Recommender systems are widely developed to learn user preferences from their past history and make predictions on the unseen items a user may like. User preferences in the form of absolute preferences, such as user ratings or clicks are commonly used to model a user’s interest and generate recommendations. However, rating items is not the most natural mechanism that users use for making decisions in daily life. For instance, we do not rate t-shirts when we want to buy one. It is more likely that we will compare them one to one, and purchase the preferred one. In this work, we focus on relative feedback, which generates pairwise preferences as an alternative way to model user preferences and compute recommendations. In our scenario, each user is shown a set of item pairs and asked to compare them to indicate which item in the pair is more preferred. We propose a recommendation algorithm to predict a user’s relative preference for a given pairs of items and compute a personalised ranking of items. We demonstrate the effectiveness of our proposed algorithm in comparison with state-of-the-art relative feedback based recommendation approaches. Our experimental results reveal that the proposed algorithm is able to outperform the baseline algorithms on popular ranking-oriented evaluation metrics.

The proliferation of personalized recommendation technologies has raised concerns about discrepancies in their recommendation performance across different genders, age groups, and racial or ethnic populations. This varying degree of performance could impact users’ trust in the system and may pose legal and ethical issues in domains where fairness and equity are critical concerns, like job recommendation. In this paper, we investigate several potential factors that could be associated with discriminatory performance of a recommendation algorithm for women versus men. We specifically study several characteristics of user profiles and analyze their possible associations with disparate behavior of the system towards different genders. These characteristics include the anomaly in rating behavior, the entropy of users’ profiles, and the users’ profile size. Our experimental results on a public dataset using four recommendation algorithms show that, based on all the three mentioned factors, women get less accurate recommendations than men indicating an unfair nature of recommendation algorithms across genders.

In this work, we introduce a context-aware hybrid artist recommender system (CAARS) that uses Twitter users’ tweet-time patterns as context and users’ bias about gender and types of musicians to recommend artists. Our model offers a novel approach to improve a personalized music recommender system (MRS) as it extracts implicit information from the users’ past tweet-behavior and combines that with related content. The proposed model performs significantly better than collaborative and hybrid recommender systems and encourages further exploration.

Visualization, while an effective tool for identifying patterns and insights, requires expert knowledge due to challenges faced when translating user queries to visual encodings. Research has shown that using a natural language interface (NLI) is effective for these challenges because the user can simply talk to a computer capable of producing the graphs directly. In this paper, we discuss our intelligent assistant which processes speech and hand pointing gestures while also dealing with any number of visualizations on a large screen display. Evaluation of the system shows that it is capable of quickly producing visualizations. It also particularly effective at responding to less ambiguous queries, while in certain cases can handle ambiguous or complex queries.

Active Learning (AL) when applied to Recommender Systems (RSs) aims at proactively acquiring additional ratings data from the RS users in order to improve subsequent recommendation quality. AL strategies are typically evaluated offline first, but the classic AL offline evaluation methodology does not take into account the bias problem in RS offline evaluation. This problem affects the evaluation of an RS, as brought to light by recent literature. But, we argue, it also affects the evaluation of AL strategies as well. For this reason, in this paper, we propose a new AL offline evaluation methodology for RSs which mitigates the bias and thus facilitates a truer picture of the performances of the AL strategies under evaluation. We illustrate our proposed methodology on two datasets and with three simple and well-known AL strategies from the literature. Our experimental results differ from those reported previously in the literature, which shows the importance of our approach to AL evaluation.

Google just relaunched its Nest Aware home security plans, complete with a simplified pricing structure and a host of new features. Chief among them: the ability for Google smart displays and speakers to alert you if they hear something suspicious. Users who pony up $6 a month ($60 if paid annually) for the standard Nest Aware plan will be able to set their Google Home and Nest devices to detect the sound of breaking glass and smoke alarms. The subscription covers all the Nest devices in your home and includes 30 days of cloud storage for event recordings made by Nest security cameras ("events" are recordings triggered by motion or sound). A $12-per-month/$120-per-year plan gives you 60 days of event storage in the cloud, plus 10 days of 24/7 video recording.

The Guardian is closing its online dating service Guardian Soulmates because it is "no longer viable". The service, which has about 35,000 free members and paid subscribers, will close at the end of June, it said. The "online dating landscape has changed dramatically" since it launched in 2004, it added - making it a "very little fish in a very big pool". The 15 years since its launch has seen the growth of global dating apps such as Tinder, Hinge and Bumble. Guardian Soulmates said on its website: "There are so many dating apps now, so many ways to meet people, which are often free and very quick.

Recommendation systems have lately been popularized globally, with primary use cases in online interaction systems, with significant focus on e-commerce platforms. We have developed a machine learning-based recommendation platform, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our platform supports multiple types of interaction data with multiple modalities of metadata natively. This is achieved through multi-modal fusion of various data representations. We deployed the platform into multiple e-commerce stores of different kinds, e.g. food and beverages, shoes, fashion items, telecom operators. Here, we present our system, its flexibility and performance. We also show benchmark results on open datasets, that significantly outperform state-of-the-art prior work.

We study the combinatorial sleeping multi-armed semi-bandit problem with long-term fairness constraints~(CSMAB-F). To address the problem, we adopt Thompson Sampling~(TS) to maximize the total rewards and use virtual queue techniques to handle the fairness constraints, and design an algorithm called \emph{TS with beta priors and Bernoulli likelihoods for CSMAB-F~(TSCSF-B)}. Further, we prove TSCSF-B can satisfy the fairness constraints, and the time-averaged regret is upper bounded by $\frac{N}{2\eta} + O\left(\frac{\sqrt{mNT\ln T}}{T}\right)$, where $N$ is the total number of arms, $m$ is the maximum number of arms that can be pulled simultaneously in each round~(the cardinality constraint) and $\eta$ is the parameter trading off fairness for rewards. By relaxing the fairness constraints (i.e., let $\eta \rightarrow \infty$), the bound boils down to the first problem-independent bound of TS algorithms for combinatorial sleeping multi-armed semi-bandit problems. Finally, we perform numerical experiments and use a high-rating movie recommendation application to show the effectiveness and efficiency of the proposed algorithm.

If you ask someone what comes to their mind after hearing the words'artificial intelligence', they would probably answer something like'robots, self-driving cars' etc. However, AI goes way beyond that. Furthermore, you have probably already encountered it in your life but didn't even know it. The perfect example is Siri. AI is more than just robots.