Blogger-Centric Contextual Advertising

Web advertising (online advertising), a form of advertising that uses the World Wide Web to attract customers, has become one of the most commonly-used marketing channels. This paper addresses the concept of Blogger-Centric Contextual Advertising, which refers to the assignment of personal ads to any blog page, chosen in according to bloggers’ interests. As blogs become a platform for expressing personal opinions, they naturally contain various kinds of statements, including facts, comments and statements about personal interests, of both a positive and negative nature. To extend the concept behind the Long Tail theory in contextual advertising, we argue that web bloggers, as the constant visitors of their own blog-sites, could be potential consumers who will respond to ads on their own blogs. Hence, in this paper, we propose using text mining techniques to discover bloggers’ immediate personal interests in order to improve online contextual advertising. The proposed Blogger-Centric Contextual Advertising (BCCA) framework aims to combine contextual advertising matching with text mining in order to select ads that are related to personal interests as revealed in a blog and rank them according to their relevance. We validate our approach experimentally using a set of data that includes both real ads and actual blog pages. The results indicate that our proposed method could effectively identify those ads that are positively-correlated with a blogger’s personal interests.     

Sentiment-oriented contextual advertising

Web advertising (Online advertising), a form of advertising that uses the World Wide Web to attract customers, has become one of the world’s most important marketing channels. This paper addresses the mechanism of Content-based advertising (Contextual advertising), which refers to the assignment of relevant ads to a generic web page, e.g., a blog post. As blogs become a platform for expressing personal opinion, they naturally contain various kinds of expressions, including both facts and comments of both a positive and negative nature. Besides, in line with the major tenet of Web 2.0 (i.e., user-centric), we believe that the web-site owners would be willing to be in charge of the ads which are positively related to their contents. Hence, in this paper, we propose the utilization of sentiment detection to improve Web-based contextual advertising. The proposed sentiment-oriented contextual advertising (SOCA) framework aims to combine contextual advertising matching with sentiment analysis to select ads that are related to the positive (and neutral) aspects of a blog and rank them according to their relevance. We experimentally validate our approach using a set of data that includes both real ads and actual blog pages. The results indicate that our proposed method can effectively identify those ads that are positively correlated with the given blog pages.     

GameSense: game-like in-image advertising

Considering the continuously increasing availability and accessibility of multimedia contents via social networking sites, our research addresses how to monetize the social multimedia contents with an efficient advertising approach. This paper presents a novel game-like advertising system called GameSense, which is driven by the compelling contents of online images. The contextually relevant ads (i.e., product logos) are embedded at appropriate positions within the online games, which are created on the basis of online images. The ads are selected based on multimodal relevance, i.e. text relevance, user relevance and visual content similarity. The game is able to provide viewers rich experience and thus promotes the embedded ads to provide more effective advertising. GameSense represents one of the first attempts toward effective online mashup applications which connect a photo-sharing site with an advertising agency. The effectiveness of GameSense is evaluated over a large-scale real world image set.     

Fuzzy Web ad selector based on Web usage mining

Internet and Web technologies are widely available, making it easier for companies to conduct business and transfer information to customers. Moreover, they speed up financial transactions efficiently, reducing the transaction costs of commercial activities that businesses would normally incur. So, Internet business has created a competitive environment, a successful company wanting to survive and gain a competitive advantage must provide an acceptable bundle of customized services that satisfy customers' needs. Despite the Internet's obvious benefits as a new communication medium its advertising gives the same advertising messages to all customers and so has suffered from poor responses. To raise a Web ad's effectiveness, we propose a Web ad selector that personalizes advertising messages for customers based on their preferences and interests. The Web ad selection system divides Web site customers with similar preferences into several segments through Web usage mining. It uses fuzzy rules that express customer segments' surfing patterns on the basis of expert advice, and recommends appropriate ads by fuzzy inference.     

