In recommender systems, cold-start issues are situations where no previous events, e.g., ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g., item attributes) and initial user ratings are valuable for seizing users' preferences on a new item. However, previous methods for the item cold-start problem either (1) incorporate content information into collaborative filtering to perform hybrid recommendation, or (2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leveraging both active learning and items' attribute information. Specifically, we design useful user selection criteria based on items' attributes and users' rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users' previous ratings and items' attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods.

Recently, interactive recommender systems are becoming increasingly popular. The insight is that, with the interaction between users and the system, (1) users can actively intervene the recommendation results rather than passively receive them, and (2) the system learns more about users so as to provide better recommendation.

Distance metric learning has been shown to be an effective and efficient method which can lead to significant improvements in classification, clustering, and retrieval. Conventional metric learning methods which apply a global metric to capture the input features and their correlations are unsuitable for heterogeneous data, whose features vary according to different clusters. Thus, a number of local metric learning methods have been proposed to overcome this limitation. Unfortunately, most of them suffer from over-fitting or need the data set to be under a strong assumption, such as Gaussian distribution. To avoid over-fitting caused by learning local metric matrices separately in the previous researches, we propose a novel local metric learning method in which we learn a metric matrix for each pair of instances as linear combinations of basis metric matrices defined on different clusters of the input space without any assumption for data set. We get the parameters of the linear combinations by using the relationship between direct distance and anchor~distance between a pair of data points. Additionally, we design a novel expectation-maximization based optimization method to learn the linear combinations and basis metric matrices. Experimental results show that our method outperforms state-of-the-art methods on various data sets.

We study a novel problem of sponsored search (SS) for E-Commerce platforms: how we can attract query users to click product advertisements (ads) by presenting them features of products that attract them. This not only benefits merchants and the platform, but also improves user experience. The problem is challenging due to the following reasons: (1) We need to carefully manipulate the ad content without affecting user search experience. (2) It is difficult to obtain users' explicit feedback of their preference in product features. (3) Nowadays, a great portion of the search traffic in E-Commerce platforms is from their mobile apps (e.g., nearly 90% in Taobao). The situation would get worse in the mobile setting due to limited space. We are focused on the mobile setting and propose to manipulate ad titles by adding a few selling point keywords (SPs) to attract query users. We model it as a personalized attractive SP prediction problem and carry out both large-scale offline evaluation and online A/B tests in Taobao. The contributions include: (1) We explore various exhibition schemes of SPs. (2) We propose a surrogate of user explicit feedback for SP preference. (3) We also explore multi-task learning and various additional features to boost the performance. A variant of our best model has already been deployed in Taobao, leading to a 2% increase in revenue per thousand impressions and an opt-out rate of merchants less than 4%.

With over 34 billion IoT devices to be installed by 2020, the Internet of Things (IoT) is fundamentally changing our lives. One of the greatest benefits of the IoT is the powerful automations achieved by applying rules to IoT devices. For instance, a rule named "Make me a cup of coffee when I wake up'' automatically turns on the coffee machine when the sensor in the bedroom detects motion in the morning. With large numbers of possible rules out there, a recommendation system is of great necessity to help users find rules they need. However, little effort has been made to design a model tailored for the IoT rule recommendation, which comes with lots of new challenges compared with traditional recommendation tasks. We not only need to re-define "users'' and "items'' in the recommendation task, but also have to consider a new type of entities, devices, and the extra information and constraints brought by them. To handle these challenges, we propose a novel efficient recommendation algorithm, which not only considers the implicit feedback of users on rules, but also takes user-rule-device interactions and the match between rule device requirements and user device possessions into account. In collaboration with Samsung, one of the leading companies in this field, we have designed an IoT rule recommendation framework and evaluated our algorithm on a real-life industry dataset. Experiments show the effectiveness and efficiency of our method.

In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality. However, existing ranking systems are not specifically designed to satisfy this kind of demand. Some design tricks may partially alleviate this problem, but still cannot provide satisfactory results or may create additional interaction cost. In this paper, we design the first brand-level ranking system to address this problem. The key challenge of this system is how to sufficiently exploit users' rich behavior in e-commerce websites to rank the brands. In our solution, we firstly conduct the feature engineering specifically tailored for the personalized brand ranking problem and then rank the brands by an adapted Attention-GRU model containing three important modifications. Note that our proposed modifications can also apply to many other machine learning models on various tasks. We conduct a series of experiments to evaluate the effectiveness of our proposed ranking model and test the response to the brand-level ranking system from real users on a large-scale e-commerce platform, i.e. Taobao.

