OpenRaiser/Intern-Atlas huggingface dataset:
An empirical study of reserve price optimisation in real-time bidding
Authors: Shuai Yuan, Jun Wang, Bowei Chen, Peter Mason, Sam Seljan
Venue: Conference KDD 2014
Year: 2014

In this paper, we report the first empirical study and live test of the reserve price optimisation problem in the context of Real-Time Bidding (RTB) display advertising from an operational environment. A reserve price is the minimum that the auctioneer would accept from bidders in auctions, and in a second price auction it could potentially uplift the auctioneer's revenue by charging winners the reserve price instead of the second highest bids. As such it has been used for sponsored search and been well studied in that context. However, comparing with sponsored search and contextual advertising, this problem in the RTB context is less understood yet more critical for publishers because 1) bidders have to submit a bid for each individual impression, which mostly is associated with user data that is subject to change over time. This, coupled with practical constraints such as the budget, campaigns' life time, etc. makes the theoretical result from optimal auction theory not necessarily applicable and a further empirical study is required to confirm its optimality from the real-world system; 2) in RTB an advertiser is facing nearly unlimited supply and the auction is almost done in "last second", which encourages spending less on the high cost ad placements. This could imply the loss of bid volume over time if a correct reserve price is not in place. In this paper we empirically examine several commonly adopted algorithms for setting up a reserve price. We report our results of a large scale online experiment in a production platform. The results suggest the our proposed game theory based OneShot algorithm performed the best and the superiority is significant in most cases.
The Matrix Stick-breaking Process for Flexible Multi-task Learning
Authors: Ya Xue, David B. Dunson, Lawrence Carin
Venue: Conference ICML 2007
Year: 2007

In multi-task learning our goal is to design regression or classification models for each of the tasks and appropriately share information between tasks. A Dirichlet process (DP) prior can be used to encourage task clustering. However, the DP prior does not allow local clustering of tasks with respect to a subset of the feature vector without making independence assumptions. Motivated by this problem, we develop a new multitask-learning prior, termed the matrix stick-breaking process (MSBP), which encourages cross-task sharing of data. However, the MSBP allows separate clustering and borrowing of information for the different feature components. This is important when tasks are more closely related for certain features than for others. Bayesian inference proceeds by a Gibbs sampling algorithm and the approach is illustrated using a simulated example and a multi-national application.
Representing document as dependency graph for document clustering
Authors: Yujing Wang, Xiaochuan Ni, Jian-Tao Sun, Yunhai Tong, Zheng Chen
Venue: Conference CIKM 2011
Year: 2011

In traditional clustering methods, a document is often represented as "bag of words" (in BOW model) or n-grams (in suffix tree document model) without considering the natural language relationships between the words. In this paper, we propose a novel approach DGDC (Dependency Graph-based Document Clustering algorithm) to address this issue. In our algorithm, each document is represented as a dependency graph where the nodes correspond to words which can be seen as meta-descriptions of the document; whereas the edges stand for the relations between pairs of words. A new similarity measure is proposed to compute the pairwise similarity of documents based on their corresponding dependency graphs. By applying the new similarity measure in the Group-average Agglomerative Hierarchial Clustering (GAHC) algorithm, the final clusters of documents can be obtained. The experiments were carried out on five public document datasets. The empirical results have indicated that the DGDC algorithm can achieve better performance in document clustering tasks compared with other approaches based on the BOW model and suffix tree document model.
Crossfire: Cross media joint friend and item recommendations
Authors: Kai Shu, Suhang Wang, Jiliang Tang, Yilin Wang, Huan Liu
Venue: Conference WSDM 2018
Year: 2018

