The real-time bidding (RTB), aka programmatic buying, has recently become the fastest growing area in online advertising. Instead of bulking buying and inventory-centric buying, RTB mimics stock exchanges and utilises computer algorithms to automatically buy and sell ads in real-time; It uses per impression context and targets the ads to specific people based on data about them, and hence dramatically increases the effectiveness of display advertising. In this paper, we provide an empirical analysis and measurement of a production ad exchange. Using the data sampled from both demand and supply side, we aim to provide first-hand insights into the emerging new impression selling infrastructure and its bidding behaviours, and help identifying research and design issues in such systems. From our study, we observed that periodic patterns occur in various statistics including impressions, clicks, bids, and conversion rates (both post-view and post-click), which suggest timedependent models would be appropriate for capturing the repeated patterns in RTB. We also found that despite the claimed second price auction, the first price payment in fact is accounted for 55.4% of total cost due to the arrangement of the soft floor price. As such, we argue that the setting of soft floor price in the current RTB systems puts advertisers in a less favourable position. Furthermore, our analysis on the conversation rates shows that the current bidding strategy is far less optimal, indicating the significant needs for optimisation algorithms incorporating the facts such as the temporal behaviours, the frequency and recency of the ad displays, which have not been well considered in the past.

In this paper, we study collaborative filtering (CF) in an interactive setting, in which a recommender system continuously recommends items to individual users and receives interactive feedback. Whilst users enjoy sequential recommendations, the recommendation predictions are constantly refined using up-to-date feedback on the recommended items. Bringing the interactive mechanism back to the CF process is fundamental because the ultimate goal for a recommender system is about the discovery of interesting items for individual users and yet users' personal preferences and contexts evolve over time during the interactions with the system. This requires us not to distinguish between the stages of collecting information to construct the user profile and making recommendations, but to seamlessly integrate these stages together during the interactive process, with the goal of maximizing the overall recommendation accuracy throughout the interactions. This mechanism naturally addresses the cold-start problem as any user can immediately receive sequential recommendations without providing ratings beforehand. We formulate the interactive CF with the probabilistic matrix factorization (PMF) framework, and leverage several exploitation-exploration algorithms to select items, including the empirical Thompson sampling and upper confidence bound based algorithms. We conduct our experiment on cold-start users as well as warm-start users with drifting taste. Results show that the proposed methods have significant improvements over several strong baselines for the MovieLens, EachMovie and Netflix datasets.

This paper studies result diversification in collaborative filtering. We argue that the diversification level in a recommendation list should be adapted to the target users' individual situations and needs. Different users may have different ranges of interests -the preference of a highly focused user might include only few topics, whereas that of the user with broad interests may encompass a wide range of topics. Thus, the recommended items should be diversified according to the interest range of the target user. Such an adaptation is also required due to the fact that the uncertainty of the estimated user preference model may vary significantly between users. To reduce the risk of the recommendation, we should take the difference of the uncertainty into account as well.In this paper, we study the adaptive diversification problem theoretically. We start with commonly used latent factor models and reformulate them using the mean-variance analysis from the portfolio theory in text retrieval. The resulting Latent Factor Portfolio (LFP) model captures the user's interest range and the uncertainty of the user preference by employing the variance of the learned user latent factors. It is shown that the correlations between items (and thus the item diversity) can be obtained by using the correlations between latent factors (topical diversity), which in return significantly reduce the computation load. Our mathematical derivation also reveals that diversification is necessary, not only for risk-averse system behavior (nonadpative), but also for the target users' individual situations (adaptive), which are represented by the distribution and the variance of the latent user factors. Our experiments confirm the theoretical insights and show that LFP succeeds in improving latent factor models by adaptively introducing recommendation diversity to fit the individual user's needs.

In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data. In this paper, we propose a novel approach to explicitly modelling the uncertainty of average F1 scores through Bayesian reasoning.

No abstract

Bisphenol A (BPA) analogues, used in a range of products due to health concerns regarding BPA, have emerged as ubiquitous environmental contaminants worldwide. This study aims to evaluate the levels of nine bisphenols (BPs) and eight biomarkers (malondialdehyde, MDA; 8-hydroxy-2′-deoxyguanosine, 8-OHdG; estradiol, E2; follicle-stimulating hormone, FSH; luteinizing hormone, LH; complement compound 3, C3; immunoglobulin M, IgM and c-reaction protein, CRP) in human serum (n = 353) to explore their potential relationships. The detection rates (DRs) of eight BPs in serum samples taken from people working in a dense industrial area of Shenzhen (Guangdong Province, China) were over 72% except for bisphenol B (BPB) (DR = 27.5%). The mean concentrations of BPA, bisphenol P (BPP), BPB, bisphenol F (BPF), bisphenol FL (BPFL), 4,4′-dihydroxy-benzophenone (DHBP), bisphenol AF (BPAF), 4,4′-thiodiphenol (TDP) and bisphenol S (BPS) were 42.062, 2.083, 0.765, 0.578, 0.423, 0.402, 0.191, 0.120, and 0.071 ng/mL, respectively. BPA and BPFL were significantly correlated with the level of oxidative stress indices MDA and 8-OHdG; BPAF, BPB, and DHBP were strongly correlated with the level of endocrine disturbance indices E2, FSH, and LH; and BPF, DHBP, and BPAF were apparently related to the level of immune interference indices C3 and IgM. This study also suggests multiple impacts (oxidative stress, endocrine disturbance, and immune interference) mediated by BPs contaminants in vivo. To our knowledge, this is the first study to report the correlations among these nine serum BPs and oxidative stress and endocrine and immune system indices in human serum samples collected from dense industrial areas.

No abstract

No abstract