International Workshop on Data Mining and Audience Intelligence for Advertising
About: International Workshop on Data Mining and Audience Intelligence for Advertising is an academic conference. The conference publishes majorly in the area(s): Online advertising & Search advertising. Over the lifetime, 29 publication(s) have been published by the conference receiving 593 citation(s).
Topics: Online advertising, Search advertising, The Internet, Ranking (information retrieval), Advertising campaign
••12 Aug 2007
TL;DR: This work presents a system that is language independent and knowledge free based on SVM ranking, and introduces two new classes of features of similarity between ads and Web pages based on machine translation technologies.
Abstract: Contextual advertising is a growing category of search advertising. It presents a particular challenge to ad placement systems because of the sparseness of the language of advertising. We present a system that is language independent and knowledge free based on SVM ranking. We evaluate it on a large number of advertisements appearing on real Web pages. Our contribution is two new classes of features of similarity between ads and Web pages based on machine translation technologies. We show that our features significantly improve performance over baseline techniques.
••12 Aug 2007
TL;DR: This paper takes a webpage classification approach to solve the problem of how to detect whether a publisher webpage contains sensitive content and is appropriate for showing advertisement(s) on it, and designs a unique sensitive content taxonomy.
Abstract: Online advertising has been a popular topic in recent years. In this paper, we address one of the important problems in online advertising, i.e., how to detect whether a publisher webpage contains sensitive content and is appropriate for showing advertisement(s) on it.We take a webpage classification approach to solve this problem. First we design a unique sensitive content taxonomy. Then we adopt an iterative training data collection and classifier building approach, to build a hierarchical classifier which can classify webpages into one of the nodes in the sensitive content taxonomy. The experimental result show that using this approach, we are able to build a unique sensitive content classifier with decent accuracy while only requiring limited amount of human labeling effort.
••28 Jun 2009
TL;DR: The Argo system attempts to learn a user's profile from his shared photos and suggests relevant ads accordingly and represents ads in the topic space and matching their topic distributions with the target user interest.
Abstract: In this paper, we introduce a system named Argo which provides intelligent advertising made possible from users' photo collections. Based on the intuition that user-generated photos imply user interests which are the key for profitable targeted ads, the Argo system attempts to learn a user's profile from his shared photos and suggests relevant ads accordingly. To learn a user interest, in an offline step, a hierarchical and efficient topic space is constructed based on the ODP ontology, which is used later on for bridging the vocabulary gap between ads and photos as well as reducing the effect of noisy photo tags. In the online stage, the process of Argo contains three steps: 1) understanding the content and semantics of a user's photos and auto-tagging each photo to supplement user-submitted tags (such tags may not be available); 2) learning the user interest given a set of photos based on the learnt hierarchical topic space; and 3) representing ads in the topic space and matching their topic distributions with the target user interest; the top ranked ads are output as the suggested ads. Two key challenges are tackled during the process: 1) the semantic gap between the low-level image visual features and the high-level user semantics; and 2) the vocabulary impedance between photos and ads. We conducted a series of experiments based on real Flickr users and Amazon.com products (as candidate ads), which show the effectiveness of the proposed approach.
••28 Jun 2009
TL;DR: This work proposes a novel user segmentation algorithm named Probabilistic Latent Semantic User Segmentation (PLSUS), which adopts the probabilistic latent semantic analysis to mine the relationship between users and their behaviors so as to segment users in a semantic manner.
Abstract: Behavioral Targeting (BT), which aims to deliver the most appropriate advertisements to the most appropriate users, is attracting much attention in online advertising market. A key challenge of BT is how to automatically segment users for ads delivery, and good user segmentation may significantly improve the ad click-through rate (CTR). Different from classical user segmentation strategies, which rarely take the semantics of user behaviors into consideration, we propose in this paper a novel user segmentation algorithm named Probabilistic Latent Semantic User Segmentation (PLSUS). PLSUS adopts the probabilistic latent semantic analysis to mine the relationship between users and their behaviors so as to segment users in a semantic manner. We perform experiments on the real world ad click through log of a commercial search engine. Comparing with the other two classical clustering algorithms, K-Means and CLUTO, PLSUS can further improve the ads CTR up to 100%. To our best knowledge, this work is an early semantic user segmentation study for BT in academia.
••24 Aug 2008
TL;DR: Two approaches for measuring the relevance between a document and a phrase aiming to provide consistent relevance scores for both in and out-of document phrases are explored.
Abstract: Measuring the relevance between a document and a phrase is fundamental to many information retrieval and matching tasks including on-line advertising. In this paper, we explore two approaches for measuring the relevance between a document and a phrase aiming to provide consistent relevance scores for both in and out-of document phrases. The first approach is a similarity-based method which represents both the document and phrase as term vectors to derive a real-valued relevance score. The second approach takes as input the relevance estimates of some in-document phrases and uses Gaussian Process Regression to predict the score of a target out-of-document phrase. While both of these two approaches work well, the best result is given by a Gaussian Process Regression model, which is significantly better than the similarity-based approach and 10% better than a baseline similarity method using bag-of-word vectors.
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