This paper investigates the problem of matrix completion from corrupted data, when a low-rank missing mechanism is considered. The better recovery of missing mechanism often helps completing the unobserved entries of the high-dimensional target matrix. Instead of the widely used uniform risk function, we weight the observations by inverse probabilities of observation, which are estimated through a specifically designed high-dimensional estimation procedure. Asymptotic convergence rates of the proposed estimators for both the observation probabilities and the target matrix are studied. The empirical performance of the proposed methodology is illustrated via both numerical experiments and a real data application.

This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment analysis and what we invent as "critical aspect" analysis altogether in one framework. TSPRA extends sentiment approaches by integrating the key concept "user preference" in collaborative filtering (CF) models into consideration, while it is distinct from current CF models by decoupling "user preference" and "sentiment" as independent factors. Our experiments conducted on 22 Amazon datasets show overwhelming better performance in rating predication against a state-of-art model FLAME (2015) in terms of error, Pearson's Correlation and number of inverted pairs. For sentiment analysis, we compare the derived word sentiments against a public sentiment resource SenticNet3 and our sentiment estimations clearly make more sense in the context of online reviews. Last, as a result of the de-correlation of "user preference" from "sentiment", TSPRA is able to evaluate a new concept "critical aspects", defined as the product aspects seriously concerned by users but negatively commented in reviews. Improvement to such "critical aspects" could be most effective to enhance user experience.

We propose Top-N-Rank, a novel family of list-wise Learning-to-Rank models for reliably recommending the N top-ranked items. The proposed models optimize a variant of the widely used discounted cumulative gain (DCG) objective function which differs from DCG in two important aspects: (i) It limits the evaluation of DCG only on the top N items in the ranked lists, thereby eliminating the impact of low-ranked items on the learned ranking function; and (ii) it incorporates weights that allow the model to leverage multiple types of implicit feedback with differing levels of reliability or trustworthiness. Because the resulting objective function is non-smooth and hence challenging to optimize, we consider two smooth approximations of the objective function, using the traditional sigmoid function and the rectified linear unit (ReLU). We propose a family of learning-to-rank algorithms (Top-N-Rank) that work with any smooth objective function. Then, a more efficient variant, Top-N-Rank.ReLU, is introduced, which effectively exploits the properties of ReLU function to reduce the computational complexity of Top-N-Rank from quadratic to linear in the average number of items rated by users. The results of our experiments using two widely used benchmarks, namely, the MovieLens data set and the Amazon Video Games data set demonstrate that: (i) The `top-N truncation' of the objective function substantially improves the ranking quality of the top N recommendations; (ii) using the ReLU for smoothing the objective function yields significant improvement in both ranking quality as well as runtime as compared to using the sigmoid; and (iii) Top-N-Rank.ReLU substantially outperforms the well-performing list-wise ranking methods in terms of ranking quality.

Google has predicted flight delays for a while, but only if you've searched for a flight yourself. Wouldn't it be better if it warned you before you packed your bags? In addition to bringing delay predictions to Assistant, Google is rolling out proactive warnings over the next few weeks. If your post-holidays return trip is likely to start late, Assistant will both let you know and provide a reason if one is available. You'll know to grab an extra book or TV episode for that longer wait at your gate.

Recommendation systems are widely used by different user service providers specially those who have interactions with the large community of users. This paper introduces a recommender system based on community detection. The recommendation is provided using the local and global similarities between users. The local information is obtained from communities, and the global ones are based on the ratings. Here, a new fuzzy community detection using the personalized PageRank metaphor is introduced. The fuzzy membership values of the users to the communities are utilized to define a similarity measure. The method is evaluated by using two well-known datasets: MovieLens and FilmTrust. The results show that our method outperforms recent recommender systems.

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a dataset consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms, and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.

One of the most powerful features of Amazon's Echo smart speakers is called Routines. Instead of just letting you do one thing at a time, Routines can tie multiple actions to a single voice command. They can even automate tasks without needing you to speak at all. Still, setting up Alexa Routines--and getting the most out of them--takes a bit of work and creativity. Here's everything you need to know about making your own: To begin creating Routines, open the Alexa app for Android or iOS, tap the menu button in the top-left corner, and select Routines.

CannaBot is paving the way for patients to easily get connected to reliable MMJ info and businesses using the power of AI via Google Home, Amazon Alexa, Skype more.

As we feel our way through the haze that is a combination of corporate party and holiday-season prep, Monday kicks off with stories on Alexa's new security talents, a car coming in 2021 that we already drove, and expect Year In Review reports to start hitting Engadget later this week. Amazon's framework can arm your system with just your voice. Amazon has upgraded its voice assistant to work with security systems. You can arm or disarm them, specify certain modes (home, away and night) and simply check in. The functionality is available now in the US, with companies like Abode, ADT, Honeywell, Ring and Scout Alarm already using it.

It's difficult to think of 2018 as a year with anything worth celebrating. But despite all the bad news the year dealt us, there were successes -- if you know where to look. In all corners of tech, we saw wins big and small. There were advances in obvious categories like laptops, smartphones and the connected home, but we also looked outside the mainstream for some of the more surprising gems. Think mini synthesizers for music nerds, retro emulators for nostalgic gamers and e-readers for modern book snobs. Humanity also collectively triumphed, as our space exploration programs broke new frontiers this year and we began to confront the increasingly real question: Should we all just move to Mars? We're just two weeks away from what is hopefully a much better 12 months, and the Engadget team took some time to commemorate our favorite gadgets and trends in tech.