Next point-of-interest (POI) recommendation is a hot research field where a recent emerging scenario, next POI to search recommendation, has been deployed in many online map services such as Baidu Maps. One of the key issues in this scenario is providing satisfactory recommendation services for cold-start cities with a limited number of user-POI interactions, which requires transferring the knowledge hidden in rich data from many other cities to these cold-start cities. Existing literature either does not consider the city-transfer issue or cannot simultaneously tackle the data sparsity and pattern diversity issues among various users in multiple cities. To address these issues, we explore city-transfer next POI to search recommendation that transfers the knowledge from multiple cities with rich data to cold-start cities with scarce data. We propose a novel Curriculum Hardness Aware Meta-Learning (CHAML) framework, which incorporates hard sample mining and curriculum learning into a meta-learning paradigm. Concretely, the CHAML framework considers both city-level and user-level hardness to enhance the conditional sampling during meta training, and uses an easy-to-hard curriculum for the city-sampling pool to help the meta-learner converge to a better state. Extensive experiments on two real-world map search datasets from Baidu Maps demonstrate the superiority of CHAML framework.

Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation model based on semi-homomorphic encryption, where every involved party can jointly obtain a shared Gradient Boosting machines model while protecting their own data from the potential privacy leakage and inferential identification. More specific, our work focused on a specific “dualparty” secure learning scenario based on two parties — both party own an unique view (i.e., attributes or features) to the sample group of samples while only one party owns the labels. In such scenario, feature and label data are not allowed to share with others.To achieve the above goal, we firstly extent — LightGBM — a well known implementation of tree-based GBM through covering its key operations for training and inference with SEAL homomorphic encryption schemes. However, the performance of such re-implementation is significantly bottle-necked by the explosive inflation of the communication payloads, based on ciphertexts subject to the increasing length of plaintexts. In this way, we then proposed to use stochastic approximation techniques to reduced the communication payloads while accelerating the overall training procedure in a statistical manner. Our experiments using the real-world data showed that SecureGBM can well secure the communication and computation of LightGBM training and inference procedures for the both parties while only losing less than 3% AUC, using the same number of iterations for gradient boosting, on a wide range of benchmark datasets. More specific, compared to LightGBM, the proposed SecureGBM would slowdown with $3\mathrm{x} \sim 64\mathrm{x}$ time consumption per iteration in the training procedure, while SecureGBM becomes more and more efficient when the scale of the training dataset increases (i.e., the larger training set, the lower slowdown ratio).

Question Answering (QA) systems are used to provide proper responses to users' questions automatically. Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem. Given a question, the aim of the task is to find the most similar question from a QA knowledge base. In this paper, we propose a Multi-task Sentence Encoding Model (MSEM) for the PI problem, wherein a connected graph is employed to depict the relation between sentences, and a multi-task learning model is applied to address both the sentence matching and sentence intent classification problem. In addition, we implement a general semantic retrieval framework that combines our proposed model and the Approximate Nearest Neighbor (ANN) technology, which enables us to find the most similar question from all available candidates very quickly during online serving. The experiments show the superiority of our proposed method as compared with the existing sentence matching models.

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information. We do this within an encoder-decoder sequence learning framework that integrates the following data: 1) offline geographical and social attributes. For example, the geographical structure of roads or public social events such as national celebrations; 2) road intersection information. In general, traffic congestion occurs at major junctions; 3) online crowd queries. For example, when many online queries issued for the same destination due to a public performance, the traffic around the destination will potentially become heavier at this location after a while. Qualitative and quantitative experiments on a real-world dataset from Baidu have demonstrated the effectiveness of our framework.

With the ever-increasing urbanization process, the traffic jam has become a common problem in the metropolises around the world, making the traffic speed prediction a crucial and fundamental task. This task is difficult due to the dynamic and intrinsic complexity of the traffic environment in urban cities, yet the emergence of crowd map query data sheds new light on it. In general, a burst of crowd map queries for the same destination in a short duration (called "hotspot'') could lead to traffic congestion. For example, queries of the Capital Gym burst on weekend evenings lead to traffic jams around the gym. However, unleashing the power of crowd map queries is challenging due to the innate spatiotemporal characteristics of the crowd queries. To bridge the gap, this paper firstly discovers hotspots underlying crowd map queries. These discovered hotspots address the spatiotemporal variations. Then Dest-ResNet (Deep spatiotemporal Residual Network) is proposed for hotspot traffic speed prediction. Dest-ResNet is a sequence learning framework that jointly deals with two sequences in different modalities, i.e., the traffic speed sequence and the query sequence. The main idea of Dest-ResNet is to learn to explain and amend the errors caused when the unimodal information is applied individually. In this way, Dest-ResNet addresses the temporal causal correlation between queries and the traffic speed. As a result, Dest-ResNet shows a 30% relative boost over the state-of-the-art methods on real-world datasets from Baidu Map.

To combat the ease of online shopping in pajamas, offline mall owners focus increasingly on driving satisfaction and improving retention by identifying customers' preferences. However, most of these studies are based on customers' offline consuming history only. Benefiting from the internet, we can also get customers' online behaviors, such as the search logs, web browsing logs, online shopping logs, and so on. Might these seemingly irrelevant information from two different modalities (i.e. online and offline) be somehow interrelated? How can we make use of the online behaviors and offline actions jointly to promote recommendation for offline retailing? In this study, we formulate this task as a cross-modality recommendation problem, and present its solution via a proposed probabilistic graphical model, called Online-to-Offline Topic Modeling (O2OTM). Specifically, this method explicitly models the relationships between online and offline topics so that the likelihood of both online and offline behaviors is maximized. Then, the recommendation is made only based on the pairs of online and offline topics, denoted by (t,l), with high values of lift, such that the existence of the online topic $t$ greatly increases the response on the corresponding offline topic $l$ compared with the average response for the population without the online topic t. Furthermore, we evaluate this solution in both live and retrospect experiments. The real-world deployment of this model for the anniversary promotion campaign of a famous shopping mall in Beijing shows that our approach increases the occurred customer purchases per promotion message by 29.75\% compared with the baseline. Also, our model finds some interesting interpretable relationships between the online search topics and offline brand topics.

In this paper we propose a framework of topic modeling ensembles, a novel solution to combine the models learned by topic modeling over each partition of the whole corpus. It has the potentials for applications such as distributed topic modeling for large corpora, and incremental topic modeling for rapidly growing corpora. Since only the base models, not the original documents, are required in the ensemble, all these applications can be performed in a privacy preserving manner. We explore the theoretical foundation of the proposed framework, give its geometric interpretation, and implement it for both PLSA and LDA. The evaluation of the implementations over the synthetic and real-life data sets shows that the proposed framework is much more efficient than modeling the original corpus directly while achieves comparable effectiveness in terms of perplexity and classification accuracy.