We demonstrate how enforcing group-fairness in every recommendation slate separately does not necessarily promote equity in second order variables of interest like network size. Connection recommendation is at the heart of user experience in many online social networks. Given a prompt such as'People you may know', connection recommender systems suggest a list of users, and the recipient of the recommendation decides which of the users to connect with. In some instances, connection recommendations can account for more than 50% of the social network graph [1]. Depending on the platform, being connected to the right people is tied to important advantages such as job opportunities or increased visibility. While this makes it imperative to treat users fairly, it is far from obvious how fairness can be enforced or what it even means to have a'fair' system in this scenario.

While dating apps were once seen as taboo, they're now one of the main ways that singletons find love around the world. And if you're single this bank holiday, there's good news, as experts from dating app Badoo have revealed the best time to go online to bag yourself a date. According to their research, Saturday from 8-10pm is the peak time to swipe this weekend. Remy Le Fevre, Senior Director of Global Marketing at Badoo said: 'Easter is a great time for dating; the days are finally getting brighter and longer, and moods are lifting. 'Not to mention that thanks to two bank holidays - Friday and Monday - we actually have a bit of extra time on our hands, too.'

Recent advancements in Natural Language Processing (NLP) have led to the development of NLP-based recommender systems that have shown superior performance. However, current models commonly treat items as mere IDs and adopt discriminative modeling, resulting in limitations of (1) fully leveraging the content information of items and the language modeling capabilities of NLP models; (2) interpreting user interests to improve relevance and diversity; and (3) adapting practical circumstances such as growing item inventories. To address these limitations, we present GPT4Rec, a novel and flexible generative framework inspired by search engines. It first generates hypothetical "search queries" given item titles in a user's history, and then retrieves items for recommendation by searching these queries. The framework overcomes previous limitations by learning both user and item embeddings in the language space. To well-capture user interests with different aspects and granularity for improving relevance and diversity, we propose a multi-query generation technique with beam search. The generated queries naturally serve as interpretable representations of user interests and can be searched to recommend cold-start items. With GPT-2 language model and BM25 search engine, our framework outperforms state-of-the-art methods by $75.7\%$ and $22.2\%$ in Recall@K on two public datasets. Experiments further revealed that multi-query generation with beam search improves both the diversity of retrieved items and the coverage of a user's multi-interests. The adaptiveness and interpretability of generated queries are discussed with qualitative case studies.

Former Wisconsin Gov. Scott Walker, R., joined Americas Newsroom to discuss what is at stake with the swing states pivotal election. A Wisconsin woman is using online dating applications to reach young people nationwide and help raise voter turnout during elections, according to a local report. Kristi Johnston is part of Next Gen. America, an organization that works toward increasing voter turnout among young Americans, WKOW-TV reported. Johnston and the group do not push for any specific political party or candidate and instead raise awareness and remind people to get out and vote.

In this paper we present a generative latent variable model for rating-based collaborative (cid:12)ltering called the User Rating Pro(cid:12)le model (URP). The generative process which underlies URP is de- signed to produce complete user rating pro(cid:12)les, an assignment of one rating to each item for each user. Our model represents each user as a mixture of user attitudes, and the mixing proportions are distributed according to a Dirichlet random variable. The rating for each item is generated by selecting a user attitude for the item, and then selecting a rating according to the preference pattern associ- ated with that attitude. URP is related to several models including a multinomial mixture model, the aspect model [7], and LDA [1], but has clear advantages over each.

We prove generalization error bounds for predicting entries in a partially observed matrix by fitting the observed entries with a low-rank matrix. In justifying the analysis approach we take to obtain the bounds, we present an example of a class of functions of finite pseudodimension such that the sums of functions from this class have unbounded pseudodimension. "Collaborative filtering" refers to the general task of providing users with information on what items they might like, or dislike, based on their preferences so far and how they relate to the preferences of other users. This approach contrasts with a more traditional feature- based approach where predictions are made based on features of the items. For feature-based approaches, we are accustomed to studying prediction methods in terms of probabilistic post-hoc generalization error bounds. Such results provide us a (proba- bilistic) bound on the performance of our predictor on future examples, in terms of its performance on the training data.

We present a novel method for learning with Gaussian process regres- sion in a hierarchical Bayesian framework. In a first step, kernel matri- ces on a fixed set of input points are learned from data using a simple and efficient EM algorithm. This step is nonparametric, in that it does not require a parametric form of covariance function. In a second step, kernel functions are fitted to approximate the learned covariance matrix using a generalized Nystrom method, which results in a complex, data driven kernel. We evaluate our approach as a recommendation engine for art images, where the proposed hierarchical Bayesian method leads to excellent prediction performance.

We present a general model-independent approach to the analysis of data in cases when these data do not appear in the form of co-occurrence of two variables X, Y, but rather as a sample of values of an unknown (stochastic) function Z (X, Y). For example, in gene expression data, the expression level Z is a function of gene X and condition Y; or in movie ratings data the rating Z is a function of viewer X and movie Y . The approach represents a consistent extension of the Information Bottleneck method that has previously relied on the availability of co-occurrence statistics. By altering the relevance variable we eliminate the need in the sample of joint distribution of all input variables. This new formulation also enables simple MDL-like model complexity control and prediction of missing values of Z .

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of Web2.0" recommender systems, allowing users to generate playlists based on use-dependent terms such as "chill" or "jogging" that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of boosted classifiers, we map audio features onto social tags collected from the Web. The resulting automatic tags (or "autotags") furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the ''cold-start problem'' common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system."

We show that matrix completion with trace-norm regularization can be significantly hurt when entries of the matrix are sampled non-uniformly, but that a properly weighted version of the trace-norm regularizer works well with non-uniform sampling. We show that the weighted trace-norm regularization indeed yields significant gains on the highly non-uniformly sampled Netflix dataset.