Although deep pre-trained language models have shown promising benefit in a large set of industrial scenarios, including Click-Through-Rate (CTR) prediction, how to integrate pre-trained language models that handle only textual signals into a prediction pipeline with non-textual features is challenging.

In sponsored search engines, pre-trained language models have shown promising performance improvements on Click-Through-Rate (CTR) prediction. A widely used approach for utilizing pre-trained language models in CTR prediction consists of fine-tuning the language models with click labels and early stopping on peak value of the obtained Area Under the ROC Curve (AUC). Thereafter the output of these fine-tuned models, i.e., the final score or intermediate embedding generated by language model, is used as a new Natural Language Processing (NLP) feature into CTR prediction baseline. This cascade approach avoids complicating the CTR prediction baseline, while keeping flexibility and agility. However, we show in this work that calibrating separately the language model based on the peak single model AUC does not always yield NLP features that give the best performance in CTR prediction model ultimately. Our analysis reveals that the misalignment is due to overlap and redundancy between the new NLP features and the existing features in CTR prediction baseline. In other words, the NLP features can improve CTR prediction better if such overlap can be reduced.

Sampling social graphs is critical for studying things like information diffusion. However, it is often necessary to laboriously obtain unbiased and well-connected datasets because existing survey algorithms are unable to generate well-connected samples, and current random-walk based unbiased sampling algorithms adopt rejection sampling, which heavily undermines performance. This paper proposes a novel random-walk based algorithm which implements Unbiased Sampling using Dummy Edges (USDE). It injects dummy edges between nodes, on which the walkers would otherwise experience excessive rejections before moving out from such nodes. We propose a rejection probability estimation algorithm to facilitate the construction of dummy edges and the computation of moving probabilities. Finally, we apply USDE in two real-life social media: Twitter and Sina Weibo. The results demonstrate that USDE generates well-connected samples, and outperforms existing approaches in terms of sampling efficiency and quality of samples.

In Search Engine Marketing (SEM), “third-party” partners play an important intermediate role by bridging the gap between search engines and advertisers in order to optimise advertisers' campaigns in exchange of a service fee. In this paper, we present an economic analysis of the market involving a third-party broker in Google AdWords and the broker's customers. We show that in order to optimise his profit, a third-party broker should minimise the weighted average Cost Per Click (CPC) of the portfolio of keywords attached to customer's ads while still satisfying the negotiated customer's demand. To help the broker build and manage such portfolio of keywords, we develop an optimisation framework inspired from the classical Markowitz portfolio management which integrates the customer's demand constraint and enables the broker to manage the tradeoff between return on investment and risk through a single risk aversion parameter. We then propose a method to augment the keywords portfolio with relevant keywords extracted from trending and popular topics on Twitter. Our evaluation shows that such a keywords-augmented strategy is very promising and enables the broker to achieve, on average, four folds larger return on investment than with a non-augmented strategy, while still maintaining the same level of risk.

In this paper, we study the process of information diffusion in a microblog service developing Galton-Watson with Killing (GWK) model. Microblog services offer a unique approach to online information sharing allowing microblog users to forward messages to others. We describe an information propagation as a discrete GWK process based on Galton-Watson model which models the evolution of family names. Our model explains the interaction between the topology of the social graph and the intrinsic interest of the message. We validate our model on dataset collected from Sina Weibo and Twitter microblog. Sina Weibo is a Chinese microblog web service which reached over 100 million users as for January 2011. Our Sina Weibo dataset contains over 261 thousand tweets which have retweets and 2 million retweets from 500 thousand users. Twitter dataset contains over 1.1 million tweets which have retweets and 3.3 million retweets from 4.3 million users. The results of the validation show that our proposed GWK model fits the information diffusion of microblog service very well in terms of the number of message receivers. We show that our model can be used in generating tweets load and also analyze the relationships between parameters of our model and popularity of the diffused information. To the best of our knowledge, this paper is the first to give a systemic and comprehensive analysis for the information diffusion on microblog services, to be used in tweets-like load generators while still guaranteeing popularity distribution characteristics.

In this work, we aim to analyze and compare interaction patterns in different types of social platforms. To this end, we measured Renren, the largest online social network in China, and Sina Weibo, the most popular microblog service in China. We model the interaction networks as unidirectional weighted graphs in light of the asymmetry of user interactions. Following this model, we first study the basic interaction patterns. Then, we examine whether weak ties hypothesis holds in these interaction graphs and analyze the impacts on information diffusion. Furthermore, we model the temporal patterns of user interactions and cluster users based on the temporal patterns. Our findings demonstrate that although users in the two platforms share some common interaction patterns, users in Sina Weibo are more popular and diverse. Moreover, analysis and simulation results show that Sina Weibo is a more efficient platform for information diffusion. These findings provide an in-depth understanding of interaction patterns in different social platforms and can be used for the design of efficient information diffusion.

Unbiased sampling of online social networks (OSNs) makes it possible to get accurate statistical properties of large-scale OSNs. However, the most used sampling methods, Breadth-First-Search (BFS) and Greedy, are known to be biased towards high degree nodes, yielding inaccurate statistical results. To give a general requirement for unbiased sampling, we model the crawling process as a Markov Chain and deduce a necessary and sufficient condition, which enables us to design various efficient unbiased sampling methods. To the best of our knowledge, we are among the first to give such a condition. Metropolis-Hastings Random Walk (MHRW) is an example which satisfies the condition. However, walkers in MHRW may stay at some low-degree nodes for a long time, resulting considerable self-loops on these nodes, which adversely affect the crawling efficiency. Based on the condition, a new unbiased sampling method, called USRS, is proposed to reduce the probabilities of self-loops. We use the dataset of Renren, the largest OSN in China, to evaluate the performance of USRS. The results have demonstrated that USRS generates unbiased samples with low self-loop probabilities, and achieves higher crawling efficiency.