Recommenders can skew results

By Kimberly Patch, Technology Research News

Just how accurate are the recommender systems online media sellers use to allow buyers to pass on their judgments about books, movies and CDs to their fellow consumers?

Researchers from the University of Minnesota have shown that the way recommender systems are set up can affect the opinions they evoke, and that artificially high or low recommendations can raise or lower subsequent recommendations.

Displaying a prediction introduces bias, said Joseph Konstan, an associate professor of computer science and engineering at the University of Minnesota. "Lying by [skewing rankings] higher or lower... biases the subsequent rating in that direction," he said. "Even the 'correct' rating led people to select that value more often."

The distortion this chain of events induces may influence consumer buying in the short-term, but adversely affects long-term consumer trust in the system, said Konstan. "While a system can get away with a small degree of lying... in the long run dishonesty erodes trust and satisfaction," he said.

The researchers' work is consistent with a long line of psychology studies showing that people shift opinions to conform to a group, said Konstan. "There's a bunch of psychology research that suggests that people exhibit a desire to conform," he said.

In a 1969 study published in Sociometry, for instance, a research team headed by Serge Moscovici found that about a third of the people in a group would call a blue block green if the researchers planted a couple of vocal people in the group who called the block the wrong color.

The Minnesota researchers conducted three experiments with a total of 536 people in order to see how previous ratings affected the test subjects' recommendations.

They used the Movie Lens recommender system, which includes about 70,000 users, 5,600 movies and around 7 million ratings.

In the first experiment, the researchers asked participants to rate 40 movies the participants had previously rated. The experiment presented lists of 10 movies using four different recommender configurations. The participants used a 1- to 5-star rating scale. One configuration showed no predictions, the second showed predictions equal to the user's original rating, the third showed predictions one star above the original rating, and the fourth showed predictions one star below the original rating.

The results revealed that people were fairly consistent in re-rating movies when there were no other ratings on-screen. Participants gave the movies the same ratings 60 percent of the time, one star below the original rating 20 percent of the time and one star above the original rating 20 percent of the time.

The results also showed that having other ratings on screen, whether they matched the user's original rating or were one star up or down, influenced the second rating the user gave. When ratings were bumped up or down one star, participants rated nearly 30 percent of movies one star above or below the original rating, respectively.

In the second experiment, a group of people rated 48 movies for the first time. The researchers predicted what people's ratings would be, then added or deleted stars in the same way as in the first experiment. They then repeated the experiment with a control group without manipulating the ratings shown to the participants.

The users were again swayed by incorrect ratings. In addition, those shown incorrect ratings were more dissatisfied with the process than the control group, probably because they sensed that the predictions were inaccurate, according to Konstan.

Other research shows that people treat computers socially, similarly to the way they treat other people, said Konstan. "We speculate that this effect may be skewing ratings towards the computer-displayed prediction," he said.

The research did not distinguish between the users' actual preferences and the ratings they entered, said Konstan. "We do not know whether [the rating system] really changes the persons' preference, or just the rating they choose to enter," he said.

Following up on their 1969 experiment, Moscovici's group looked at people's actual preferences in addition to what they said, and showed that even those who did not call the blue block green rated blue-green slides as more green than pretests predicted they would. The researchers produced similar results after going a step further by asking participants to rate the color of the afterimage they saw after looking at the slide. Afterimages are involuntary artifacts manufactured by the human visual system.

The Minnesota study confirms the line of research that shows that people tend to conform with suggestions, and points out that care is needed to avoid introducing biases in information interfaces, said Konstan.

In a third experiment, the researchers asked users to rate three sets of 15 movies they had previously rated using three different scales: thumbs up or thumbs down, a scale from -3 to +3 not including a zero, or a 0.5 to five-star scale in half-star increments.

This experiment showed that people prefer finer-grained scales, and that finer-grained scales are ultimately more accurate. Participants rated the half-star scale the most satisfactory followed by the plus or minus three scale, and were least satisfied with the binary scale. The finer-grained scales are more accurate because people tend to give borderline movies the benefit of the doubt when forced to rate on a coarse scale, according to Konstan.

To evoke recommendations that are as independent as possible, recommender systems should give consumers an environment that allows them to provide ratings without having to see previous ratings, Konstan said. And the system should provide fine-grained rating scales rather than simpler thumbs up, thumbs down ratings, he said.

The Minnesota researchers are ultimately aiming to better understand how interfaces, social and economic structures, and other design factors influence people's participation in and use of recommender systems, said Konstan. The design implications of the current results can be used immediately to improve recommender sites, he said.

Konstan's research colleagues were Shyong K. Lam, Istvan Albert and John Riedl. They presented the results at the Association of Computing Machinery (ACM) Computer-Human Interaction conference held in Fort Lauderdale, Florida April 5-10, 2003. The research was funded by the National Science Foundation (NSF).

Timeline:   Now
Funding:   Government
TRN Categories:   Internet
Story Type:   News
Related Elements:  Technical paper, "Good Ratings Gone Bad: Study Shows Recommender Systems Can Manipulate Users' Opinions," presented at the Association of Computing Machinery Computer-Human Interaction (ACM-CHI) conference, Fort Lauderdale, Florida, April 5-10, 2003; "Influences of a Consistent Minority on the Responses of a Majority in a Color Perception Task," Sociometry 32 Moscovisi & Personnaz, 1980.


July 2/9, 2003

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