OpenRaiser/Intern-Atlas huggingface dataset:
Modeling the temporal dynamics of social rating networks using bidirectional effects of social relations and rating patterns
Authors: Mohsen Jamali, Gholamreza Haffari, Martin Ester
Venue: WWW
Year: 2011

A social rating network (SRN) is a social network in which edges represent social relationships and users (nodes) express ratings on some of the given items. Such networks play an increasingly important role in reviewing websites such as Epinions.com or online sharing websites like Flickr.com. In this paper, we first observe and analyze the temporal behavior of users in a social rating network, who express ratings and create social relations. Then, we model the temporal dynamics of an SRN based on our observations, using the bidirectional effects of ratings and social relations. While existing models for other types of social networks have captured some of the effects, our model is the first one to represent all four effects, i.e. social relations-on-ratings (social influence), social relations-on-social relations (transitivity), ratings-on-social relations (selection), and ratings-on-ratings (correlational influence). Existing works consider these effects as static and constant throughout the evolution of an SRN, however our observations reveal that these effects are actually dynamic. We propose a probabilistic generative model for SRNs, which models the strength and dynamics of each effect throughout the network evolution. This model can serve for the prediction of future links, ratings or community structures. Due to the sensitive nature of SRNs, another motivation for our work is the generation of synthetic SRN data sets for research purposes. Our experimental studies on two real life datasets (Epinions and Flickr) demonstrate that the proposed model produces social rating networks that agree with real world data on a comprehensive set of evaluation criteria.
The web of topics: discovering the topology of topic evolution in a corpus
Authors: Yookyung Jo, John E. Hopcroft, Carl Lagoze
Venue: WWW
Year: 2011

In this paper we study how to discover the evolution of topics over time in a time-stamped document collection. Our approach is uniquely designed to capture the rich topology of topic evolution inherent in the corpus. Instead of characterizing the evolving topics at fixed time points, we conceptually define a topic as a quantized unit of evolutionary change in content and discover topics with the time of their appearance in the corpus. Discovered topics are then connected to form a topic evolution graph using a measure derived from the underlying document network. Our approach allows inhomogeneous distribution of topics over time and does not impose any topological restriction in topic evolution graphs. We evaluate our algorithm on the ACM corpus.
Heat-seeking honeypots: design and experience
Authors: John P. John, Fang Yu, Yinglian Xie, Arvind Krishnamurthy, Martín Abadi
Venue: WWW
Year: 2011

Many malicious activities on the Web today make use of compromised Web servers, because these servers often have high pageranks and provide free resources. Attackers are therefore constantly searching for vulnerable servers. In this work, we aim to understand how attackers find, compromise, and misuse vulnerable servers. Specifically, we present heat-seeking honeypots that actively attract attackers, dynamically generate and deploy honeypot pages, then analyze logs to identify attack patterns.
Traffic characterization and internet usage in rural Africa
Authors: David L. Johnson, Veljko Pejovic, Elizabeth M. Belding, Gertjan van Stam
Venue: WWW
Year: 2011

While Internet connectivity has reached a significant part of the world’s population, those living in rural areas of the developing world are still largely disconnected. Recent efforts have provided Internet connectivity to a growing number of remote locations, yet Internet traffic demands cause many of these networks to fail to deliver basic quality of service needed for simple applications. For an in-depth investigation of the problem, we gather and analyze network traces from a rural wireless network in Macha, Zambia. We supplement our analysis with on-site interviews from Macha, Zambia and Dwesa, South Africa, another rural community that hosts a local wireless network. The results reveal that Internet traffic in rural Africa differs significantly from the developed world. We observe dominance of web-based traffic, as opposed to peer-to-peer traffic common in urban areas. Application-wise, online social networks are the most popular, while the majority of bandwidth is consumed by large operating system updates. Our analysis also uncovers numerous network anomalies, such as significant malware traffic. Finally, we find a strong feedback loop between network performance and user behavior. Based on our findings, we conclude with a discussion of new directions in network design that take into account both technical and social factors. Categories andSubject Descriptors
Ranked answer graph construction for keyword queries on RDF graphs without distance neighbourhood restriction
Authors: Parthasarathy K., P. Sreenivasa Kumar, Dominic Damien
Venue: WWW
Year: 2011

RDF and RDFS have recently become very popular as frameworks for representing data and meta-data in form of a domain description, respectively. RDF data can also be thought of as graph data. In this paper, we focus on keyword-based querying of RDF data. In the existing approaches for answering such keyword queries, keywords are mapped to nodes in the graph and their neighborhoods are explored to extract subgraph(s) of the data graph that contain(s) information relevant to the query. In order to restrict the computational effort, a fixed distance bound is used to define the neighborhoods of nodes. In this paper we present an elegant algorithm for keyword query processing on RDF data that does not assume such a fixed bound. The approach adopts a pruned exploration mechanism where closely related nodes are identified, subgraphs are pruned and joined using suitable hook nodes. The system dynamically manages the distance depending on the closeness between the keywords. The working of the algorithm is illustrated using a fragment of AIFB institute data represented as an RDF graph.
Ranking in context-aware recommender systems
Authors: Minsuk Kahng, Sangkeun Lee, Sang-goo Lee
Venue: WWW
Year: 2011

