Most of the existing recommender systems use the ratings provided by users on individual items. An additional source of preference information is to use the ratings that users provide on sets of items. The advantages of using preferences on sets are two-fold. First, a rating provided on a set conveys some preference information about each of the set's items, which allows us to acquire a user's preferences for more items that the number of ratings that the user provided. Second, due to privacy concerns, users may not be willing to reveal their preferences on individual items explicitly but may be willing to provide a single rating to a set of items, since it provides some level of information hiding. This paper investigates two questions related to using set-level ratings in recommender systems. First, how users' item-level ratings relate to their set-level ratings. Second, how collaborative filtering-based models for item-level rating prediction can take advantage of such set-level ratings. We have collected set-level ratings from active users of Movielens on sets of movies that they have rated in the past. Our analysis of these ratings shows that though the majority of the users provide the average of the ratings on a set's constituent items as the rating on the set, there exists a significant number of users that tend to consistently either under- or over-rate the sets. We have developed collaborative filtering-based methods to explicitly model these user behaviors that can be used to recommend items to users. Experiments on real data and on synthetic data that resembles the under- or over-rating behavior in the real data, demonstrate that these models can recover the overall characteristics of the underlying data and predict the user's ratings on individual items.

Recommender systems are widely used to recommend the most appealing items to users. These recommendations can be generated by applying collaborative filtering methods. The low-rank matrix completion method is the state-of-the-art collaborative filtering method. In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches. Also, we show that the number of ratings that an item or a user has positively correlates with the ability of low-rank matrix-completion-based approaches to predict the ratings for the item or the user accurately. Furthermore, we use these insights to develop four matrix completion-based approaches, i.e., Frequency Adaptive Rating Prediction (FARP), Truncated Matrix Factorization (TMF), Truncated Matrix Factorization with Dropout (TMF + Dropout) and Inverse Frequency Weighted Matrix Factorization (IFWMF), that outperforms traditional matrix-completion-based approaches for the users and the items with few ratings in the user-item rating matrix.

Recommending new items to existing users has remained a challenging problem due to absence of user's past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user's previous preferred items. While computing similarities based on item features, these methods overlook the interactions among the features of the items and consider them independently. Modeling interactions among features can be helpful as some features, when considered together, provide a stronger signal on the relevance of an item when compared to case where features are considered independently. To address this important issue, in this work we introduce the Feature-based factorized Bilinear Similarity Model (FBSM), which learns factorized bilinear similarity model for TOP-n recommendation of new items, given the information about items preferred by users in past as well as the features of these items. We carry out extensive empirical evaluations on benchmark datasets, and we find that the proposed FBSM approach improves upon traditional non-collaborative methods in terms of recommendation performance. Moreover, the proposed approach also learns insightful interactions among item features from data, which lead to deep understanding on how these interactions contribute to personalized recommendation.

In order to help undergraduate students towards successfully completing their degrees, developing tools that can assist students during the course selection process is a significant task in the education domain. The optimal set of courses for each student should include courses that help him/her graduate in a timely fashion and for which he/she is well-prepared for so as to get a good grade in. To this end, we propose two different grade-aware course recommendation approaches to recommend to each student his/her optimal set of courses. The first approach ranks the courses by using an objective function that differentiates between courses that are expected to increase or decrease a student's GPA. The second approach combines the grades predicted by grade prediction methods with the rankings produced by course recommendation methods to improve the final course rankings. To obtain the course rankings in the first approach, we adapt two widely-used representation learning techniques to learn the optimal temporal ordering between courses. Our experiments on a large dataset obtained from the University of Minnesota that includes students from 23 different majors show that the grade-aware course recommendation methods can do better on recommending more courses in which the students are expected to perform well and recommending fewer courses in which they are expected not to perform well in than grade-unaware course recommendation methods.

