OpenRaiser/Intern-Atlas huggingface dataset:
Multi-agent event recognition
Authors: Somboon Hongeng, Ramakant Nevatia
Venue: Conference ICCV 2001
Year: 2001

This paper presents a new approach to recognizing multiagent events observed by a static camera. To track objects robustly, knowledge about the ground plane and the events is used. An event is considered as composed of action threads, each thread being executed by a single actor. A single thread of action is recognized from the characteristics of the trajectory and moving blob of the actor using Bayesian methods. A multi-agent event is represented by a number of action threads related by temporal constraints. Multi-agent events are recognized by propagating the constraints and likelihoods of event threads in a temporal logic network.
Towards the self-annotating web
Authors: Philipp Cimiano, Siegfried Handschuh, Steffen Staab
Venue: Conference WWW 2004
Year: 2004

The success of the Semantic Web depends on the availability of ontologies as well as on the proliferation of web pages annotated with metadata conforming to these ontologies. Thus, a crucial question is where to acquire these metadata from. In this paper wepropose PANKOW (Pattern-based Annotation through Knowledge on theWeb), a method which employs an unsupervised, pattern-based approach to categorize instances with regard to an ontology. The approach is evaluated against the manual annotations of two human subjects. The approach is implemented in OntoMat, an annotation tool for the Semantic Web and shows very promising results.
A Parallel Indexed Algorithm for Information Retrieval
Authors: Craig Stanfill, Robert Thau, David L. Waltz
Venue: Conference SIGIR 1989
Year: 1989

In this paper we present a parallel document ranking algorithm suitable for use on databases of 1-1000 GB, resident on primary or secondary storage. The algorithm is based on inverted indexes, and has two advantages over a previously published parallel algorithm for retrieval based on signature files. First, it permits the employment of ranking strategies which cannot be easily implemented using signature files, specifically methods which depend on document-term weighting. Second, it permits the interactive searching of databases resident on secondary storage. The algorithm is evaluated via a mixture of analytic and simulation techniques, with a particular focus on how cost-effectiveness and efficiency change as the size of the database, number of processors, and cost of memory are altered. In particular, we find that if the ratio of the number of processors and/or disks to the size of the database is held constant, then the cost-effectiveness of the resulting system remains constant. Furthermore, for a given size of database, there is a number of processors which optimizes cost-effectiveness. Estimated response times are also presented. Using these methods, it appears that cost-effective interactive access to databases in the 100-1000 GB range can be achieved using current technology.
Viewpoint selection by navigation through entropy maps
Authors: Tal Arbel, Frank P. Ferrie
Venue: Conference ICCV 1999
Year: 1999

In this paper, we show how entropy maps can be used to guide an active observer along an optimal trajectory, by which the identity and pose of objects in the world can be inferred with confidence, while minimizing the amount of data that must be gathered. Specifically we consider the case of active object recognition where entropy maps are used to encode prior knowledge about the discriminability of objects as a function of viewing position. The paper describes how these maps are computed using optical flow signatures as a case study, and how a gaze-planning strategy can be formulated by using entropy minimization as a basis for choosing a next best view. Experimental results are presented which show the strategy's effectiveness for active object recognition using a single monochrome television camera.
Trust-based recommendation systems: an axiomatic approach
Authors: Reid Andersen, Christian Borgs, Jennifer T. Chayes, Uriel Feige, Abraham D. Flaxman, Adam Kalai, Vahab S. Mirrokni, Moshe Tennenholtz
Venue: Conference WWW 2008
Year: 2008

High-quality, personalized recommendations are a key feature in many online systems. Since these systems often have explicit knowledge of social network structures, the recommendations may incorporate this information. This paper focuses on networks that represent trust and recommendation systems that incorporate these trust relationships. The goal of a trust-based recommendation system is to generate personalized recommendations by aggregating the opinions of other users in the trust network.In analogy to prior work on voting and ranking systems, we use the axiomatic approach from the theory of social choice. We develop a set of five natural axioms that a trust-based recommendation system might be expected to satisfy. Then, we show that no system can simultaneously satisfy all the axioms. However, for any subset of four of the five axioms we exhibit a recommendation system that satisfies those axioms. Next we consider various ways of weakening the axioms, one of which leads to a unique recommendation system based on random walks. We consider other recommendation systems, including systems based on personalized PageRank, majority of majorities, and minimum cuts, and search for alternative axiomatizations that uniquely characterize these systems.Finally, we determine which of these systems are incentive compatible, meaning that groups of agents interested in manipulating recommendations can not induce others to share their opinion by lying about their votes or modifying their trust links. This is an important property for systems deployed in a monetized environment.
Discovery of climate indices using clustering
Authors: Michael S. Steinbach, Pang-Ning Tan, Vipin Kumar, Steven A. Klooster, Christopher Potter
Venue: Conference KDD 2003
Year: 2003