As context is acknowledged as an important factor that can affect users' preferences, many researchers have worked on improving the quality of recommender systems by utilizing users' context. However, incorporating context into recommender systems is not a simple task in that context can influence users' item preferences in various ways depending on the application. In this paper, we propose a novel method for context-aware recommendation, which incorporates several features into the ranking model. By decomposing a query, we propose several types of ranking features that reflect various contextual effects. In addition, we present a retrieval model for using these features, and adopt a learning to rank framework for combining proposed features. We evaluate our approach on two real-world datasets, and the experimental results show that our approach outperforms several baseline methods.
Ranking related entities for web search queries
Authors: Changsung Kang, Srinivas Vadrevu, Ruiqiang Zhang, Roelof van Zwol, Lluis Garcia Pueyo, Nicolas Torzec, Jianzhang He, Yi Chang
Venue: WWW
Year: 2011

Entity ranking is a recent paradigm that refers to retrieving and ranking related objects and entities from different structured sources in various scenarios. Entities typically have associated categories and relationships with other entities. In this work, we present an extensive analysis of Web-scale entity ranking, based on machine learned ranking models using an ensemble of pairwise preference models. Our proposed system for entity ranking uses structured knowledge bases, entity relationship graphs and user data to derive useful features to facilitate semantic search with entities directly within the learning to rank framework. The experimental results are validated on a large-scale graph containing millions of entities and hundreds of millions of entity relationships. We show that our proposed ranking solution clearly improves a simple user behavior based ranking model.
Pay as you browse: microcomputations as micropayments in web-based services
Authors: Ghassan Karame, Aurélien Francillon, Srdjan Capkun
Venue: WWW
Year: 2011

Currently, several online businesses deem that advertising revenues alone are not sufficient to generate profits and are therefore set to charge for online content. In this paper, we explore a complement to the current advertisement model; more specifically, we propose a micropayment model for non-specialized commodity web-services based on microcomputations. In our model, a user that wishes to access online content offered by a website does not need to register or pay to access the website; instead, he will accept to run microcomputations on behalf of the website in exchange for access to the content. These microcomputations can, for example, support ongoing computing projects that have clear social benefits (e.g., projects relating to HIV, dengue, cancer, etc.) or can contribute towards commercial computing projects. We argue that this micropayment model is economically and technically viable and that it can be integrated in existing distributed computing frameworks (e.g., the BOINC platform). We implement a preliminary prototype of a system based on our model through which we evaluate its performance and usability. Finally, we analyze the security and privacy of our proposal and we show that it ensures payment for the content while preserving the privacy of users.
A stochastic learning-to-rank algorithm and its application to contextual advertising
Authors: Maryam Karimzadehgan, Wei Li, Ruofei Zhang, Jianchang Mao
Venue: WWW
Year: 2011

This paper is concerned with the problem of learning a model to rank objects (Web pages, ads and etc.). We propose a framework where the ranking model is both optimized and evaluated using the same information retrieval measures such as Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The main difficulty in direct optimization of NDCG and MAP is that these measures depend on the rank of objects and are not differentiable. Most learning-to-rank methods that attempt to optimize NDCG or MAP approximate such measures so that they can be differentiable. In this paper, we propose a simple yet effective stochastic optimization algorithm to directly minimize any loss function, which can be defined on NDCG or MAP for the learning-to-rank problem. The algorithm employs Simulated Annealing along with Simplex method for its parameter search and finds the global optimal parameters. Experiment results using NDCG-Annealing algorithm, an instance of the proposed algorithm, on LETOR benchmark data sets show that the proposed algorithm is both effective and stable when compared to the baselines provided in LETOR 3.0. In addition, we applied the algorithm for ranking ads in contextual advertising. Our method has shown to significantly improve relevance in offline evaluation and business metrics in online tests in a real large-scale advertising serving system. To scale our computations, we parallelize the algorithm in a MapReduce framework running on Hadoop.
Anytime algorithm for QoS web service composition
Authors: Hyunyoung Kil, Wonhong Nam
Venue: WWW
Year: 2011