Matching Amazon's new free music offer on Echo speakers, Google now offers owners of the Google Home speaker access to free tunes, via YouTube Music. There's a big difference: songs are sponsored on Amazon Echo speakers, while tunes for Google users are free. Additionally, they can be listened on both the Google Home speaker line, and any speaker that has the Google Assistant, Google's Siri/Alexa like helper. That includes the brands Sony, JBL, Harman and others. What you can't get is on-demand song selection, but instead playlists or radio stations created based on your requests.

Details on what Apple may have in store for the next major versions of its operating systems are trickling out ahead of June's Worldwide Developers Conference. The latest leaks are linked to macOS 10.15, to which Apple could add some iOS features, such as Siri Shortcuts and Screen Time, according to 9to5 Mac. Apple revealed Siri Shortcuts at WWDC last year. It opens up the voice assistant, allowing you to create custom Siri commands for various apps, though you might need to download a Marzipan app (those ported from iPad to Mac) from the Mac App Store to use Shortcuts on your computer. It's a move that makes sense, as it should allow developers to make sure the Shortcuts for their apps still work after they port them to macOS.

Amazon, like many other tech companies investing heavily in artificial intelligence, has always been forthright about its Alexa assistant being a work in progress. "The more data we use to train these systems, the better Alexa works, and training Alexa with voice recordings from a diverse range of customers helps ensure Alexa works well for everyone," reads the company's Alexa FAQ. What the company doesn't tell you explicitly, as highlighted by an in-depth investigation from Bloomberg published this evening, is that one of the only, and often the best, ways Alexa improves over time is by having human beings listen to recordings of your voice requests. Of course, this is all buried in product and service terms few consumers will ever read, and Amazon has often downplayed the privacy implications of having cameras and microphones in millions of homes around the globe. But concerns about how AI is trained as it becomes an ever more pervasive force in our daily lives will only continue to raise alarms, especially as most of how this technology works remains beyond closed doors and improves using methods Amazon is loathe to ever disclose. In this case, the process is known as data annotation, and it's quietly become a bedrock of the machine learning revolution that's churned out advances in natural language processing, machine translation, and image and object recognition.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. Get your Nordstrom cards out, people. There is a sale going on right now at the beloved retailer, and it's good. I literally shopped the entire sale online, so I can confirm: this is a must-shop event (my bank account is shaking).

Amazon Fire TV Stick & nbsp; & nbsp; & bull; Price: $24.99 & nbsp; & nbsp; & bull; Savings: $15.00 & nbsp; & nbsp; & bull; Percent off: 37.5% & nbsp; & nbsp; ALSO READ: 40 Places Young People Are Moving (Photo: Courtesy of Target) Google and Amazon, longtime corporate adversaries who have been feuding for years, have made up. Consumers who have complained about not being able to watch YouTube or the cutting-the-cord YouTube TV service via Amazon's popular Fire TV Stick streaming products and the Amazon branded Fire TV Edition TV sets will finally get to do so later this year. Additionally, Google is bringing Amazon's Prime Video service to the Android TV platform and the Google Chromecast streaming device. Still, the companies have a ways to go. You still won't be able to see YouTube on Amazon's Echo Show, the video version of the Alexa speaker, nor will Amazon be opening up the doors to sell products like the Google Home speaker (Alexa's main rival) or the Pixel phone, in its eStore.

Amazon officially began offering free ad-supported music for owners of Echo speakers Thursday, even if they don't subscribe to Prime, Amazon's expedited shipping and entertainment offering. Amazon already provides some 2 million songs free, but without ads to Echo owners who also pay $119 yearly to subscribe to Prime.A more full-featured ad-free music offering, Amazon Music Unlimited, is available for $9.99 monthly, or $7.99 monthly for Prime members. Amazon says it has access to around 50 million songs. The new free service has limitations, more akin to online radio station Pandora than Apple Music in that customers can't request a specific on-demand song. Instead, the request will lead to a music station "based" on that song, or a pre-existing Amazon playlist.