UEO '13Pub Date : 2013-11-01DOI: 10.1145/2512875.2517150
D. Agarwal
{"title":"Recommending items to users: an explore/exploit perspective","authors":"D. Agarwal","doi":"10.1145/2512875.2517150","DOIUrl":"https://doi.org/10.1145/2512875.2517150","url":null,"abstract":"Content recommendation is the science of algorithmically recommending engaging content to users interacting with the web in various contexts. Information on WWW is consumed in various forms that may range from web search results displayed in response to an explicit query on one hand, to unsolicited advertisements that attract user attention on the other extreme. Between these two extremes, there is a continuum of contexts where users visit websites in the browse mode without a specific task in mind but expecting a certain mix of content. Examples include visits to sites like news, sports, videos, blog pages and so on. Traditionally, content in such contexts have been served through editorial oversight to ensure high quality and adhere to typical mix associated with a website. Such a process is not scalable in large scale modern web applications, it is also difficult to efficiently incorporate various measurable metrics like clicks and downstream engagement in recommending content. Further, deeply personalized content recommendation that is desirable in various applications is simply not possible through such a manual process. This clearly underscores the importance of using algorithmic methods to optimize content that we discuss in this review article. We begin by discussing why content recommendation is a key technology to enable engaging user interactions on WWW in many contexts. We then provide a mathematical","PeriodicalId":129068,"journal":{"name":"UEO '13","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130365772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
UEO '13Pub Date : 2013-11-01DOI: 10.1145/2512875.2512879
Ariyam Das, Harish Siddapura Ranganath
{"title":"When web personalization misleads bucket testing","authors":"Ariyam Das, Harish Siddapura Ranganath","doi":"10.1145/2512875.2512879","DOIUrl":"https://doi.org/10.1145/2512875.2512879","url":null,"abstract":"Online service platforms like search engines, news websites, information portal sites and others offer highly personalized content to the users based on their interest and taste. At the same time, these online sites, with large audiences, frequently use bucket testing to evaluate the impact of a new feature or service on a small subset of its users before releasing it to the entire user population. In general, web personalization leads to an improved user engagement for the sites, but it can also interfere and adversely impact the online bucket testing experiments. In this work, we show empirically through real experiments conducted on Yahoo pages that how personalization can mislead to erroneous interpretation of the bucket testing results. We also present a novel algorithmic framework that addresses this challenge and draws a more accurate inference from the bucket testing results by factoring in the personalization experience of the users. The effectiveness of our algorithm is demonstrated through experiments conducted on Yahoo pages.","PeriodicalId":129068,"journal":{"name":"UEO '13","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121324425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
UEO '13Pub Date : 2013-11-01DOI: 10.1145/2512875.2512880
Bhautik Patel, Ashish V. Tendulkar, Sutanu Chakraborti
{"title":"Towards selective user specific query expansion","authors":"Bhautik Patel, Ashish V. Tendulkar, Sutanu Chakraborti","doi":"10.1145/2512875.2512880","DOIUrl":"https://doi.org/10.1145/2512875.2512880","url":null,"abstract":"Queries submitted to a search engine are usually short and ambiguous. Query expansion is an effective way to resolve the ambiguity of a query. We attempt to make a consolidated study of the effectiveness of query logs in this regard. We report empirical findings on the effect of personalization over an anonymous query log. We have designed a novel evaluation method to quantify improvements due to personalization. In the second part of our work, we present a novel approach to intelligently detect queries whose expansions potentially lead to improvements in retrieval. Empirical results obtained using this approach clearly show significant improvements over indiscriminate query expansion.","PeriodicalId":129068,"journal":{"name":"UEO '13","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133918529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
UEO '13Pub Date : 2013-11-01DOI: 10.1145/2512875.2512877
Janette Lehmann, M. Lalmas, R. Baeza-Yates, E. Yom-Tov
{"title":"Networked user engagement","authors":"Janette Lehmann, M. Lalmas, R. Baeza-Yates, E. Yom-Tov","doi":"10.1145/2512875.2512877","DOIUrl":"https://doi.org/10.1145/2512875.2512877","url":null,"abstract":"Online providers frequently offer a variety of services, ranging from news to mail. These providers endeavour to keep users accessing and interacting with the sites offering these services, that is to engage users by spending time on these sites and returning regularly to them. The standard approach to evaluate engagement with a site is by measuring engagement metrics of each site separately. However, when assessing engagement within a network of sites, it is crucial to take into account the traffic between sites. This paper proposes a methodology for studying networked used engagement. We represent sites (nodes) and user traffic (edges) between them as a network, and apply complex network metrics to study networked user engagement. We demonstrate the value of these metrics when applied to 728 services offered by Yahoo!~with a sample of 2M users and a total of 25M online sessions.","PeriodicalId":129068,"journal":{"name":"UEO '13","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121205677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