Learning weights for translation candidates in Japanese–Chinese information retrieval

This paper describes our Japanese–Chinese information retrieval system. Our system takes the “query-translation” approach. Our system employs both a more conventional bilingual Japanese–Chinese dictionary and Wikipedia for translating query terms. We propose that Wikipedia can be used as a good NE bilingual dictionary. By exploiting the nature of Japanese writing system, we propose that query terms be processed differently based on the forms they are written in. We use an iterative method for weight-tuning and term disambiguation, which is based on the PageRank algorithm. When evaluating on the NTCIR-5 test set, our system achieves as high as 0.2217 and 0.2276 in relax MAP (mean average precision) measurement of T-runs and D-runs.     

AdOn: toward contextual overlay in-video advertising

This paper presents a contextual video advertising system, called AdOn, which supports intelligent overlay in-video advertising. Unlike most current ad-networks such as Youtube that overlay the ads at fixed locations in the videos (e.g., on the bottom fifth of videos 15 s in), AdOn is able to automatically detect a set of spatio-temporal non-intrusive locations and associate the contextually relevant ads with these locations. The overlay ad locations are obtained on the basis of video structuring, face and text detection, as well as visual saliency analysis, so that the intrusiveness to the users can be minimized. The ads are selected according to content-based multimodal relevance so that the relevance can be maximized. AdOn represents one of the first attempts towards contextual overlay video advertising by leveraging information retrieval and multimedia content analysis techniques. The experiments conducted on a video database with more than 100 video programs and 7,000 ad products indicated that AdOn is superior to existing advertising approaches in terms of ad relevance and user experience.     

A logistic regression model for Semantic Web service matchmaking

Semantic Web service matchmaking,as one of the most challenging problems in Semantic Web services (SWS),aims to filter and rank a set of services with respect to a service query by using a certain matching strategy.In this paper,we propose a logistic regression based method to aggregate several matching strategies instead of a fixed integration (e.g.,the weighted sum) for SWS matchmaking.The logistic regression model is trained on training data derived from binary relevance assessments of existing test collections,and then used to predict the probability of relevance between a new pair of query and service according to their matching values obtained from various matching strategies.Services are then ranked according to the probabilities of relevance with respect to each query.Our method is evaluated on two main test collections,SAWSDL-TC2 and Jena Geography Dataset(JGD).Experimental results show that the logistic regression model can effectively predict the relevance between a query and a service,and hence can improve the effectiveness of service matchmaking.     

Complex-query web image search with concept-based relevance estimation

Complex queries are widely used in current Web applications. They express highly specific information needs, but simply aggregating the meanings of primitive visual concepts does not perform well. To facilitate image search of complex queries, we propose a new image reranking scheme based on concept relevance estimation, which consists of Concept-Query and Concept-Image probabilistic models. Each model comprises visual, web and text relevance estimation. Our work performs weighted sum of the underlying relevance scores, a new ranking list is obtained. Considering the Web semantic context, we involve concepts by leveraging lexical and corpus-dependent knowledge, such as Wordnet and Wikipedia, with co-occurrence statistics of tags in our Flickr corpus. The experimental results showed that our scheme is significantly better than the other existing state-of-the-art approaches.     

SocialSearch + : enriching social network with web evidences

This paper introduces the problem of searching for social network accounts, e.g., Twitter accounts, with the rich information available on the Web, e.g., people names, attributes, and relationships to other people. For this purpose, we need to map Twitter accounts with Web entities. However, existing solutions building upon naive textual matching inevitably suffer low precision due to false positives (e.g., fake impersonator accounts) and false negatives (e.g., accounts using nicknames). To overcome these limitations, we leverage “relational” evidences extracted from the Web corpus. We consider two types of evidence resources—First, web-scale entity relationship graphs, extracted from name co-occurrences crawled from the Web. This co-occurrence relationship can be interpreted as an “implicit” counterpart of Twitter follower relationships. Second, web-scale relational repositories, such as Freebase with complementary strength. Using both textual and relational features obtained from these resources, we learn a ranking function aggregating these features for the accurate ordering of candidate matches. Another key contribution of this paper is to formulate confidence scoring as a separate problem from relevance ranking. A baseline approach is to use the relevance of the top match itself as the confidence score. In contrast, we train a separate classifier, using not only the top relevance score but also various statistical features extracted from the relevance scores of all candidates, and empirically validate that our approach outperforms the baseline approach. We evaluate our proposed system using real-life internet-scale entity-relationship and social network graphs.     

