intellectual excitement. “People like us are motivated by the research, not necessarily by the money,” says Chris Volinsky, executive director of statistics research at AT&T Research and a member of BellKor’s Pragmatic Chaos. “Having an academic flavor to the competition has really helped it to sustain energy for two-and-a-half years.”

Although Netflix is unique in publicly enlisting and rewarding outside researchers, many other companies are fine-tuning the choices their recommender systems present to customers. Some of their efforts, like those of Amazon, L.L. Bean, and i Tunes, are obvious to users. Other companies work behind the scenes, quietly monitoring and personalizing the experience of each user. But either way, user satisfaction depends on not just new and improved algorithms, but individual human preferences, with all of their many quirks.

The Netflix Prize has brought a lot of attention to the field, notes John Riedl, a professor of computer science at the University of Minnesota. However, Riedl worries that the Netflix Prize puts “a little too much of the focus on the algorithmic side of the things, whereas I think the real action is going to happen in how you build interfaces … that expose the information in more creative and interesting ways.”

 

Implicit and explicit Information To entice its customers to rate movies, Netflix promises to show them other

movies they will enjoy. Netflix also encourages its customers to provide detailed information about their viewing preferences. Unfortunately, this rich, explicit feedback demands a level of user effort that most Web sites can’t hope for.

Instead, many companies rely on implicit information about customer preferences, such as their purchasing history. However they get the feedback, though, researchers must manage with a sparse data set that reveals little of many customers’ tastes about most products. A further, critical challenge for Web-based recommender systems is generating accurate results in less than a second. To maintain a rapid response as databases grow, researchers must continually trade off effectiveness for speed.

One popular and efficient set of methods, called k nearest neighbors, searches for a handful of other customers (k of them) who have chosen the same items as the current customer. The system then recommends other items chosen by these “neighbors.”

In contrast, latent factor methods search customers’ choices for patterns that can explain them. Some factors have obvious interpretations, such as a user’s preference for horror films, while other statistically important factors have no obvious interpretation. One advantage of latent-factor methods is they can provide recommendations for a new product that has yet to generate much consumer data.

These algorithms all aim to solve the generic problem of correlating preferences without invoking knowledge of the domain they refer to, such as clothing, movies, and music. In principle, notes Joseph Konstan, a professor of computer science and engineering at the University of Minnesota, as long as individuals’ preferences remain constant, then with enough opinions from a sufficient number of people, “you don’t need to know anything about the domain.” In practice, Konstan says, limited data and changing tastes can make domain-specific approaches more effective.

One of the most sophisticated domain-specific approaches is used by Internet-radio company Pandora, which employs dozens of trained musicologists to rate songs on hundreds of attributes. “We are of the opinion that to make a good music recommendation system you need to understand both the music and the listeners,” says Pandora Chief Operating Officer Etienne Handman. Still, the most enjoyed Pandora playlists, he says, supplement the musicologists’ sophisticated ratings with statistical information from users.

 

measuring effectiveness To win the Netflix Prize, a team must beat Cinematch by 10% on a purely statistical measure, the root mean square error, of the differences between predicted and actual ratings. Like content-based assessments, however, this ob-

Obituary

Rajeev Motwani, Google Mentor, Dies at 47

Rajeev motwani, a professor of computer science at stanford University who mentored many students and silicon Valley entrepreneurs, including the founders of Google, died in an apparent accidental drowning on June 5. He was 47. motwani was well known for his theoretical research on randomized algorithms and his contributions in data mining, Web search, information

retrieval, streaming databases, and robotics. He is the author of two classic computer science textbooks, Randomized Algorithms, with prabhakar Raghavan, and Introduction to Automata Theory, Languages and Computation, with John Hopcroft and Jeffrey Ullman.

motwani served as director of graduate studies in the computer science department at stanford and founded the mining Data at stanford (miDAs) project. He was known as a friendly, well-respected professor who always made

himself available to advise and mentor young entrepreneurs and students, including Google cofounders sergey Brin and larry page when they were graduate students at stanford in the mid-1990s.

“the Google founders used both his technical expertise and his understanding of how technology can transition into the real world. He was helpful in that regard,” recalls stanford computer science professor Balaji prabhakar, a friend of motwani. “many former students and silicon Valley

folks have sought out Rajeev’s
advice and input. He was a
generous person who saw the
potential in people and their
ideas.”
motwani was also an angel
investor and technical advisor
for many startup companies in
silicon Valley, and sat on the
board of numerous companies,
including Google, Kaboodle,
and mimosa systems.
motwani’s research earned him
numerous awards, including the
Gödel prize and an Arthur p. sloan
Foundation Research Fellowship.
— Wylie Wong