Friend and item recommendation on a social media site is an important task, which not only brings conveniences to users but also benefits platform providers. However, recommendation for newly launched social media sites is challenging because they often lack user historical data and encounter data sparsity and cold-start problem. Thus, it is important to exploit auxiliary information to help improve recommendation performances on these sites. Existing approaches try to utilize the knowledge transferred from other mature sites, which often require overlapped users or similar items to ensure an effective knowledge transfer. However, these assumptions may not hold in practice because 1) Overlapped user set is often unavailable and costly to identify due to the heterogeneous user profile, content and network data, and 2) Different schemes to show item attributes across sites cause the attribute values inconsistent, incomplete, and noisy. Thus, how to transfer knowledge when no direct bridge is given between two social media sites remains a challenge. In addition, another auxiliary information we can exploit is the mutual benefit between social relationships and rating preferences within the platform. User-user relationships are widely used as side information to improve item recommendation, whereas how to exploit user-item interactions for friend recommendation is rather limited. To tackle these challenges, we propose aCross media jointF riend andI temRe commendation framework (CrossFire ), which can capture both 1) cross-platform knowledge transfer, and 2) within-platform correlations among user-user relations and user-item interactions. Empirical results on real-world datasets demonstrate the effectiveness of the proposed framework.
Joint Topic-Semantic-aware Social Recommendation for Online Voting
Authors: Hongwei Wang, Jia Wang, Miao Zhao, Jiannong Cao, Minyi Guo
Venue: Conference CIKM 2017
Year: 2017

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.
Personalizing web search results by reading level
Authors: Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Sebastian de la Chica, David A. Sontag
Venue: Conference CIKM 2011
Year: 2011

Traditionally, search engines have ignored the reading difficulty of documents and the reading proficiency of users in computing a document ranking. This is one reason why Web search engines do a poor job of serving an important segment of the population: children. While there are many important problems in interface design, content filtering, and results presentation related to addressing children's search needs, perhaps the most fundamental challenge is simply that of providing relevant results at the right level of reading difficulty. At the opposite end of the proficiency spectrum, it may also be valuable for technical users to find more advanced material or to filter out material at lower levels of difficulty, such as tutorials and introductory texts. We show how reading level can provide a valuable new relevance signal for both general and personalized Web search. We describe models and algorithms to address the three key problems in improving relevance for search using reading difficulty: estimating user proficiency, estimating result difficulty, and re-ranking based on the difference between user and result reading level profiles. We evaluate our methods on a large volume of Web query traffic and provide a large-scale log analysis that highlights the importance of finding results at an appropriate reading level for the user.
Probabilistic models for personalizing web search
Authors: David A. Sontag, Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. White, Susan T. Dumais, Bodo Billerbeck
Venue: Conference WSDM 2012
Year: 2012

We present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms using either direct human relevance judgments or indirect judgments obtained from click-through data from millions of users. The rankings are thus optimized to this generic population of users, not to any specific user. We propose a generative model of relevance which can be used to infer the relevance of a document to a specific user for a search query. The user-specific parameters of this generative model constitute a compact user profile. We show how to learn these profiles from a user's long-term search history. Our algorithm for computing the personalized ranking is simple and has little computational overhead. We evaluate our personalization approach using historical search data from thousands of users of a major Web search engine. Our findings demonstrate gains in retrieval performance for queries with high ambiguity, with particularly large improvements for acronym queries.
Cleansing Wikipedia Categories Using Centrality
Authors: Paolo Boldi, Corrado Monti
Venue: Conference WWW 2016
Year: 2016

We propose a novel general technique aimed at pruning and cleansing the Wikipedia category hierarchy, with a tunable level of aggregation. Our approach is endogenous, since it does not use any information coming from Wikipedia articles, but it is based solely on the user-generated (noisy) Wikipedia category folksonomy itself. We show how the proposed techniques can help reduce the level of noise in the hierarchy and discuss how alternative centrality measures can differently impact on the result.
Leveraging Fine-Grained Wikipedia Categories for Entity Search
Authors: Denghao Ma, Yueguo Chen, Kevin Chen-Chuan Chang, Xiaoyong Du, Chuanfei Xu, Yi Chang
Venue: Conference WWW 2018
Year: 2018

Ad-hoc entity search, which is to retrieve a ranked list of relevant entities in response to a query of natural language question, has been widely studied. It has been shown that category matching of entities, especially when matching to fine-grained entity types/categories, is critical to the performance of entity search. However, the potentials of the fine-grained Wikipedia entity categories, has not been well exploited by existing studies. Based on the observation of how people describe entities of a specific type, we propose a headword-and-modifier model to deeply interpret both queries and fine-grained entity types/categories. Probabilistic generative models are designed to effectively estimate the relevance of headwords and modifiers as a pattern-based matching problem, taking the Wikipedia type taxonomy as an important input to address the ad-hoc representations of concepts/entities in queries. Extensive experimental results on three widely-used test sets: INEX-XER 2009, SemSearch-LS and TREC-Entity, show that our method achieves a significant improvement of the entity search performance over the state-of-the-art methods.
Ontology Evaluation Using Wikipedia Categories for Browsing
Authors: Jonathan Yu, James A. Thom, Audrey M. Tam
Venue: Conference CIKM 2007
Year: 2007