Event assigning based on hierarchical features and enhanced association for Chinese mayor's hotline

Nowadays, manual event assignment for Chinese mayor's hotline is still a problem of low efficiency. In this paper, we propose a computer-aided event assignment method based on hierarchical features and enhanced association. First, hierarchical features of hotline events are extracted to obtain event encoding vectors. Second, the fine-tuned RoBERTa2RoBERTa model is used to encode the “sanding” responsibility texts of Chinese local departments. Third, an association enhanced attention (AEA) mechanism is proposed to capture the correlation information of the “event-sanding” splicing vectors for the sake of obtaining matching results of “event-sanding,” and the matching results are input into the classifier. Finally, the assignment department for is obtained by a department selection module. Experimental results show that our method can achieve better performance compared with several baseline methods on HEAD (a dataset we construct independently). The ablation experiments also demonstrate the validity of each key module in our method.     

Time-aware conversion prediction

The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The “conversion” here refers to actually a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product’s actual conversion period and the application’s target conversion period has been the subtle culprit compromising many existing recommendation algorithms.The challenging question: what products should be recommended for a given time period to maximize conversion—is what has motivated us in this paper to propose a rank-based time-aware conversion prediction model (rTCP), which considers both recommendation relevance and conversion time. We adopt lifetime models in survival analysis to model the conversion time and personalize the temporal prediction by incorporating context information such as user preference. A novel mixture lifetime model is proposed to further accommodate the complexity of conversion intervals. Experimental results on two real-world data sets illustrate the high goodness of fit of our proposed model rTCP and demonstrate its effectiveness in time-aware conversion rate prediction for advertising and product recommendation.     

基于Web日志挖掘的个性化网络广告模型

网络广告的飞速发展使得广告的效果越来越受重视。通过研究如何从Web日志中挖掘出用户对于网络广告的交互行为,设计出个性化网络广告系统。对提高网络广告平台的效益,增强网络广告效果具有重要的意义。     

User behavior modeling for better Web search ranking

Modern search engines record user interactions and use them to improve search quality. In particular, user click-through has been successfully used to improve clickthrough rate (CTR), Web search ranking, and query recommendations and suggestions. Although click-through logs can provide implicit feedback of users’ click preferences, deriving accurate absolute relevance judgments is difficult because of the existence of click noises and behavior biases. Previous studies showed that user clicking behaviors are biased toward many aspects such as “position” (user’s attention decreases from top to bottom) and “trust” (Web site reputations will affect user’s judgment). To address these problems, researchers have proposed several behavior models (usually referred to as click models) to describe users? practical browsing behaviors and to obtain an unbiased estimation of result relevance. In this study, we review recent efforts to construct click models for better search ranking and propose a novel convolutional neural network architecture for building click models. Compared to traditional click models, our model not only considers user behavior assumptions as input signals but also uses the content and context information of search engine result pages. In addition, our model uses parameters from traditional click models to restrict the meaning of some outputs in our model’s hidden layer. Experimental results show that the proposed model can achieve considerable improvement over state-of-the-art click models based on the evaluation metric of click perplexity.     

An intelligent Internet information delivery system to evaluate site preferences

Due to the continual growth of the popularity of the Internet, commercial as well as industrial companies have been advertising their products and services via the Web, resulting in a drastic increase in the number of Web sites. With a huge amount of information available on various Web sites, it is important that the relevant and useful information favored by individual visitors is delivered to the destinations in a timely manner. The two traditional approaches for sorting web information including search engines and hierarchical indices require specific input by the visitors who may not have any specific favorite sites in mind. In most cases, site surfers are just “window-shopping” on the Internet, looking for “exciting” things. This paper proposes the development of an Intelligent Internet Information Delivery System (IIIDS) which is characterized by its machine learning capability based on the data of site spots “movements” by the users within the Web pages and then evaluates the site preferences of the relevant users by means of fuzzy logic principle. The development of IIIDS and the test of a prototype to evaluate its feasibility are covered in this paper.     