The QoS-aware web service composition (WSC) problem aims at the automatic construction of a composite web servicewiththeoptimalaccumulatedQoSvalue. Itis, however, intractable to solve the QoS-aware WSC problem for large scale instances, since the problem corresponds to a global optimization problem. In this paper, we propose a novel anytime algorithm for the QoS-aware WSC problem to identify composite web services with high quality much earlier than an optimal algorithm and the beam stack search [3].
A politeness recognition tool for Hindi: with special emphasis on online texts
Authors: Ritesh Kumar
Venue: WWW
Year: 2011

This paper gives an overview of a politeness recognition tool (PoRT) for Hindi that is currently under preparation. It describes the the kind of problems that need to be tackled with before developing the tool, the approach and the methodology that will be adopted for the development and testing of the tool, the current progress and the future plan to achieve this goal.
Smart news feeds for social networks using scalable joint latent factor models
Authors: Himabindu Lakkaraju, Angshu Rai, Srujana Merugu
Venue: WWW
Year: 2011

Social networks such as Facebook and Twitter offer a huge opportunity to tap the collective wisdom (both published and yet to be published) of all the participating users in order to address the information needs of individual users in a highly contextualized fashion using rich user-specific information. Realizing this opportunity, however, requires addressing two key limitations of current social networks: (a) difficulty in discovering relevant content beyond the immediate neighborhood, (b) lack of support for information filtering based on semantics, content source and linkage. We propose a scalable framework for constructing smart news feeds based on predicting user-post relevance using multiple signals such as text content and attributes of users and posts, and various user-user, post-post and user-post relations (e.g. friend, comment, author relations). Our solution comprises of two steps where the first step ensures scalability by selecting a small set of user-post dyads with potentially interesting interactions using inverted feature indexes. The second step models the interactions associated with the selected dyads via a joint latent factor model, which assumes that the user/post content and relationships can be effectively captured by a common latent representation of the users and posts. Experiments on a Facebook dataset using the proposed model lead to improved precision/recall on relevant posts indicating potential for constructing superior quality news feeds.
Milgram-routing in social networks
Authors: Silvio Lattanzi, Alessandro Panconesi, D. Sivakumar
Venue: WWW
Year: 2011

We demonstrate how a recent model of social networks ("Affiliation Networks", [21]) offers powerful cues in local routing within social networks, a theme made famous by sociologist Milgram's "six degrees of separation" experiments. This model posits the existence of an "interest space" that underlies a social network; we prove that in networks produced by this model, not only do short paths exist among all pairs of nodes but natural local routing algorithms can discover them effectively. Specifically, we show that local routing can discover paths of length O(log2 n) to targets chosen uniformly at random, and paths of length O(1) to targets chosen with probability proportional to their degrees. Experiments on the co-authorship graph derived from DBLP data confirm our theoretical results, and shed light into the power of one step of lookahead in routing algorithms for social networks.
Inverted index compression via online document routing
Authors: Gal Lavee, Ronny Lempel, Edo Liberty, Oren Somekh
Venue: WWW
Year: 2011

Modern search engines are expected to make documents searchable shortly after they appear on the ever changing Web. To satisfy this requirement, the Web is frequently crawled. Due to the sheer size of their indexes, search engines distribute the crawled documents among thousands of servers in a scheme called local index-partitioning, such that each server indexes only several million pages. To ensure documents from the same host (e.g., www.nytimes.com) are distributed uniformly over the servers, for load balancing purposes, random routing of documents to servers is common. To expedite the time documents become searchable after being crawled, documents may be simply appended to the existing index partitions. However, indexing by merely appending documents, results in larger index sizes since document reordering for index compactness is no longer performed. This, in turn, degrades search query processing performance which depends heavily on index sizes.
Rewriting queries on SPARQL views
Authors: Wangchao Le, Songyun Duan, Anastasios Kementsietsidis, Feifei Li, Min Wang
Venue: WWW
Year: 2011

The problem of answering SPARQL queries over virtual SPARQL views is commonly encountered in a number of settings, including while enforcing security policies to access RDF data, or when integrating RDF data from disparate sources. We approach this problem by rewriting SPARQL queries over the views to equivalent queries over the underlying RDF data, thus avoiding the costs entailed by view materialization and maintenance. We show that SPARQL query rewriting combines the most challenging aspects of rewriting for the relational and XML cases: like the relational case, SPARQL query rewriting requires synthesizing multiple views; like the XML case, the size of the rewritten query is exponential to the size of the query and the views. In this paper, we present the first native query rewriting algorithm for SPARQL. For an input SPARQL query over a set of virtual SPARQL views, the rewritten query resembles a union of conjunctive queries and can be of exponential size. We propose optimizations over the basic rewriting algorithm to (i) minimize each conjunctive query in the union; (ii) eliminate conjunctive queries with empty results from evaluation; and (iii) efficiently prune out big portions of the search space of empty rewritings. The experiments, performed on two RDF stores, show that our algorithms are scalable and independent of the underlying RDF stores. Furthermore, our optimizations have order of magnitude improvements over the basic rewriting algorithm in both the rewriting size and evaluation time.
Social media analytics: tracking, modeling and predicting the flow of information through networks
Authors: Jure Leskovec
Venue: WWW
Year: 2011