Ontology evaluation is a maturing discipline with methodologies and measures being developed and proposed. However, evaluation methods that have been proposed have not been applied to specific examples. In this paper, we present the state-of-the-art in ontology evaluation - current methodologies, criteria and measures, analyse appropriate evaluations that are important to our application - browsing in Wikipedia, and apply these evaluations in the context of ontologies with varied properties. Specifically, we seek to evaluate ontologies based on categories found in Wikipedia.
EntiTables: Smart Assistance for Entity- Focused Tables
Authors: Shuo Zhang, Krisztian Balog
Venue: Conference SIGIR 2017
Year: 2017

Tables are among the most powerful and practical tools for organizing and working with data. Our motivation is to equip spreadsheet programs with smart assistance capabilities. We concentrate on one particular family of tables, namely, tables with an entity focus. We introduce and focus on two specific tasks: populating rows with additional instances (entities) and populating columns with new headings. We develop generative probabilistic models for both tasks. For estimating the components of these models, we consider a knowledge base as well as a large table corpus. Our experimental evaluation simulates the various stages of the user entering content into an actual table. A detailed analysis of the results shows that the models' components are complimentary and that our methods outperform existing approaches from the literature.
Auto-completion for Data Cells in Relational Tables
Authors: Shuo Zhang, Krisztian Balog
Venue: Conference CIKM 2019
Year: 2019

We address the task of auto-completing data cells in relational tables. Such tables describe entities (in rows) with their attributes (in columns). We present the CellAutoComplete framework to tackle several novel aspects of this problem, including: (i) enabling a cell to have multiple, possibly conflicting values, (ii) supplementing the predicted values with supporting evidence, (iii) combining evidence from multiple sources, and (iv) handling the case where a cell should be left empty. Our framework makes use of a large table corpus and a knowledge base as data sources, and consists of preprocessing, candidate value finding, and value ranking components. Using a purpose-built test collection, we show that our approach is 40% more effective than the best baseline.
An extended relevance model for session search
Authors: Nir Levine, Haggai Roitman, Doron Cohen
Venue: Conference SIGIR 2017
Year: 2017

The session search task aims at best serving the user's information need given her previous search behavior during the session. We propose an extended relevance model that captures the user's dynamic information need in the session. Our relevance modelling approach is directly driven by the user's query reformulation (change) decisions and the estimate of how much the user's search behavior affects such decisions. Overall, we demonstrate that, the proposed approach significantly boosts session search performance.
Personalization of search results using interaction behaviors in search sessions
Authors: Chang Liu, Nicholas J. Belkin, Michael J. Cole
Venue: Conference SIGIR 2012
Year: 2012

Personalization of search results offers the potential for significant improvement in information retrieval performance. User interactions with the system and documents during information-seeking sessions provide a wealth of information about user preferences and their task goals. In this paper, we propose methods for analyzing and modeling user search behavior in search sessions to predict document usefulness and then using information to personalize search results. We generate prediction models of document usefulness from behavior data collected in a controlled lab experiment with 32 participants, each completing uncontrolled searching for 4 tasks in the Web. The generated models are then tested with another data set of user search sessions in radically different search tasks and constrains. The documents predicted useful and not useful by the models are used to modify the queries in each search session using a standard relevance feedback technique. The results show that application of the models led to consistently improved performance over a baseline that did not take account of user interaction information. These findings have implications for designing systems for personalized search and improving user search experience.
Fighting search engine amnesia: Reranking repeated results
Authors: Milad Shokouhi, Ryen W. White, Paul N. Bennett, Filip Radlinski
Venue: Conference SIGIR 2013
Year: 2013

Web search engines frequently show the same documents repeatedly for different queries within the same search session, in essence forgetting when the same documents were already shown to users. Depending on previous user interaction with the repeated results, and the details of the session, we show that sometimes the repeated results should be promoted, while some other times they should be demoted.
Tracking the Impact of Fact Deletions on Knowledge Graph Queries Using Provenance Polynomials
Authors: Garima Gaur, Srikanta J. Bedathur, Arnab Bhattacharya
Venue: Conference CIKM 2017
Year: 2017

