Recommender systems support decisions in various domains ranging from simple items such as books and movies to more complex items such as financial services, telecommunication equipment, and software systems. In this context, recommendations are determined, for example, on the basis of analyzing the preferences of similar users. In contrast to simple items which can be enumerated in an item catalog, complex items have to be represented on the basis of variability models (e.g., feature models) since a complete enumeration of all possible configurations is infeasible and would trigger significant performance issues. In this paper, we give an overview of a potential new line of research which is related to the application of recommender systems and machine learning techniques in feature modeling and configuration. In this context, we give examples of the application of recommender systems and machine learning and discuss future research issues.

In recent years, there has been significant interest in understanding users' online content consumption patterns. But, the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here we propose a model that combines the simplicity of matrix factorization with the flexibility of neural networks to efficiently extract nonlinear patterns from massive text data collections relevant to consumers' online consumption patterns. Our model decomposes a user's content consumption journey into nonlinear user and content factors that are used to model their dynamic interests. This natural decomposition allows us to summarize each user's content consumption journey with a dynamic probabilistic weighting over a set of underlying content attributes. The model is fast to estimate, easy to interpret and can harness external data sources as an empirical prior. These advantages make our method well suited to the challenges posed by modern datasets. We use our model to understand the dynamic news consumption interests of Boston Globe readers over five years. Thorough qualitative studies, including a crowdsourced evaluation, highlight our model's ability to accurately identify nuanced and coherent consumption patterns. These results are supported by our model's superior and robust predictive performance over several competitive baseline methods.

Whether you're looking for love or ways to celebrate your loved one, technology is playing an increasingly important role – especially during a pandemic. After all, many of us are forced to remain socially distant for the time being. Valentine's Day might be celebrated at home this year, as opposed to dining in a restaurant, and florists may sell more bouquets to online customers instead of in-store shoppers. As the expression goes, there's an app for that. Interestingly, even online dating apps aren't just used to find a mate over the internet, but quite literally to date online – until it's safe to meet in person.

Unfortunately, scams trying to steal your heart and money are, too. As Valentine's Day nears, potential scammers are attempting to take advantage, focused on stealing personal information or money. Whether you're looking for love on social networks or dating sites or looking to buy a special gift for your loved one, scammers are lurking to trick you. This season in particular, as many Americans remain homebound due to COVID-19 outbreaks, the number of scams related to romance or Valentine's Day is on the rise. Lynette Owens, global director of internet safety at Trend Micro, said scams related to romance are up 20% over last year, caused by the "double whammy" of people staying online more due to the pandemic and increased isolation.

The listing of Bumble, which also owns Badoo, makes a billionaire of 31-year-old founder Whitney Wolfe Herd.

A sequential recommender system aims to recommend attractive items to users based on behaviour patterns. The predominant sequential recommendation models are based on natural language processing models, such as the gated recurrent unit, that embed items in some defined space and grasp the user's long-term and short-term preferences based on the item embeddings. However, these approaches lack fundamental insight into how such models are related to the user's inherent decision-making process. To provide this insight, we propose a novel recurrent cell, namely FaNC, from Freudian and Newtonian perspectives. FaNC divides the user's state into conscious and unconscious states, and the user's decision process is modelled by Freud's two principles: the pleasure principle and reality principle. To model the pleasure principle, i.e., free-floating user's instinct, we place the user's unconscious state and item embeddings in the same latent space and subject them to Newton's law of gravitation. Moreover, to recommend items to users, we model the reality principle, i.e., balancing the conscious and unconscious states, via a gating function. Based on extensive experiments on various benchmark datasets, this paper provides insight into the characteristics of the proposed model. FaNC initiates a new direction of sequential recommendations at the convergence of psychoanalysis and recommender systems.

Ever heard a catchy song on your smart speaker and wanted to share it right away? You can act on that impulse if you have an Echo. Amazon is rolling out a music sharing feature that lets you share songs with Alexa contacts using Echo devices or the Alexa app. Ask the voice assistant to "share this song with" a contact and they can not only choose to listen, but send a reaction. Thankfully, you don't need to use Amazon Music or even the same streaming service as your recipient.

Recommender systems are important and valuable tools for many personalized services. Collaborative Filtering (CF) algorithms -- among others -- are fundamental algorithms driving the underlying mechanism of personalized recommendation. Many of the traditional CF algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data for matching, including memory-based methods such as user/item-based CF as well as learning-based methods such as matrix factorization and deep learning models. However, advancing from correlative learning to causal learning is an important problem, because causal/counterfactual modeling can help us to think outside of the observational data for user modeling and personalization. In this paper, we propose Causal Collaborative Filtering (CCF) -- a general framework for modeling causality in collaborative filtering and recommendation. We first provide a unified causal view of CF and mathematically show that many of the traditional CF algorithms are actually special cases of CCF under simplified causal graphs. We then propose a conditional intervention approach for $do$-calculus so that we can estimate the causal relations based on observational data. Finally, we further propose a general counterfactual constrained learning framework for estimating the user-item preferences. Experiments are conducted on two types of real-world datasets -- traditional and randomized trial data -- and results show that our framework can improve the recommendation performance of many CF algorithms.

Online-dating company Match Group Inc. has reached an agreement to acquire South Korean social-media company Hyperconnect for $1.73 billion, broadening its services beyond connecting people in their love lives. The cash-and-stock deal, announced Tuesday, marks Match Group's largest acquisition to date. Hyperconnect, based in Seoul, has developed two video apps that focus on helping people interact one-on-one and with new communities. Hyperconnect's first app, Azar, offers live video and audio chat and can instantly translate voice and text for users that speak different languages. Hyperconnect's other app, Hakuna Live, is a social live-streaming app that provides group video and audio broadcasts.

Instagram is taking on TikTok with a new update that will de-rank Reels with its competitor's watermark. The update is being unleashed to an algorithm tasked with recommending Reels with the hopes of users creating unique content on the platform – and not just recycling from other apps. Instagram launched Reels just five days before former President Donald Trump announced plans to ban Chinese-owned TikTok in the US, with the hopes of being the front runner in the video content space. However, six months later and Reels has not gained uptick in usage as Instagram had hoped and TikTok is still around and seems to be thriving with its some 100 million users in just the US alone – which may be why Instagram is not promoting the watermarked clips. Reels offers users shot-form video editing tools that lets users be creative with 30-second video clips with captions and music.