To analyze the effect of the oceans and atmosphere on land climate, Earth Scientists have developed climate indices, which are time series that summarize the behavior of selected regions of the Earth’s oceans and atmosphere. In the past, Earth scientists have used observation and, more recently, eigenvalue analysis techniques, such as principal components analysis (PCA) and singular value decomposition (SVD), to discover climate indices. However, eigenvalue techniques are only useful for finding a few of the strongest signals. Furthermore, they impose a condition that all discovered signals must be orthogonal to each other, making it difficult to attach a physical interpretation to them. This paper presents an alternative clustering-based methodology for the discovery of climate indices that overcomes these limitations and is based on clusters that represent regions with relatively homogeneous behavior. The centroids of these clusters are time series that summarize the behavior of the ocean or atmosphere in those regions. Some of these centroids correspond to known climate indices and provide a validation of our methodology; other centroids are variants of known indices that may provide better predictive power for some land areas; and still other indices may represent potentially new Earth science phenomena. Finally, we show that cluster based indices generally outperform SVD derived indices, both in terms of area weighted correlation and direct correlation with the known indices.
Search engine click spam detection based on bipartite graph propagation
Authors: Xin Li, Min Zhang, Yiqun Liu, Shaoping Ma, Yijiang Jin, Liyun Ru
Venue: Conference WSDM 2014
Year: 2014

Using search engines to retrieve information has become an important part of people's daily lives. For most search engines, click information is an important factor in document ranking. As a result, some websites cheat to obtain a higher rank by fraudulently increasing clicks to their pages, which is referred to as "Click Spam". Based on an analysis of the features of fraudulent clicks, a novel automatic click spam detection approach is proposed in this paper, which consists of 1. modeling user sessions with a triple sequence, which, to the best of our knowledge, takes into account not only the user action but also the action objective and the time interval between actions for the first time; 2. using the user-session bipartite graph propagation algorithm to take advantage of cheating users to find more cheating sessions; and 3. using the pattern-session bipartite graph propagation algorithm to obtain cheating session patterns to achieve higher precision and recall of click spam detection. Experimental results based on a Chinese commercial search engine using real-world log data containing approximately 80 million user clicks per day show that 2.6% of all clicks were detected as spam with a precision of up to 97%.
Joint relevance and answer quality learning for question routing in community qa
Authors: Guangyou Zhou, Kang Liu, Jun Zhao
Venue: Conference CIKM 2012
Year: 2012

Community question answering (cQA) has become a popular service for users to ask and answer questions. In recent years, the efficiency of cQA service is hindered by a sharp increase of questions in the community. This paper is concerned with the problem of question routing. Question routing in cQA aims to route new questions to the eligible answerers who can give high quality answers. However, the traditional methods suffer from the following two problems: (1) word mismatch between the new questions and the users' answering history; (2) high variance in perceived answer quality. To solve the above two problems, this paper proposes a novel joint learning method by taking both word mismatch and answer quality into a unified framework for question routing. We conduct experiments on large-scale real world data set from Yahoo! Answers. Experimental results show that our proposed method significantly outperforms the traditional query likelihood language model (QLLM) as well as state-of-the-art cluster-based language model (CBLM) and category-sensitive query likelihood language model (TCSLM).
Contextual advertising by combining relevance with click feedback
Authors: Deepayan Chakrabarti, Deepak Agarwal, Vanja Josifovski
Venue: Conference WWW 2008
Year: 2008

