By LISA GUERNSEY

WHEN Mike Kellogg of Chicago shops at Amazon.com, he can

view a list of recommended products that match his taste in

music with uncanny precision. The store knows he loves

Wilco, a folksy rock band, and offers him CD's with a

similar sound.

When Anne Heilemann of Iowa City visits Amazon, she is

immediately reminded how clueless the recommendations can

be. A few months ago she bought a book about flower girls

for a friend's 7-year-old daughter, who will be in her

wedding this month. Now Amazon offers her more flower-girl

books (one is more than enough, Ms. Heilemann said), and

assumes that she must also need a book about bargains on

baby products.

"I would freak if I needed a baby-bargains book right now,"

she said.

Mr. Kellogg's experience is a dream come true for

developers of recommender systems - software that analyzes

patterns in a customer's choices to predict what else that

person might want or need. In addition to Amazon's system,

the better-known examples include TiVo, the digital

television recorder, and NetFlix, the online DVD rental

service.

But Ms. Heilemann's experience may ring truer to most

people. Only 7.4 percent of online consumers who noticed

these systems said they often purchased recommended

products, according to a report issued in February by

Forrester Research. About 22 percent said they found the

recommendations valuable, and about 42 percent said the

products listed were not of interest.

To improve the recommendations, many software developers

are doing an about-face from the mid-1990's, when they put

their energy into getting computers to do all the work.

Today they say that automated programs that look for

patterns in customer data are not smart enough to detect a

gaffe. Something more sophisticated is required: the human

mind.

People are becoming a critical component: analysts who

understand why a particular type of music appeals to some

people, categorization experts who know how to

cross-reference material, retail executives who tweak the

system to improve the bottom line and reviewers who check

for nonsensical or offensive results.

"The holy grail is to be able to capture all the customer's

interactions in detail and get smarter about what not to

recommend," said Usama Fayyad, chief executive and

president of digiMine, the software company behind the

online recommendations of J. Crew and Barnes & Noble. "We

can recommend very well. Knowing when not to bother someone

is much harder."

Odd pitches and poor matches have led to an outpouring of

anecdotes. A discussion on a bulletin board at Salon.com

this year titled "When Customer Profiling Goes Wrong"

described people's befuddlement upon receiving off-the-wall

recommendations from Amazon. Someone named Molly wrote that

she bought "a single trashy romance novel" and is now

"branded for life."

"The best results are achieved from powerful technology and

human intervention," said Matt Turck, president of

TripleHop Technologies, a company that has built

recommendation engines for USA Today's online travel

section and SkiMatcher, which advises travelers on ski

resorts.

All this talk of human intervention sounds very different

from the hype of the dot-com boom, when startup companies

spun visions of computer programs that could help people

discover their yet-to-be-revealed tastes in books and

music. Imaginative online software was more coveted than

gold, and recommendation systems looked like a step toward

the creation of artificial intelligence. It was hard not to

be intrigued by the idea that with the right data and the

right mathematical formula, a computer might be able to

grasp a person's preferences better than friends and

family, suggesting books or movies that the consumer would

not have discovered otherwise.

"There is a sense that with people you are perceived

stereotypically, but that the system might give you a

totally different chance," said Rashmi Sinha, the founder

of Uzanto Consulting, a company that focuses on end-user

experiences with technology. She is also a cognitive

psychologist who has conducted studies of how people

respond to recommender systems.

In the mid-1990's, much attention was drawn to

collaborative filtering, a technique that matches a user to

a group of others who have purchased or praised similar

products, then analyzes the group's data to predict what

else the user might like. Patti Maes, an expert in the

field and a professor at the Media Lab of the Massachusetts

Institute of Technology, called it "automating

word-of-mouth." Firefly, a company she helped start, became

the symbol of the technology's promise. The New York Times

Magazine ran a 4,200-word article in 1997 about Firefly,

which was then valued at $100 million, Microsoft bought the

company the following year.

It turned out that Microsoft hadn't bought Firefly for its

collaborative filter. It wanted the software that kept

track of user profiles, which Firefly had called

"passport," for what became Microsoft's own Passport

software for the quick transfer of personal data.

"They weren't so much interested in the recommendation

engine," Dr. Maes said in an interview last month. "It

wasn't because they didn't believe in it, but it wasn't as

good a match for their strategy."

