From Hugh Heffner to Donald Trump, many male celebrities are known for their tendency to date younger women. Now, a new study has revealed that as many as one in five British men choose to date women at least five years younger. Researchers trawled through 120,000 dating profiles to understand whether men really do live up to the stereotype of preferring younger women. Their findings indicate that the stereotype is very much true, with men citing good looks and health as the main reasons they prefer ladies their junior. Many male celebrities are known for their tendency to date younger women.

Current state-of-the-art large-scale conversational AI or intelligent digital assistant systems in industry comprises a set of components such as Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). For some of these systems that leverage a shared NLU ontology (e.g., a centralized intent/slot schema), there exists a separate skill routing component to correctly route a request to an appropriate skill, which is either a first-party or third-party application that actually executes on a user request. The skill routing component is needed as there are thousands of skills that can either subscribe to the same intent and/or subscribe to an intent under specific contextual conditions (e.g., device has a screen). Ensuring model robustness or resilience in the skill routing component is an important problem since skills may dynamically change their subscription in the ontology after the skill routing model has been deployed to production. We show how different modeling design choices impact the model robustness in the context of skill routing on a state-of-the-art commercial conversational AI system, specifically on the choices around data augmentation, model architecture, and optimization method. We show that applying data augmentation can be a very effective and practical way to drastically improve model robustness.

Recommending relevant items to users is a crucial task on online communities such as Reddit and Twitter. For recommendation system, representation learning presents a powerful technique that learns embeddings to represent user behaviors and capture item properties. However, learning embeddings on online communities is a challenging task because the user interest keep evolving. This evolution can be captured from 1) interaction between user and item, 2) influence from other users in the community. The existing dynamic embedding models only consider either of the factors to update user embeddings. However, at a given time, user interest evolves due to a combination of the two factors. To this end, we propose Influence-aware and Attention-based Co-evolutionary Network (IACN). Essentially, IACN consists of two key components: interaction modeling and influence modeling layer. The interaction modeling layer is responsible for updating the embedding of a user and an item when the user interacts with the item. The influence modeling layer captures the temporal excitation caused by interactions of other users. To integrate the signals obtained from the two layers, we design a novel fusion layer that effectively combines interaction-based and influence-based embeddings to predict final user embedding. Our model outperforms the existing state-of-the-art models from various domains.

Until a few years ago Artificial Intelligence seemed like a thing from sci-fi movies. The whole concept seemed like fiction or a far fetched dream fed by wishful thinking. Then came personal assistants like Siri, Google Assistant, Bixby, Alexa and Cortana, which made the people realise that they could have something like a Jarvis in their homes as well. However, these are just known as weak AIs. Strong AIs are theoretically able to work with human cognitive abilities.

As AIs progress, the limits between robots and humans are narrowing. AI challenges us in countless areas and is surpassing our ability to complete countless tasks. And today, companies want us to talk to them via AI–their so-called vocal assistants. As if talking to a robot has become normal! Recent years have seen an explosion in so-called conversational AI. The problem is that some current systems are still unstable and don't exactly spark the desire for conversation.

Artificial intelligence – AI – was a mere computational theory back in the 1950s when Alan Turing designed the first Turing Test to measure a machine's intelligence. Today, AI inhabits consumer electronics in the form of Siri, Cortana, Alexa and Google Assistant – it lives behind our internet browsers, within the relative confines of wireless networks and circuit boards. We interact with AI all the time – Google's auto-suggest function, customer service bots and YouTube's search algorithm are all examples of AI. In just half a century, AI's role in society has become firmly established. Developments in software programming and IT have facilitated important innovations in AI.

Tour itinerary planning and recommendation are challenging tasks for tourists in unfamiliar countries. Many tour recommenders only consider broad POI categories and do not align well with users' preferences and other locational constraints. We propose an algorithm to recommend personalized tours using POI-embedding methods, which provides a finer representation of POI types. Our recommendation algorithm will generate a sequence of POIs that optimizes time and locational constraints, as well as user's preferences based on past trajectories from similar tourists. Our tour recommendation algorithm is modelled as a word embedding model in natural language processing, coupled with an iterative algorithm for generating itineraries that satisfies time constraints. Using a Flickr dataset of 4 cities, preliminary experimental results show that our algorithm is able to recommend a relevant and accurate itinerary, based on measures of recall, precision and F1-scores.

Since the recent study (Krichene and Rendle 2020) done by Krichene and Rendle on the sampling-based top-k evaluation metric for recommendation, there has been a lot of debates on the validity of using sampling to evaluate recommendation algorithms. Though their work and the recent work (Li et al.2020) have proposed some basic approaches for mapping the sampling-based metrics to their global counterparts which rank the entire set of items, there is still a lack of understanding and consensus on how sampling should be used for recommendation evaluation. The proposed approaches either are rather uninformative (linking sampling to metric evaluation) or can only work on simple metrics, such as Recall/Precision (Krichene and Rendle 2020; Li et al. 2020). In this paper, we introduce a new research problem on learning the empirical rank distribution, and a new approach based on the estimated rank distribution, to estimate the top-k metrics. Since this question is closely related to the underlying mechanism of sampling for recommendation, tackling it can help better understand the power of sampling and can help resolve the questions of if and how should we use sampling for evaluating recommendation. We introduce two approaches based on MLE (MaximalLikelihood Estimation) and its weighted variants, and ME(Maximal Entropy) principals to recover the empirical rank distribution, and then utilize them for metrics estimation. The experimental results show the advantages of using the new approaches for evaluating recommendation algorithms based on top-k metrics.

Google Sheets, Docs, and Slides have traditionally been the cornerstone of Google Workspace Essentials. Now you can add Chat, Jamboard, and Calendar, too. Google made a number of additions and changes to its Google Workspace on the eve of Microsoft Ignite this week. Workspace, formerly known as G Suite, is being beefed up with the addition of a number of notable features, including the ability to use a Nest Hub Max as a second screen for a Google Meet meeting, even in the home. Google is adding its Google Assistant to Workspace, and it's generally available to respond to questions like "When's my next meeting?"

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain. Although CDR has been extensively studied in recent years, there is a lack of a systematic review of the existing CDR approaches. To fill this gap, in this paper, we provide a comprehensive review of existing CDR approaches, including challenges, research progress, and future directions. Specifically, we first summarize existing CDR approaches into four types, including single-target CDR, multi-domain recommendation, dual-target CDR, and multi-target CDR. We then present the definitions and challenges of these CDR approaches. Next, we propose a full-view categorization and new taxonomies on these approaches and report their research progress in detail. In the end, we share several promising research directions in CDR.