Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challenging, since the addition of context may increases both the dimensionality and sparsity of the model. Recent research has shown that modeling contextual information as a latent vector may address the sparsity and dimensionality challenges. We suggest a new latent modeling of sequential context by generating sequences of contextual information and reducing their contextual space to a compressed latent space.We train a long short-term memory (LSTM) encoder-decoder network on sequences of contextual information and extract sequential latent context from the hidden layer of the network in order to represent a compressed representation of sequential data. We propose new context-aware recommendation models that extend the neural collaborative filtering approach and learn nonlinear interactions between latent features of users, items, and contexts which take into account the sequential latent context representation as part of the recommendation process. We deployed our approach using two context-aware datasets with different context dimensions. Empirical analysis of our results validates that our proposed sequential latent context-aware model (SLCM), surpasses state of the art CARS models.

Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users. As a result, these measures cannot fully utilize the rating information and are not suitable for real world sparse data. To solve these issues, we propose a novel user distance measure named Preference Mover's Distance (PMD) which makes full use of all ratings made by each user. Our proposed PMD can properly measure the distance between a pair of users even if they have no co-rated items. We show that this measure can be cast as an instance of the Earth Mover's Distance, a well-studied transportation problem for which several highly efficient solvers have been developed. Experimental results show that PMD can help achieve superior recommendation accuracy than state-of-the-art methods, especially when training data is very sparse.

Traditional recommendation systems produce static rather than interactive recommendations invariant to a user's specific requests, clarifications, or current mood, and can suffer from the cold-start problem if their tastes are unknown. These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone's preferences, react to their requests, and recommend more appropriate items. In this work, we collect a goal-driven recommendation dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260 conversation turns between pairs of human workers recommending movies to each other. The task is specifically designed as a cooperative game between two players working towards a quantifiable common goal. We leverage the dataset to develop an end-to-end dialogue system that can simultaneously converse and recommend. Models are first trained to imitate the behavior of human players without considering the task goal itself (supervised training). We then finetune our models on simulated bot-bot conversations between two paired pre-trained models (bot-play), in order to achieve the dialogue goal. Our experiments show that models finetuned with bot-play learn improved dialogue strategies, reach the dialogue goal more often when paired with a human, and are rated as more consistent by humans compared to models trained without bot-play. The dataset and code are publicly available through the ParlAI framework.

The Internet of Things, or IoT, is a growing infrastructure of internet-enabled objects ranging from vacuums to light bulbs, all aimed at increasing control, automation and even data collection. IoT can be a huge benefit for a business office when used appropriately. Many offices are already used to internet-connected printers, but a new generation of smart alternatives is hitting the market that allow more than network printing. They monitor their paper and ink and can warn a support person when they're getting low. They can also connect to inventory systems to know how much spare ink or paper they have on hand and can even make orders for more without human involvement.

Increasingly, voice assistants from vendors such as Amazon, Apple, Google, Microsoft, and others are starting to find their way into myriad of devices, products, and tools used on a daily basis. While once we might have only interacted with conversational systems on our phones, dedicated desktop appliances, or desktop computers, we can now find conversational interfaces on a wide range of appliances and products from televisions to cars and even toaster ovens. Soon, any device we can interact with will have an audio conversational interface instead of buttons or screens to type or click. The dawn of the conversational computing age is here. However, are these devices intelligent enough to handle the wide range of queries that humans are posing?

As artificial intelligence (AI) and machine learning become ubiquitous, we will soon be hard-pressed to find any industry not capitalizing on the benefits they can provide. Telecommunications is one of the fastest-growing industries as well as one that uses artificial intelligence and machine learning in many aspects of their business from enhancing the customer experience to predictive maintenance to improving network reliability. The largest telecoms in the world rely on artificial intelligence and machine learning in a number of ways. Here are the most common applications. Nearly every telecom uses artificial intelligence and machine learning to improve its customer service primarily by using virtual assistants and chatbots.

The Apple iPad Pro is seen above. Workers carry them around offices. Entire stores have traded their cash registers for iPads. The flagship Apple tablet isn't just a novelty item for watching movies on the sofa; it has become a major workhorse, transforming our domestic and professional lives. Add the power of Siri, and your iPad becomes a full-on virtual assistant, especially with a few little-known features.

It was my favorite out of the ones our team put on and we are talking about doing a BI event in the next few months. If you interested in presenting on any BI related topic or your company would like to sponsor a full day BI training event in LA, please let me know.

Both datasets are being shared by Google AI researchers to supply the training material necessary to model natural language systems that achieve human-level performance. Google researchers call CCPE a new way to collect voice data. It includes 500 dialogues with people about their movie preferences -- 10,000 in total, across 12,000 utterances. Movie preferences were chosen as a topic because of the value of metadata such as the names of actors and directors. "We do not restrict the workers to detailed scripts or to a small knowledge base and hence we observe that our dataset contains more realistic and diverse conversations in comparison to existing datasets," a paper published covering CCPE reads.

Implicit feedback plays a critical role to construct recommender systems because this type of feedback is prevalent in the real-world. However, effectively utilizing implicit feedback is challenging because of positive-unlabeled or missing-not-at-random problems. To tackle these challenges, in this paper, we first show that existing approaches are biased toward the true metric. Subsequently, we provide a theoretically principled approach to handle the problems inspired by estimation methods in causal inference. In particular, we propose an unbiased estimator for the true metric of interest solving the above problems simultaneously. Experiments on two standard real-world datasets demonstrate the superiority of the proposed approach against state-of-the-art recommendation algorithms.