Mohit Sharma
F. Maxwell Harper
Abstract
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 twofold. 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 than 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 article 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.
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Index Terms
- Learning from Sets of Items in Recommender Systems
Recommendations
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Adaptive matrix completion for the users and the items in tail
WWW '19: The World Wide Web ConferenceRecommender 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 ...
Correcting noisy ratings in collaborative recommender systems
Recommender systems help users to find information that best fits their preferences and needs in an overloaded search space. Most recommender systems research has been focused on the accuracy improvement of recommendation algorithms. Despite this, ...
Reviews
Brijendra Singh
Recommender systems are sets of computer algorithms or methods, implemented to provide suggestions or recommendations of relevant items to users. With the intensification of web services, from e-commerce to online advertising, recommender systems have become inevitable while using these online services. Most of the collaborative filtering methods utilize item-based past preferences provided by users. This paper explores an additional source of preferences "provided by users on sets of items," for example, ratings on a complete music album. Further investigation is done to describe "the user behavior related to rating sets [of items]" and "item-level rating predictions." Due to restrictions or privacy, users provide set-level ratings, but this mechanism does expose some user preference for many items. Apparently, this research evidently focuses on how a user's item-based preference conveys to their whole set-level preference, and how the existing item-based collaborative filtering model can benefit from such set-level ratings. Various models "for predicting the ratings that users will provide to the individual items" are discussed in detail, as well as how to "use these item-level ratings to derive set-level ratings." Model learning algorithms are very well defined. The paper includes a thorough statistical analysis, and the list of references is comprehensive. The authors use graphs, figures, and formulas to demonstrate their area of research. Performance testing and analysis of the proposed methods is performed on synthetically generated and real datasets from a popular online movie recommender system. This novel study is worthwhile to consider when enhancing existing recommender algorithms. This research is beneficial for intermediate and expert engineers, but beginners may have trouble understanding the complexity. It is necessary for all levels of engineers to be well versed in mathematical concepts.
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