Mobile advertising has undoubtedly become one of the fastest-growing industries in the world. The influx of capital attracts increasing fraudsters to defraud money from advertisers. Fraudsters can leverage many techniques, where bots install fraud is the most difficult to detect due to its ability to emulate normal users by implementing sophisticated behavioral patterns to evade from detection rules defined by human experts. Therefore, we proposed BotSpot 1 for bots install fraud detection previously. However, there are some drawbacks in BotSpot, such as the sparsity of the devices’ neighbors, weak interactive information of leaf nodes, and noisy labels. In this work, we propose BotSpot++ to improve these drawbacks: (1) for the sparsity of the devices’ neighbors, we propose to construct a super device node to enrich the graph structure and information flow utilizing domain knowledge and a clustering algorithm; (2) for the weak interactive information, we propose to incorporate a self-attention mechanism to enhance the interaction of various leaf nodes; and (3) for the noisy labels, we apply a label smoothing mechanism to alleviate it. Comprehensive experimental results show that BotSpot++ yields the best performance compared with six state-of-the-art baselines. Furthermore, we deploy our model to the advertising platform of Mobvista, 2 a leading global mobile advertising company. The online experiments also demonstrate the effectiveness of our proposed method.

Mobile advertising has become inarguably one of the fastest growing industries all over the world. The influx of capital attracts increasing fraudsters to defraud money from advertisers. There are many tricks a fraudster can leverage, among which bot install fraud is undoubtedly the most insidious one due to its ability to implement sophisticated behavioral patterns and emulate normal users, so as to evade from detection rules defined by human experts. In this work, we propose an anti-fraud method based on heterogeneous graph that incorporates both local context and global context via graph neural networks (GNN) and gradient boosting classifier to detect bot fraud installs at Mobvista, a leading global mobile advertising company. Offline evaluations in two datasets show the proposed method outperforms all the competitive baseline methods by at least 2.2% in the first dataset and 5.75% in the second dataset given the evaluation metric [email protected]% Precision. Furthermore, we deploy our method to tackle million-scale data daily at Mobvista. The online performance also shows that the proposed methods consistently detect more bots than other baseline methods.

Collaborative Filtering (CF) by learning from the wisdom of crowds has become one of the most important approaches to recommender systems research, and various CF models have been designed and applied to different scenarios. However, a challenging task is how to select the most appropriate CF model for a specific recommendation task. In this paper, we propose a Unified Collaborative Filtering framework based on Graph Embeddings (UGrec for short) to solve the problem. Specifically, UGrec models user and item interactions within a graph network, and sequential recommendation path is designed as a basic unit to capture the correlations between users and items. Mathematically, we show that many representative recommendation approaches and their variants can be mapped as a recommendation path in the graph. In addition, by applying a carefully designed attention mechanism on the recommendation paths, UGrec can determine the significance of each sequential recommendation path so as to conduct automatic model selection. Compared with state-of-the-art methods, our method shows significant improvements for recommendation quality. This work also leads to a deeper understanding of the connection between graph embeddings and recommendation algorithms.

Although marketing researchers and sociologists have recognized the large impact of life stage on consumer's purchasing behaviors, existing recommender systems have not taken this impact into consideration. In this paper, we found obvious correlation between life stage and purchasing behavior in many E-commerce categories. For example, a mum may look for different suitable products when her baby is at different ages. Motivated by this, we introduce the conception of life stage into recommender systems and propose to predict a user's current life-stage and recommend products correspondingly. We propose a new Maximum Entropy Semi Markov Model to segment and label consumer life stage based on the observed purchasing data over time. In the mom-baby product category where the life stage transition is deterministic, we develop an efficient approximate solution using large scale logistic regression and a Viterbi-like algorithm. We also propose a Gaussian mixture model to efficiently handle multi-kids life stage prediction problem. We integrate the life stage information predicted into the recommender system behind the largest online shopping website taobao.com. Both offline and online experiments demonstrate the effectiveness of the proposed life-stage based recommendation approach.

Search result diversification has gained attention as a way to tackle the ambiguous or multi-faceted information needs of users. Most existing methods on this problem utilize a heuristic predefined ranking function, where limited features can be incorporated and extensive tuning is required for different settings. In this paper, we address search result diversification as a learning problem, and introduce a novel relational learning-to-rank approach to formulate the task. However, the definitions of ranking function and loss function for the diversification problem are challenging. In our work, we firstly show that diverse ranking is in general a sequential selection process from both empirical and theoretical aspects. On this basis, we define ranking function as the combination of relevance score and diversity score between the current document and those previously selected, and loss function as the likelihood loss of ground truth based on Plackett-Luce model, which can naturally model the sequential generation of a diverse ranking list. Stochastic gradient descent is then employed to conduct the unconstrained optimization, and the prediction of a diverse ranking list is provided by a sequential selection process based on the learned ranking function. The experimental results on the public TREC datasets demonstrate the effectiveness and robustness of our approach.

Topic-focused multi-document summarization aims to produce a summary over a set of documents and conveys the most important aspects of a given topic. Most existing extractive methods view the task as a multi-criteria ranking problem over sentences, where relevance, salience and diversity are three typical requirements. However, diversity is a challenging problem as it involves modeling the relationship between sentences during ranking, where traditional methods usually tackle it in a heuristic or implicit way. In this paper, we propose a novel relational learning-to-rank approach (R-LTR) to solve this problem. Relational learning-to-rank is a new learning framework which further incorporates relationships into traditional learning-to-rank in an elegant way. Specifically, the ranking function is defined as the combination of content-based score of individual sentence, and relation-based score between the current sentence and those already selected. On this basis, we propose to learn the ranking function by minimizing the likelihood loss based on Plackett-Luce model, which can naturally model the sequential ranking procedure of candidate sentences. Stochastic gradient descent is then employed to conduct the learning process, and the summary is predicted by the greedy selection procedure based on the learned ranking function. Finally, we conduct extensive experiments on benchmark data sets TAC2008 and TAC2009. Experimental results show that our approach can significantly outperform the state-of-the-art methods from both quantitative and qualitative aspects.