Contextual advertising supports much of the Web's ecosystem today. User experience and revenue (shared by the site publisher and the ad network) depend on the relevance of the displayed ads to the page content. As with other document retrieval systems, relevance is provided by scoring the match between individual ads (documents) and the content of the page where the ads are shown (query). In this paper we show how this match can be improved significantly by augmenting the ad-page scoring function with extra parameters from a logistic regression model on the words in the pages and ads. A key property of the proposed model is that it can be mapped to standard cosine similarity matching and is suitable for efficient and scalable implementation over inverted indexes. The model parameter values are learnt from logs containing ad impressions and clicks, with shrinkage estimators being used to combat sparsity. To scale our computations to train on an extremely large training corpus consisting of several gigabytes of data, we parallelize our fitting algorithm in a Hadoop framework [10]. Experimental evaluation is provided showing improved click prediction over a holdout set of impression and click events from a large scale real-world ad placement engine. Our best model achieves a 25% lift in precision relative to a traditional information retrieval model which is based on cosine similarity, for recalling 10% of the clicks in our test data.
Learning the Click-through Rate for Rare/New Ads from Similar Ads
Authors: Kushal S. Dave, Vasudeva Varma
Venue: Conference SIGIR 2010
Year: 2010

Ads on the search engine (SE) are generally ranked based on their Click-through rates (CTR). Hence, accurately predicting the CTR of an ad is of paramount importance for maximizing the SE's revenue. We present a model that inherits the click information of rare/new ads from other semantically related ads. The semantic features are derived from the query ad click-through graphs and advertisers account information. We show that the model learned using these features give a very good prediction for the CTR values.
Transfer understanding from head queries to tail queries
Authors: Yangqiu Song, Haixun Wang, Weizhu Chen, Shusen Wang
Venue: Conference CIKM 2014
Year: 2014

One of the biggest challenges of commercial search engines is how to handle tail queries, or queries that occur very infrequently. Frequent queries, also known as head queries, are easier to handle largely because their intents are evidenced by abundant click-through data (query logs). Tail queries have little historical data to rely on, which makes them difficult to be learned by ranking algorithms. In this paper, we leverage knowledge from two resources to fill the gap. The first is a general knowledgebase containing different granularities of concepts automatically harnessed from the Web. The second is the click-through data for head queries. From the click-through data, we obtain an understanding of queries that trigger clicks. Then, we show that by extracting single or multi-word expressions from both head and tail queries and mapping them to a common concept space defined by the knowledgebase, we are able to transfer the click information of the head queries to the tail queries. To validate our approach, we conduct large scale experiments on two real data sets. One is a mixture of head and tail queries, and the other contains pure tail queries. We show that our approach effectively improves tail query search relevance.
Deep transfer via second-order markov logic
Authors: Jesse Davis, Pedro M. Domingos
Venue: Conference ICML 2009
Year: 2009

Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test instances are from the same domain, but have a different distribution. In deep transfer, test instances are from a different domain entirely (i.e., described by different predicates). Humans routinely perform deep transfer, but few learning systems, if any, are capable of it. In this paper we propose an approach based on a form of second-order Markov logic. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Using this approach, we have successfully transferred learned knowledge among molecular biology, social network and Web domains. The discovered patterns include broadly useful properties of predicates, like symmetry and transitivity, and relations among predicates, such as various forms of homophily.
Cost-aware travel tour recommendation
Authors: Yong Ge, Qi Liu, Hui Xiong, Alexander Tuzhilin, Jian Chen
Venue: Conference KDD 2011
Year: 2011

Advances in tourism economics have enabled us to collect massive amounts of travel tour data. If properly analyzed, this data can be a source of rich intelligence for providing real-time decision making and for the provision of travel tour recommendations. However, tour recommendation is quite different from traditional recommendations, because the tourist's choice is directly affected by the travel cost, which includes the financial cost and the time. To that end, in this paper, we provide a focused study of cost-aware tour recommendation. Along this line, we develop two cost-aware latent factor models to recommend travel packages by considering both the travel cost and the tourist's interests. Specifically, we first design a cPMF model, which models the tourist's cost with a 2-dimensional vector. Also, in this cPMF model, the tourist's interests and the travel cost are learnt by exploring travel tour data. Furthermore, in order to model the uncertainty in the travel cost, we further introduce a Gaussian prior into the cPMF model and develop the GcPMF model, where the Gaussian prior is used to express the uncertainty of the travel cost. Finally, experiments on real-world travel tour data show that the cost-aware recommendation models outperform state-of-the-art latent factor models with a significant margin. Also, the GcPMF model with the Gaussian prior can better capture the impact of the uncertainty of the travel cost, and thus performs better than the cPMF model.
What emotions do news articles trigger in their readers?
Authors: Kevin Hsin-Yih Lin, Changhua Yang, Hsin-Hsi Chen
Venue: Conference SIGIR 2007
Year: 2007

