Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations

Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks of such systems have previously been studied mostly in the context of recovery of personal information in the form of usage records from the training data. However, the user representations themselves may be used together with external data to recover private user information such as gender and age. In this paper we show that user vectors calculated by a common recommender system can be exploited in this way. We propose the privacy-adversarial framework to eliminate such leakage of private information, and study the trade-off between recommender performance and leakage both theoretically and empirically using a benchmark dataset. An advantage of the proposed method is that it also helps guarantee fairness of results, since all implicit knowledge of a set of attributes is scrubbed from the representations used by the model, and thus can't enter into the decision making. We discuss further applications of this method towards the generation of deeper and more insightful recommendations.Comment: International Conference on Pattern Recognition and Method

Semi-supervised Adversarial Learning for Complementary Item Recommendation

Complementary item recommendations are a ubiquitous feature of modern e-commerce sites. Such recommendations are highly effective when they are based on collaborative signals like co-purchase statistics. In certain online marketplaces, however, e.g., on online auction sites, constantly new items are added to the catalog. In such cases, complementary item recommendations are often based on item side-information due to a lack of interaction data. In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i.e., for items for which no co-purchase statistics yet exist. Given that complementary items typically have to be of a different category than the seed item, we technically maintain a latent space for each item category. Simultaneously, we learn to project distributed item representations into these category spaces to determine suitable recommendations. The main learning process in our architecture utilizes labeled pairs of complementary items. In addition, we adopt ideas from Cycle Generative Adversarial Networks (CycleGAN) to leverage available item information even in case no labeled data exists for a given item and category. Experiments on three e-commerce datasets show that our method is highly effective.Comment: ACM Web Conference 202

Up close, but not too personal: Hypotargeting for recommender systems

Hypotargeting for recommender systems (hyporec) is the idea of controlling the number of unique lists of items that a recommender system can recommend to users during a given time period. The main advantage of hyporec is oversight. If a recommender system offers only a finite number of unique lists, then it becomes feasible for a person without technological knowledge to audit the recommender system. Oversight makes it possible to spot filter bubbles or cases in which users are being bombarded with divisive content. We argue that hyporec is actually not so far from many existing recommender system ideas, and that with further research hyporec systems could be capable of making good tradeoffs between the number of unique lists, rate of list renewal (which controls coverage), and conventional evaluation metrics for user satisfaction.</p

Preface to the joint proceedings of the ComplexRec and ImpactRS workshops at ACM RecSys 2020

Recommender systems are widely used in modern online applications, from e-commerce sites over media streaming services to social networks. In academic research, we however often abstract from the specifics of these applications and rely on simplified assumptions such as the availability of past rating data. Furthermore, we mostly focus on predicting to what extent a user will like a certain item, but do not explicitly consider the long-term effects of recommendations on the users' decision-making processes or the expected impact on orgnizations. The 14th ACM Conference on Recommender Systems hosted two workshops which aim to look beyond our often too simplifying assumptions, the Fourth Workshop on Recommendation in Complex Environments and the Second Workshop on the Impact of Recommender Systems. These proceedings describe the specific goals of the workshops and contain the papers that were presented during the online events

Preface to the joint proceedings of the ComplexRec and ImpactRS workshops at ACM RecSys 2020

Recommender systems are widely used in modern online applications, from e-commerce sites over media streaming services to social networks. In academic research, we however often abstract from the specifics of these applications and rely on simplified assumptions such as the availability of past rating data. Furthermore, we mostly focus on predicting to what extent a user will like a certain item, but do not explicitly consider the long-term effects of recommendations on the users' decision-making processes or the expected impact on orgnizations. The 14th ACM Conference on Recommender Systems hosted two workshops which aim to look beyond our often too simplifying assumptions, the Fourth Workshop on Recommendation in Complex Environments and the Second Workshop on the Impact of Recommender Systems. These proceedings describe the specific goals of the workshops and contain the papers that were presented during the online events