4 research outputs found
Sequential Selection of Correlated Ads by POMDPs
Online advertising has become a key source of revenue for both web search
engines and online publishers. For them, the ability of allocating right ads to
right webpages is critical because any mismatched ads would not only harm web
users' satisfactions but also lower the ad income. In this paper, we study how
online publishers could optimally select ads to maximize their ad incomes over
time. The conventional offline, content-based matching between webpages and ads
is a fine start but cannot solve the problem completely because good matching
does not necessarily lead to good payoff. Moreover, with the limited display
impressions, we need to balance the need of selecting ads to learn true ad
payoffs (exploration) with that of allocating ads to generate high immediate
payoffs based on the current belief (exploitation). In this paper, we address
the problem by employing Partially observable Markov decision processes
(POMDPs) and discuss how to utilize the correlation of ads to improve the
efficiency of the exploration and increase ad incomes in a long run. Our
mathematical derivation shows that the belief states of correlated ads can be
naturally updated using a formula similar to collaborative filtering. To test
our model, a real world ad dataset from a major search engine is collected and
categorized. Experimenting over the data, we provide an analyse of the effect
of the underlying parameters, and demonstrate that our algorithms significantly
outperform other strong baselines
A unified posterior regularized topic model with maximum margin for learning-to-rank
While most methods for learning-to-rank documents only consider relevance scores as features, better results can often be obtained by taking into account the latent topic structure of the document collection. Existing approaches that consider latent topics follow a two-stage approach, in which topics are discovered in an unsupervised way, as usual, and then used as features for the learning-to-rank task. In contrast, we propose a learning-to-rank framework which integrates the supervised learning of a maximum margin classifier with the discovery of a suitable probabilistic topic model. In this way, the labelled data that is available for the learning-to-rank task can be exploited to identify the most appropriate topics. To this end, we use a unified constrained optimization framework, which can dynamically compute the latent topic similarity score between the query and the document. Our experimental results show a consistent improvement over the state-of-the-art learning-to-rank models
Supervised topic models with word order structure for document classification and retrieval learning
One limitation of most existing probabilistic latent topic models for document classification is that the topic model itself does not consider useful side-information, namely, class labels of documents. Topic models, which in turn consider the side-information, popularly known as supervised topic models, do not consider the word order structure in documents. One of the motivations behind considering the word order structure is to capture the semantic fabric of the document. We investigate a low-dimensional latent topic model for document classification. Class label information and word order structure are integrated into a supervised topic model enabling a more effective interaction among such information for solving document classification. We derive a collapsed Gibbs sampler for our model. Likewise, supervised topic models with word order structure have not been explored in document retrieval learning. We propose a novel supervised topic model for document retrieval learning which can be regarded as a pointwise model for tackling the learning-to-rank task. Available relevance assessments and word order structure are integrated into the topic model itself. We conduct extensive experiments on several publicly available benchmark datasets, and show that our model improves upon the state-of-the-art models
Learning to rank audience for behavioral targeting in display ads
10.1145/2063576.2063666International Conference on Information and Knowledge Management, Proceedings605-61