Product reviews are valuable for upcoming buyers in helping them make decisions. To this end, different opinion mining techniques have been proposed, where judging a review sentence's orientation (e.g., positive or negative) is one of their key challenges. Recently, deep learning has emerged as an effective means for solving sentiment classification problems. A neural network intrinsically learns a useful representation automatically without human efforts. However, the success of deep learning highly relies on the availability of large-scale training data. We propose a novel deep learning framework for product review sentiment classification which employs prevalently available ratings as weak supervision signals. The framework consists of two steps: (1) learning a high level representation (an embedding space) which captures the general sentiment distribution of sentences through rating information; and (2) adding a classification layer on top of the embedding layer and use labeled sentences for supervised fine-tuning. We explore two kinds of low level network structure for modeling review sentences, namely, convolutional feature extractors and long short-term memory. To evaluate the proposed framework, we construct a dataset containing 1.1M weakly labeled review sentences and 11,754 labeled review sentences from Amazon. Experimental results show the efficacy of the proposed framework and its superiority over baselines.

Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users' actions, and RNN has been proved to perform excellently when modeling sequential data. In traditional tasks such as language modeling, RNN solutions usually only consider the sequential order of objects without the notion of interval. However, in RS, time intervals between users' actions are of significant importance in capturing the relations of users' actions and the traditional RNN architectures are not good at modeling them. In this paper, we propose a new LSTM variant, i.e. Time-LSTM, to model users' sequential actions. Time-LSTM equips LSTM with time gates to model time intervals. These time gates are specifically designed, so that compared to the traditional RNN solutions, Time-LSTM better captures both of users' short-term and long-term interests, so as to improve the recommendation performance. Experimental results on two real-world datasets show the superiority of the recommendation method using Time-LSTM over the traditional methods.

With email overload becoming a billion-level drag on the economy, personalized email prioritization is of urgent need to help predict the importance level of an email. Despite lots of previous effort on the topic, broadcast email, an important type of emails with its unique challenges and intriguing opportunities, has been overlooked. The most salient opportunity lies in that effective collaborative filtering can be exploited due to thousands of receivers of a typical broadcast email. However, every broadcast email is completely cold and it is very costly to obtain users' preference feedback. Fortunately, there exist up to million-level broadcast mailing lists in a real life email system. Similar mailing lists can provide useful extra information for broadcast email prioritization in a target mailing list. How to mine such useful extra information is a challenging problem that has never been touched. In this work, we propose the first broadcast email prioritization framework considering large numbers of mailing lists by formulating this problem as a cross domain recommendation problem. An optimization framework is proposed to select the optimal set of source domains considering multiple criteria including overlap of users, feedback pattern similarity and coverage of users. Our method is thoroughly evaluated on a real world industrial dataset from Samsung Electronics and is proved highly effective and outperforms all the baselines.

Email is one of the most important communication tools today, but email overload resulting from the large number of unimportant or irrelevant emails is causing trillion-level economy loss every year. Thus personalized email prioritization algorithms are of urgent need. Despite lots of previous effort on this topic, broadcast email, an important type of email, is overlooked in previous literature. Broadcast emails are significantly different from normal emails, introducing both new challenges and opportunities. On one hand, lack of real senders and limited user interactions invalidate the key features exploited by traditional email prioritization algorithms; on the other hand, thousands of receivers for one broadcast email bring us the opportunity to predict importance through collaborative filtering. However, broadcast emails face a severe cold-start problem which hinders the direct application of collaborative filtering. In this paper, we propose the first framework for broadcast email prioritization by designing a novel active learning model that considers the collaborative filtering, implicit feedback and time sensitive responsiveness features of broadcast emails. Our method is thoroughly evaluated on a large scale real world industrial dataset from Samsung Electronics. Our method is proved highly effective and outperforms state-of-the-art personalized email prioritization methods.

Social explanation, the statement with the form of "A and B also like the item", is widely used in almost all the major recommender systems in the web and effectively improves the persuasiveness of the recommendation results by convincing more users to try. This paper presents the first algorithm to generate the most persuasive social explanation by recommending the optimal set of users to be put in the explanation. New challenges like modeling persuasiveness of multiple users, different types of users in social network, sparsity of likes, are discussed in depth and solved in our algorithm. The extensive evaluation demonstrates the advantage of our proposed algorithm compared with traditional methods.

Nowadays, micro-blogging systems like Twitter have become one of the most important ways for information sharing. In Twitter, a user posts a message (tweet) and the others can forward the message (retweet). Mention is a new feature in micro-blogging systems. By mentioning users in a tweet, they will receive notifications and their possible retweets may help to initiate large cascade diffusion of the tweet. To enhance a tweet's diffusion by finding the right persons to mention, we propose in this paper a novel recommendation scheme named as whom-to-mention. Specifically, we present an in-depth study of mention mechanism and propose a recommendation scheme to solve the essential question of whom to mention in a tweet. In this paper, whom-to-mention is formulated as a ranking problem and we try to address several new challenges which are not well studied in the traditional information retrieval tasks. By adopting features including user interest match, content-dependent user relationship and user influence, a machine learned ranking function is trained based on newly defined information diffusion based relevance. The extensive evaluation using data gathered from real users demonstrates the advantage of our proposed algorithm compared with the traditional recommendation methods.