Critical business applications in domains ranging from technical support to healthcare increasingly rely on large-scale, automatically constructed knowledge graphs. These applications use the results of complex queries over knowledge graphs in order to help users in taking crucial decisions such as which drug to administer, or whether certain actions are compliant with all the regulatory requirements and so on. However, these knowledge graphs constantly evolve, and the newer versions may adversely impact the results of queries that the previously taken business decisions were based on. We propose a framework based on provenance polynomials to track the impact of knowledge graph changes on arbitrary SPARQL query results. Focusing on the deletion of facts, we show how to efficiently determine the queries impacted by the change, develop ways to incrementally maintain these polynomials, and present an efficient implementation on top of RDF graph databases. Our experimental evaluation over large-scale RDF/SPARQL benchmarks show the effectiveness of our proposal.
How and Why is an Answer (Still) Correct? Maintaining Provenance in Dynamic Knowledge Graphs
Authors: Garima Gaur, Arnab Bhattacharya, Srikanta Bedathur
Venue: Conference CIKM 2020
Year: 2020

Knowledge graphs (KGs) have increasingly become the backbone of many critical knowledge-centric applications. Most large-scale KGs used in practice are automatically constructed based on an ensemble of extraction techniques applied over diverse data sources. Therefore, it is important to establish the provenance of results for a query to determine how these were computed. Provenance is shown to be useful for assigning confidence scores to the results, for debugging the KG generation itself, and for providing answer explanations. In many such applications, certain queries are registered as standing queries since their answers are needed often. However, KGs keep continuously changing due to reasons such as changes in the source data, improvements to the extraction techniques, refinement/enrichment of information, and so on. This brings us to the issue of efficiently maintaining the provenance polynomials of complex graph pattern queries for dynamic and large KGs instead of having to recompute them from scratch each time the KG is updated. Addressing these issues, we present HUKA which uses provenance polynomials for tracking the derivation of query results over knowledge graphs by encoding the edges involved in generating the answer. More importantly, HUKA also maintains these provenance polynomials in the face of updates---insertions as well as deletions of facts---to the underlying KG. Experimental results over large real-world KGs such as YAGO and DBpedia with various benchmark SPARQL query workloads reveals that HUKA can be almost 50 times faster than existing systems for provenance computation on dynamic KGs.
TripleProv: Efficient Processing of Lineage Queries in a Native RDF Store
Authors: Marcin Wylot, Philippe Cudré-Mauroux, Paul Groth
Venue: Conference WWW 2014
Year: 2014

Given the heterogeneity of the data one can find on the Linked Data cloud, being able to trace back the provenance of query results is rapidly becoming a must-have feature of RDF systems. While provenance models have been extensively discussed in recent years, little attention has been given to the efficient implementation of provenance-enabled queries inside data stores. This paper introduces TripleProv: a new system extending a native RDF store to efficiently handle such queries. TripleProv implements two different storage models to physically co-locate lineage and instance data, and for each of them implements algorithms for tracing provenance at two granularity levels. In the following, we present the overall architecture of our system, its different lineage storage models, and the various query execution strategies we have implemented to efficiently answer provenance-enabled queries. In addition, we present the results of a comprehensive empirical evaluation of our system over two different datasets and workloads.
What makes a link successful on wikipedia?
Authors: Dimitar Dimitrov, Philipp Singer, Florian Lemmerich, Markus Strohmaier
Venue: Conference WWW 2017
Year: 2017

While a plethora of hypertext links exist on the Web, only a small amount of them are regularly clicked. Starting from this observation, we set out to study large-scale click data from Wikipedia in order to understand what makes a link successful. We systematically analyze effects of link properties on the popularity of links. By utilizing mixed-effects hurdle models supplemented with descriptive insights, we find evidence of user preference towards links leading to the periphery of the network, towards links leading to semantically similar articles, and towards links in the top and left-side of the screen. We integrate these findings as Bayesian priors into a navigational Markov chain model and by doing so successfully improve the model fits. We further adapt and improve the well-known classic PageRank algorithm that assumes random navigation by accounting for observed navigational preferences of users in a weighted variation. This work facilitates understanding navigational click behavior and thus can contribute to improving link structures and algorithms utilizing these structures.