Designing an e-commerce recommender system that serves hundreds of millions of active users is a daunting challenge. To our best knowledge, the complex brain activity mechanism behind human shopping activities is never considered in existing recommender systems. From a human vision perspective, we found two key factors that affect users’ behaviors: items’ attractiveness and their matching degrees with users’ interests. This paper proposes Telepath, a vision-based bionic recommender system model, which simulates human brain activities in decision making of shopping, thus understanding users from such perspective. The core of Telepath is a complex deep neural network with multiple subnetworks. In practice, the Telepath model has been launched to JD’s recommender system and advertising system and outperformed the former state-of-the-art method. For one of the major item recommendation blocks on the JD app, click-through rate (CTR), gross merchandise value (GMV) and orders have been increased 1.59%, 8.16% and 8.71% respectively by Telepath. For several major ad publishers of JD demand-side platform, CTR, GMV and return on investment have been increased 6.58%, 61.72% and 65.57% respectively by the first launch of Telepath, and further increased 2.95%, 41.75% and 41.37% respectively by the second launch.

Personalized tag recommender systems suggest a list of tags to a user when he or she wants to annotate an item. They utilize users’ preferences and the features of items. Tensorfactorization techniques have been widely used in tag recommendation. Given the user-item pair, although the classic PITF (Pairwise Interaction Tensor Factorization) explicitly models the pairwise interactions among users, items and tags, it overlooks users’ short-term interests and suffers from data sparsity. On the other hand, given the user-item-time triple, time-aware approaches like BLL (Base-Level Learning) utilize the time effect to capture the temporal dynamics and the most popular tags on items to handle cold start situation of new users. However, it works only on individual level and the target resource level, which cannot find users’ potential interests. In this paper, we propose an unified tag recommendation approach by considering both time awareness and personalization aspects, which extends PITF by adding weightsto user-tag interaction and item-tag interaction respectively. Compared to PITF, our proposed model can depict temporal factor by temporal weights and relieve data sparsity problem by referencing the most popular tags on items. Further, our model brings collaborative filtering (CF) to time-aware models, which can mine information from global data and help improving the ability of recommending new tags. Different from the power-form functions used in the existing time aware recommendation models, we use the Hawkes process with the exponential intensity function to improve the model’s efficiency. The experimental results show that our proposed model outperforms the state of the art tag recommendation methods in accuracy and has better ability to recommend new tags.

Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been widely used in recommender systems. The literature has reported that matrix factorization methods often produce superior accuracy of rating prediction in recommender systems. However, existing matrix factorization methods rarely consider confidence of the rating prediction and thus cannot support advanced recommendation tasks. In this paper, we propose a Confidence-aware Matrix Factorization (CMF) framework to simultaneously optimize the accuracy of rating prediction and measure the prediction confidence in the model. Specifically, we introduce variance parameters for both users and items in the matrix factorization process. Then, prediction interval can be computed to measure confidence for each predicted rating. These confidence quantities can be used to enhance the quality of recommendation results based on Confidence-aware Ranking (CR). We also develop two effective implementations of our framework to compute the confidence-aware matrix factorization for large-scale data. Finally, extensive experiments on three real-world datasets demonstrate the effectiveness of our framework from multiple perspectives.

Social recommendation, which aims to exploit social information to improve the quality of a recommender system, has attracted an increasing amount of attention in recent years. A large portion of existing social recommendation models are based on the tractable assumption that users consider the same factors to make decisions in both recommender systems and social networks. However, this assumption is not in concert with real-world situations, since users usually show different preferences in different scenarios. In this paper, we investigate how to exploit the differences between user preference in recommender systems and that in social networks, with the aim to further improve the social recommendation. In particular, we assume that the user preferences in different scenarios are results of different linear combinations from a more underlying user preference space. Based on this assumption, we propose a novel social recommendation framework, called social recommendation with an essential preferences space (SREPS), which simultaneously models the structural information in the social network, the rating and the consumption information in the recommender system under the capture of essential preference space. Experimental results on four real-world datasets demonstrate the superiority of the proposed SREPS model compared with seven state-of-the-art social recommendation methods.