Online social media represent a fundamental shift of how information is being produced, transferred and consumed. User generated content in the form of blog posts, comments, and tweets establishes a connection between the producers and the consumers of information. Tracking the pulse of the social media outlets, enables companies to gain feedback and insight in how to improve and market products better. For consumers, the abundance of information and opinions from diverse sources helps them tap into the wisdom of crowds, to aid in making more informed decisions.
Here, there, and everywhere: correlated online behaviors can lead to overestimates of the effects of advertising
Authors: Randall A. Lewis, Justin M. Rao, David H. Reiley
Venue: WWW
Year: 2011

Measuring the causal effects of online advertising (adfx) on user behavior is important to the health of the WWW publishing industry. In this paper, using three controlled experiments, we show that observational data frequently lead to incorrect estimates of adfx. The reason, which we label "activity bias," comes from the surprising amount of time-based correlation between the myriad activities that users undertake online. In Experiment 1, users who are exposed to an ad on a given day are much more likely to engage in brand-relevant search queries as compared to their recent history for reasons that had nothing do with the advertisement. In Experiment 2, we show that activity bias occurs for page views across diverse websites. In Experiment 3, we track account sign-ups at a competitor's (of the advertiser) website and find that many more people sign-up on the day they saw an advertisement than on other days, but that the true "competitive effect" was minimal. In all three experiments, exposure to a campaign signals doing "more of everything" in given period of time, making it difficult to find a suitable "matched control" using prior behavior. In such cases, the "match" is fundamentally different from the exposed group, and we show how and why observational methods lead to a massive overestimate of adfx in such circumstances.
A demo search engine for products
Authors: Beibei Li, Anindya Ghose, Panagiotis G. Ipeirotis
Venue: WWW
Year: 2011

Most product search engines today build on models of relevance devised for information retrieval. However, the decision mechanism that underlies the process of buying a product is different than the process of locating relevant documents or objects. We propose a theory model for product search based on expected utility theory from economics. Specifically, we propose a ranking technique in which we rank highest the products that generate the highest surplus, after the purchase. We instantiate our research by building a demo search engine for hotels that takes into account consumer heterogeneous preferences, and also accounts for the varying hotel price. Moreover, we achieve this without explicitly asking the preferences or purchasing histories of individual consumers but by using aggregate demand data. This new ranking system is able to recommend consumers products with "best value for money" in a privacy-preserving manner. The demo is accessible at http://nyuhotels.appspot.com/
Hypergraph-based inductive learning for generating implicit key phrases
Authors: Decong Li, Sujian Li
Venue: WWW
Year: 2011

This paper presents a novel approach to generate implicit key phrases which are ignored in previous researches. Recent researches prefer to extract key phrases with semi-supervised transductive learning methods, which avoid the problem of training data. In this paper, based on a transductive learning method, we formulate the phrases in the document as a hypergraph and expand the hypergraph to include implicit phrases, which are ranked by an inductive learning approach. The highest ranked phrases are seen as implicit key phrases, and experimental results demonstrate the satisfactory performance of this approach.
Finding influential mediators in social networks
Authors: Cheng-Te Li, Shou-De Lin, Man-Kwan Shan
Venue: WWW
Year: 2011

Given a social network, who are the key players controlling the bottlenecks of influence propagation if some persons would like to activate specific individuals? In this paper, we tackle the problem of selecting a set of k mediator nodes as the influential gateways whose existence determines the activation probabilities of targeted nodes from some given seed nodes. We formally define the k-Mediators problem. To have an effective and efficient solution, we propose a three-step greedy method by considering the probabilistic influence and the structural connectivity on the pathways from sources to targets. To the best of our knowledge, this is the first work to consider the k-Mediators problem in networks. Experiments on the DBLP co-authorship graph show the effectiveness and efficiency of the proposed method.
Open and decentralized access across location-based services
Authors: Yiming Liu, Rui Yang, Erik Wilde
Venue: WWW
Year: 2011

Users now interact with multiple Location-Based Services (LBS) through a myriad set of location-aware devices and interfaces. However, current LBS tend to be centralized silos with ad-hoc APIs, which limits potential for information sharing and reuse. Further, LBS subscriptions and user experiences are not easily portable across devices. We propose a general architecture for providing open and decentralized access to LBS, based on Tiled Feeds - a RESTful protocol for access and interactions with LBS using feeds, and Feed Subscription Management (FSM) - a generalized feed-based service management protocol. We describe two client designs, and demonstrate how they enable standardized access to LBS services, promote information sharing and mashup creation, and offer service management across various types of location-enabled devices.