Traditionally, recommender systems were built with the goal of aiding users' decision-making process by extrapolating what they like and what they have done to predict what they want next. However, in attempting to personalize the suggestions to users' preferences, these systems create an isolated universe of information for each user, which may limit their perspectives and promote complacency. In this paper, we describe our research plan to test a novel approach to recommender systems that goes beyond \good recommendations" that supports user aspirations and exploration.
Recommender systems have become ubiquitous in daily user interactions across
many e-commerce websites, social networking sites and streaming services. The
main purpose of these systems is to provide users with relevant information, and
as such, much of the content consumed online is personally tailored [
Although personalized content has numerous bene ts, presenting items that
are only based on some of users' expressed preferences could hinder the e
ectiveness of the recommender, and trap users in a \ lter bubble" [
Recently, scholars have acknowledged the importance of other user-centered
factors beyond accuracy that contribute to the e ectiveness of recommender
systems [
This could have long-term societal consequences as the persuasive nature of
recommendations could replace human creativity and understanding [
Previous research on critiquing [
Our list of things we think you'll hate contains items that have a low predicted rating for this user, compared to the average predicted rating. To populate this list, compute the di erence between the total average rating of the item and the user predicted rating, with the following formula: items = max(average predicted rating user predicted rating)
This allows users to correct mistakes quickly.
Unknown preferences. It is di cult for recommenders to predict items for which there is insu cient information about whether the user will like them or not. As a result, recommender usually tailor to users' known preferences only. Rather than only catering to known preferences, we propose to display a list of things we have no clue about. This list consists of items with a user predicted rating for which the system has the lowest con dence. Current recommender algorithms do not provide con dence intervals, so for our study we estimate the system's con dence by computing the di erence in user-predicted ratings for di erent algorithms, e.g. matrix factorization (mf) and k-nearest neighbors (knn): items = max(predicted ratingmf
predicted ratingknn)2
This allows the system to learn information about all of a user's preferences, rather than just a subset of their preferences.
Novel items. New items are an enduring complication in recommender systems.
Since users have yet to try them, they rarely show up among the
recommendations. Most recommender systems solve this cold start problem through content
based techniques to approximate predicted ratings. However, this solution
ignores the fact that user may at time actually be excited to try new things, even
if it does not always t their preferences [
users = max(% top rated items with (#ratings < threshold))
items = min(#ratings) Controversial items. Recommenders usually identify a set of users that are similar to the current user, and then calculate recommendations based on the preferences of these nearest neighbors. This often leads to recommendations that the neighbors unanimously like. These \safe" recommendations do not challenge a user's tastes beyond what is generally agreed upon as \good" among like-minded users. However, these neighbors may not always be an unvaried group, and there may be certain polarizing items that some of them really like, but others hate. Our fourth feature will detect these polarizing items. This list of things that are controversial can help users to develop their unique tastes.
Among the four proposed features, identifying polarizing items is arguably the most challenging from an algorithmic perspective. The simplest approach to identify polarizing items is to select items that have the highest rating variability or range (rather than average) among the neighbors:
items = max(var(neighbors0 predicted ratings))
A more sophisticated approach would be to cluster the identi ed neighbors based on their ratings, and then select items that best discriminate between clusters.
The proposed features could improve recommenders? ability to support people in life-altering decisions (e.g. choosing an education, a job, an insurance plan, or a retirement fund) where it is important that they develop a strong sense of determination about the chosen path. The features would also improve recommenders? ability help people make lifestyle choices (e.g., about music, movies, or fashion) based on carefully developed personal tastes. Our proposed plan to test and evaluate these features in a movie recommender system are outlined below. The goal of our research is to develop, test and evaluate a recommender system that supports rather replaces the decision-making process for users. Our proposed features that were mentioned in the previous section will be tested alongside a traditional Top-N recommender. The system will have the capability to display a Top-N recommendation list, as well as the lists of the four new features. We will train the system using the MovieLens dataset. An online experiment will be conducted to test the RSSA features. The experiment will be conducted on Amazon Mechanical Turk (MTurk) with at least 300 participants. Including the traditional \Top-N only" recommendations, the experiment will also test the four RSSA features in combination with a Top-N list (see Fig. 1).
In our study, participants will see two lists of 10 items: one list will be the traditional Top-10 (\Things you might like"), while the other list will be manipulated between-subjects with the following ve conditions: { \More things you might like"; i.e. the next 10 recommendation (Top-11-20) { \Things we think you will hate" { \Things we are not sure about" { \Things you'll be among the rst to try" { \Things that are controversial"
After being randomly assigned to one of the ve experimental conditions,
participants will be asked rate 15 movies that they have seen before, to use as
a base for their recommendations. Next, we will show them the
recommendations the Top-10 list of \things you might like" on the left, while the list on the
right will feature 10 items that are based on the randomly selected experimental
condition. At this point, we will ask participants to rate the movies from the
two lists. After this nal round of rating we will update the two lists, and ask
participants to select one movie that they would watch right now. Finally,
participants will be asked to complete a questionnaire to evaluate their experience
with using the system. The behavioral and objective aspects to be evaluated
are outlined in Table 1. We will adopt highly validated questionnaire items from
previous studies [
Existing scales [
In this paper, we describe a new direction for recommender systems that move towards supporting our aspirational selves rather than pushing content to users based on their history. We outline our research plan for developing and evaluating the new interface and algorithmic features. Aside from this, we are also working with several companies and organizations to build these new features into real-life recommenders. We believe that our Recommender Systems for Self-Actualization acknowledges the multidimensionality and evolving nature of human beings, and can fundamentally change the way recommender systems are used.
This research was supported in part by the NSF award IIS 1565809.