Points of interest (POI) recommendation has been drawn much attention recently due to the increasing popularity of location-based networks, e.g., Foursquare and Yelp. Among the existing approaches to POI recommendation, Matrix Factorization (MF) based techniques have proven to be effective. However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices. (2) Privacy issues: the users' preferences are at risk of leaking to malicious attackers via the centralized learner. To solve these, we present a Decentralized MF (DMF) framework for POI recommendation. Specifically, instead of maintaining all the low rank matrices and sensitive rating data for training, we propose a random walk based decentralized training technique to train MF models on each user's end, e.g., cell phone and Pad. By doing so, the ratings of each user are still kept on one's own hand, and moreover, decentralized learning can be taken as distributed learning with multi-learners (users), and thus alleviates the computation and storage issue. Experimental results on two real-world datasets demonstrate that, comparing with the classic and state-of-the-art latent factor models, DMF significantly improvements the recommendation performance in terms of precision and recall.

The popularity of live streaming has led to the explosive growth in new video contents and social communities on emerging platforms such as Facebook Live and Twitch. Viewers on these platforms are able to follow multiple streams of live events simultaneously, while engaging discussions with friends. However, existing approaches for selecting live streaming channels still focus on satisfying individual preferences of users, without considering the need to accommodate real-time social interactions among viewers and to diversify the content of streams. In this paper, therefore, we formulate a new Social-aware Diverse and Preferred Live Streaming Channel Query (SDSQ) that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. We perform a user study on Twitch with 432 participants to validate the need of SDSQ and the usefulness of SDSSel. We also conduct large-scale experiments on real datasets to demonstrate the superiority of the proposed algorithm over several baselines in terms of solution quality and efficiency.

The Google Home ($99 at Walmart) is a worthy alternative to the Amazon Echo; it can answer questions you'd usually type into Google, set a timer for the kitchen, serve as a centralized smart home controller, and of course, play music. It also works with lots of great smart devices, but that's not the only trick up the Home's sleeve. Did you know you can program it to do nearly anything you can think of? With the help of free services like IFTTT (If This Then That), the smart speaker can perform common tasks like sending messages, posting to social media, or even adding ingredients to a shared shopping list in a cloud service like Google Drive. IFTTT is a free service that lets you automate tasks between Internet-connected devices and cloud or social media accounts. All the recipes are for both Android and iOS unless otherwise specified.

In all their infinite wisdom, the Romans decided to celebrate Valentine's Day in the dead of winter. Even if that wasn't their original intention, the holiday is a good excuse to stave off the cold, gray doldrums with a new gaming computer. Or maybe a smart home assistant to help you check the forecast? With the help of our friends at TechBargains, we've pulled together a few deals to indulge yourself or someone you love. Dell is currently still offering 10% with the code TENOFF.

Microsoft updated its Cortana app and it now features native iPad support. The interface makes use of the bigger iPad screen, but otherwise it's largely similar to the iPhone version. The update, spotted by MacRumors, also notes that the app now opens 20 percent faster. Enjoy an exclusive layout and interface for your iPad," says the update release. "Cortana now launches 20 percent faster!

The 2018 Winter Olympics games, taking place in PyeongChang, South Korea, kicks off in February. A record amount of condoms -- nearly 40 per person -- were distributed to athletes participating in the 2018 Winter Olympics this month, a handout officials say will allow tense competitors to let out steam during the high-pressure games in South Korea. But the move is raising questions about allowing the Olympic Village to turn into a freewheeling sex romp at a time when the #MeToo movement has taken the world by storm and sensitivity about sexual misconduct is at an all-time high. "The Olympics is a very sexually charged time," Julie Spira, an online and mobile dating expert, told CBS News. "Young athletes and attendees have been using dating apps heavily in prior Olympics, but now the popularity of an app like Tinder continues to grow and grow." Some 1.2 million people, including athletes, were expected to gather in PyeongChang, South Korea for the 2018 Winter Olympics.