UEO '13Pub Date : 2013-11-01DOI: 10.1145/2512875.2512881
Balaji Vasan Srinivasan, Anandhavelu Natarajan, R. Sinha, Vineet Gupta, S. Revankar, Balaraman Ravindran
{"title":"Will your facebook post be engaging?","authors":"Balaji Vasan Srinivasan, Anandhavelu Natarajan, R. Sinha, Vineet Gupta, S. Revankar, Balaraman Ravindran","doi":"10.1145/2512875.2512881","DOIUrl":"https://doi.org/10.1145/2512875.2512881","url":null,"abstract":"Social media has become the ideal platform for promotional activities of organizations. However, due to the volatility of social media, the wrong message posted at the wrong time can result in significant damage to hard-built brand image. This calls for a mechanism to gauge the reactions a post will evoke from a given social community. The community can vary from customers of a particular brand to brand loyalists interacting through its social pages (for example, on Facebook). In this paper, we focus on learning the community's reaction from past posts and providing a predictive model for gauging the reaction of the community before the post is published. This helps the marketer take better-informed decisions. Short-text posts in social media leads to a sparse feature space, we propose additional meta-features that improve reaction modeling. Given the feature representation, we discuss the possibility of casting the underlying problem under different paradigms - classification, regression and learning to rank. We study the performances of each of these paradigms on real data from Facebook. We will discuss the challenges involved, and ways to mitigate them, in addition to our observations, results and insights.","PeriodicalId":129068,"journal":{"name":"UEO '13","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116321779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
UEO '13Pub Date : 2013-11-01DOI: 10.1145/2512875.2512878
Marina Danilevsky, Eunyee Koh
{"title":"Information graph model and application to online advertising","authors":"Marina Danilevsky, Eunyee Koh","doi":"10.1145/2512875.2512878","DOIUrl":"https://doi.org/10.1145/2512875.2512878","url":null,"abstract":"We present an algorithm which adapts a graph-based ranking model to the context of the problem of improving the process of serving advertisements to users. We transform the ad-based clickstream data into a heterogeneous graph model which respects differences in feature types (e.g. geolocation features, or browser-history features). The heterogeneous network model generates meaningful rankings of features which are predictive for each ad, as demonstrated by our classifier's performance. We also discuss how, in addition to serving as the basis for a classifier, this model may also provide an informative view of the data, which is not possible with black-box approaches, and which therefore makes it very suitable to the problem space of targeted ad serving.","PeriodicalId":129068,"journal":{"name":"UEO '13","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127949121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
UEO '13Pub Date : 2013-11-01DOI: 10.1145/2512875.2517149
Ron Kohavi
{"title":"Online controlled experiments: introduction, insights, scaling, and humbling statistics","authors":"Ron Kohavi","doi":"10.1145/2512875.2517149","DOIUrl":"https://doi.org/10.1145/2512875.2517149","url":null,"abstract":"The web provides an unprecedented opportunity to accelerate innovation by evaluating ideas quickly and accurately using controlled experiments (e.g., A/B tests and their generalizations). From front-end user-interface changes to backend algorithms, online controlled experiments are now utilized to make data-driven decisions at many other companies. While the theory of a controlled experiment is simple, running online controlled experiments at scale - hundreds of concurrent experiments on a given day at Bing has taught us many lessons. We provide an introduction, share real examples, key insights, cultural challenges, scaling challenges, and humbling statistics.","PeriodicalId":129068,"journal":{"name":"UEO '13","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122137691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
UEO '13Pub Date : 2013-11-01DOI: 10.1145/2512875.2512876
Jia Huang, Xiaohua Hu
{"title":"Information passing in online recommendation","authors":"Jia Huang, Xiaohua Hu","doi":"10.1145/2512875.2512876","DOIUrl":"https://doi.org/10.1145/2512875.2512876","url":null,"abstract":"In this paper, we analyze a recommendation network with over 4,000 users and half a million books. There are two types of edges in this network, \"read\" relations between users and books, and following relations between users. We first investigate in general, if one's followees' recommendations have impacts on one's decision. We then analyze the correlation between one's influence and her centrality in the network. Finally, we study how effective a recommendation is as one sends or receives more and more recommendations. Results show that although in general, one's followee do have an impact over her decision, such influence is not correlated with the followee's centrality. As one receives more and more recommendations for a product, it is more likely that she will accept it. However, there is a saturate point over which more recommendations will have no further impact. As one sends out more and more recommendations, the probabilities that these recommendations get accepted become larger and larger.","PeriodicalId":129068,"journal":{"name":"UEO '13","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132186472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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