Collaborative filtering is only a piece of today's

recommendation technology. "There was this great

expectation that it was going to be this killer app, and it

didn't meet people's expectations," said Jack Aaronson, who

helped to design the technology for a recommendation

company called Open Sesame and who now runs the Aaronson

Group, a consulting practice.

Some of the problems with collaborative filtering are

common enough that they have earned nicknames, like the

cold start problem and the popularity effect.

To make interesting matches, a company needs a large number

of people who have rated or purchased a large number of

products. The cold start phenomenon arises when a Web site

opens but neither of those criteria has been met. Joe Smith

might buy the same book as Jane Doe, but without more data,

it would be a stretch to predict that the next book Joe

buys is one that Jane might want.

To counter that, some companies employ human editors to

make the first connections between products and likely

purchase patterns. At Barnesandnoble.com, an editorial

staff makes recommendations. Choicestream, the company that

creates the MyBestBets technology used by America Online,

has brought in analysts to distill the defining attributes

of television programs ("thought-provoking" is one example)

and uses the computer to match them with other programs

that have been similarly categorized.

"If it is not vetted and monitored by humans and not

complemented by actual hand-selling, as we say in the book

industry, it doesn't feel like there is anybody there,"

said Daniel Blackman, vice president for books, video and

music for Barnesandnoble .com.

The popularity effect is at work when results delivered by

the computer are boring and obvious. MediaUnbound, the

company that develops the recommendation engine for the MP3

service PressPlay, has been analyzing the more than four

million MP3 collections that are open for browsing through

Napster, KaZaA and other file-sharing services. Michael S.

Papish, the company's chief executive, said that if he were

to run that data through a collaborative filter to predict

musical taste, one band would be at the top of the list for

every single person: the Beatles.

Because of the Beatles' name recognition and popularity,

they are likely to be in anyone's collection, regardless of

their taste. But if MediaUnbound were to put the Beatles at

the top of every recommendation list, their service would

seem stale and uninspired.

To address that problem, the company built some new rules

into the software. But it also hired music analysts to

scout the music scene for new bands, seed the databases

with interesting acts and build genre maps to show how

musical tastes are connected.

"The computer is good at averaging things; it rounds it

out, sands it down," Mr. Papish said. "And then we use the

humans to bring back the exciting rough edges."

Colin Wambsgans, an analyst at MediaUnbound, said he was

working last week on a recommendation list generated for

consumers who like the Flatlanders, a 1970's band from

Texas that recently released a new album. He moved Squirrel

Bait, a rock band with a harder sound, down a few notches

and pushed another band, the Palace Brothers, up. "The

style of music wasn't quite the same," he said. "The Palace

Brothers have more of a folksy sound."

Even Mr. Kellogg's experience with helpful taste-matching

at Amazon was a case of a human's coming to the rescue. To

make sure that he gets recommendations that follow his

tastes, Mr. Kellogg, 29, a product manager in Chicago, has

spent a good deal of time editing his online profile. He

wants Amazon to know that he loves Bob Dylan, so he has

given high ratings to those CD's and dozens of other

favorites. When he makes a purchase for someone else - like

a karaoke CD for his 7-year-old cousin - he checks a box

that tells Amazon to ignore it.

"I actively work at my recommendations," Mr. Kellogg said.

Jason Kilar, vice president of worldwide software

operations for Amazon, would not comment on how many people

use the editing feature; Christopher M. Kelley, an analyst

at Forrester, said he figured that most people never take

the time to use it. But it has become an important tool for

taking into account the complexity of human interactions.

Mr. Kilar said that customers need to be able to

communicate, for example, "Please exclude this one because

I was temporarily out of my mind and I thought I liked

Sade."

Business sense is the latest layer to be added to

recommendation technology. Some companies want to be able

to weight the results that appear on a recommendation list

so that products they want to clear out appear at the top

and out-of-stock items are suppressed. "The technology

needs to be able to support this," said Dr. Fayyad, whose

digiMine software offers such options. But Dr. Maes and

some software developers warn that if companies allow the

bottom line to dictate their recommendations, shoppers may

distrust their systems.

Amazon says it is holding out against such tweaking. "We

let the recommendation engine do its magic," Mr. Kilar

said. "We are extremely pure."

That is giving Ms. Heilemann, a 29-year-old development

director for a university in Iowa, a good laugh. "Am I

missing something in my life?" she asked. "Maybe I really

do need this booster bundle thing for my computer or this

Wiggles 'Yummy Yummy' DVD," a video for the toddler set.

"But they do think that I need a total body yoga workout,"

she added, "and they might be right about that."