手机广告推荐中的用户兴趣建模研究*

为了增强基于WAP网页的手机广告推荐中用户建模的准确性,并对"非邀"式广告推荐中脱离用户兴趣试探性推荐进行修正,针对手机广告推荐中手机屏幕小、用户注意力集中等特点,根据用户对广告的访问历史和操作模式建立其广告兴趣模型和非兴趣模型,同时分析用户网页访问模式探测其网页兴趣度,在此基础上建立用户综合兴趣模型。分别采用基于网页兴趣模型、基于广告兴趣模型和基于用户综合兴趣模型进行广告推荐,随着样本空间增大,综合兴趣模型的查准率明显优于另两者。实验验证了用户综合兴趣模型在手机广告推荐中的有效性和优越性。     

物联网环境下基于情境的语义Web服务选择

当前Web服务海量增加，物联网应用技术快速发展、不断普及，而现有的Web服务选择算法低效、用户匹配度低。针对该问题提出一种物联网环境下基于情境的语义Web服务选择方法。该方法应用QoS参数的无量纲化与语义Web服务动态选择方式，将物联网环境下服务与语义Web服务相结合，并根据用户需求针对QoS选择最优的服务集。实验表明，该方法能有效地提高用户服务动态选择的成功率。     

Wikipedia跨语言链接发现中的锚文本译项选择

Wikipedia跨语言链接发现主要研究从源语言Wikipedia文章中自动识别与主题相关的锚文本,并为锚文本推荐一组相关的目标语言链接。该研究涉及三个关键问题: 锚文本识别、锚文本翻译和目标链接发现。在锚文本翻译中,一个锚文本可能存在多个目标译项,如果其译项选择有误,将会直接影响目标链接发现中的链接推荐的准确性。为此,该文提出了一种基于上下文的锚文本译项选择方法,使用基于逐点互信息投票的方式确定锚文本的译项。 对中英文Wikipedia中的人名、术语以及缩略语的译项选择进行测试,实验表明该方法取得了较好的效果。     

Wikiometrics: a Wikipedia based ranking system

We present a new concept—Wikiometrics—the derivation of metrics and indicators from Wikipedia. Wikipedia provides an accurate representation of the real world due to its size, structure, editing policy and popularity. We demonstrate an innovative “mining” methodology, where different elements of Wikipedia – content, structure, editorial actions and reader reviews – are used to rank items in a manner which is by no means inferior to rankings produced by experts or other methods. We test our proposed method by applying it to two real-world ranking problems: top world universities and academic journals. Our proposed ranking methods were compared to leading and widely accepted benchmarks, and were found to be extremely correlative but with the advantage of the data being publically available.     

Trust beyond reputation: A computational trust model based on stereotypes

Models of computational trust support users in taking decisions. They are commonly used to guide users’ judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger’s actions in absence of the knowledge of such behavioral history, we often use our “instinct”—essentially stereotypes developed from our past interactions with other “similar” persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger’s profile. Since stereotypes are formed locally, recommendations stem from the trustor’s own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information.     

基于细粒度标签的在线视频广告投放机制研究

随着互联网的发展,对精彩视频点进行标注、评论和分享成为趋势.这类群体智慧信息的有效利用将有助于提升视频广告的投放效果.首先将用户提供的细粒度视频标签收集起来,通过视频时间轴加权计算生成视频热点,进而利用视频热点描述信息基于分类匹配的思想来选取广告,最后找出视频热点内用户对视频关注度下降幅度最大的时间点投放广告.实验证明,在数量为百万级的视频集合中,该方法选取的广告与视频的相关性达到85%左右.用户在广告播放过程中关闭广告的概率小于10%.与目前广泛应用的广告投放方式相比,广告的平均播放时间能提升21.5%,广告点击率能从0.65%提高至0.73%.