more structured environments because the structure of the system can be used to generate the suggestions. There certainly have been a number of projects which have tried to provide something like this with general web searching. Rudimentary systems like Google Suggest  or more advanced ones like Teoma show off the potential. Notice, however, that neither of these has exactly taken the search engine industry by storm, meaning people are apparently happy to muddle along with plain keyword searching and advanced ranking algorithms. I do wonder if their finding that users liked to have some idea about how suggestions were found would apply here as well—would users be happier with Google if they were told why PageRank picked a certain site as the number one result? Since the algorithms used by Google, Yahoo, MSN and others are trade secrets I doubt we’ll see anything like that in the near future. On the other hand, Amazon.com’s recommendation engine does tell the user why a certain book was suggested, and allow the user to remove certain suggestions. Although it is not really a search tool, it follows the precepts discussed here and seems to be successful.

Information visualizations and spatial maps on the web – Usability concerns

Visualizing the web

Although web technologies are constantly changing, most users still browse the web the same way they did back in 1995–typing keywords into search boxes, clicking from home page, to section, to subsection on a navigation bar, or following link, to link, to link. The fact that it is called a “web” suggests that there should be other ways of navigating websites, and there are a number of projects attempting to employ information visualizations and spatial maps to do so.

All web pages organize information visually, but “information visualization centers around helping people explore or explain data that is not inherently spatial, such as that from the domains of bioinformatics, data mining and databases, finance and commerce, telecommunications and networking, information retrieval from large text corpora, software, and computer-supported cooperative work.” (“InfoVis 2003 Symposium”) Spatial metaphors are used to communicate different levels of information. A simple, static example would be a personal homepage built to look like the designers home, with links to favorite movies in the living room and recipes in the kitchen. A more advanced example would be a customer relationship management system for a large company which instead of presenting a list of technical support problems and solutions, displays an interactive map of problems, with more common problems in a larger font size, and recent problems in red. In both cases, users get an immediate grasp of complex information.

Such visualizations are intended to help solve two current web usability problems: the lack of a wide view to web structure, and the lack of query refinement based on relationships of retrieved pages (Ohwada 548). But they must do so without creating additional usability barriers. This paper will describe three current information visualization projects and describe some of the usability issues these sorts of projects face.

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