We study the classification of news articles into emotions they invoke in their readers. Our work differs from previous studies, which focused on the classification of documents into their authors' emotions instead of the readers'. We use various combinations of feature sets to find the best combination for identifying the emotional influences of news articles on readers.
An optimization framework for query recommendation
Authors: Aris Anagnostopoulos, Luca Becchetti, Carlos Castillo, Aristides Gionis
Venue: Conference WSDM 2010
Year: 2010

Query recommendation is an integral part of modern search engines. The goal of query recommendation is to facilitate users while searching for information. Query recommendation also allows users to explore concepts related to their information needs.
A structured approach to query recommendation with social annotation data
Authors: Jiafeng Guo, Xueqi Cheng, Gu Xu, Huawei Shen
Venue: Conference CIKM 2010
Year: 2010

Query recommendation has been recognized as an important mean to help users search and also improve the usability of search engines. Existing approaches mainly focus on helping users refine their search queries and the recommendations typically stick to users' search intent, named search interests in this paper. However, users may also have some vague or delitescent interests which they are unaware of until they are faced with one, named exploratory interests. These interests may be provoked within a search session when users read a web page from search results or even follow links on the page. By considering exploratory interests in query recommendation, we attract more user clicks on recommendations. This type of query recommendation has not been explicitly addressed in previous work. In this paper, we propose to recommend queries in a structured way for better satisfying both search and exploratory interests of users. Specifically, we construct a query relation graph from query logs and social annotation data which capture two types of interests respectively. Based on the query relation graph, we employ hitting time to rank possible recommendations, leverage a modularity based approach to group top recommendations into clusters, and label each cluster with social tags. Empirical experimental results indicate that our structured approach to query recommendation with social annotation data can better satisfy users' interests and significantly enhance users' click behavior on recommendations.
A study on the impact of product images on user clicks for online shopping
Authors: Anjan Goswami, Naren Chittar, Sung H. Chung
Venue: Conference WWW 2011
Year: 2011

In this paper we study the importance of image based features on the click-through rate (CTR) in the context of a large scale product search engine. Typically product search engines use text based features in their ranking function. We present a novel idea of using image based features, common in the photography literature, in addition to text based features. We used a stochastic gradient boosting based regression model to learn relationships between features and CTR. Our results indicate statistically significant correlations between the image features and CTR. We also see improvements to NDCG and mean standard regression.
Fashionista: A fashion-aware graphical system for exploring visually similar items
Authors: Ruining He, Chunbin Lin, Julian J. McAuley
Venue: Conference WWW 2016
Year: 2016

To build a fashion recommendation system, we need to help users retrieve fashionable items that are visually similar to a particular query, for reasons ranging from searching alternatives (i.e., substitutes), to generating stylish outfits that are visually consistent, among other applications. In domains like clothing and accessories, such considerations are particularly paramount as the visual appearance of items is a critical feature that guides users' decisions. However, existing systems like Amazon and eBay still rely mainly on keyword search and recommending loosely consistent items (e.g. based on co-purchasing or browsing data), without an interface that makes use of visual information to serve the above needs. In this paper, we attempt to fill this gap by designing and implementing an image-based query system, called Fashionista, which provides a graphical interface to help users efficiently explore those items that are not only visually similar to a given query, but which are also fashionable, as determined by visually-aware recommendation approaches. Methodologically, Fashionista learns a low-dimensional visual space as well as the evolution of fashion trends from large corpora of binary feedback data such as purchase histories of Women's Clothing & Accessories from Amazon, which we use for this demonstration.
Learning to recommend related entities to search users
Authors: Bin Bi, Hao Ma, Bo-June Paul Hsu, Wei Chu, Kuansan Wang, Junghoo Cho
Venue: Conference WSDM 2015
Year: 2015

Over the past few years, major web search engines have introduced knowledge bases to offer popular facts about people, places, and things on the entity pane next to regular search results. In addition to information about the entity searched by the user, the entity pane often provides a ranked list of related entities. To keep users engaged, it is important to develop a recommendation model that tailors the related entities to individual user interests. We propose a probabilistic Three-way Entity Model (TEM) that provides personalized recommendation of related entities using three data sources: knowledge base, search click log, and entity pane log. Specifically, TEM is capable of extracting hidden structures and capturing underlying correlations among users, main entities, and related entities. Moreover, the TEM model can also exploit the click signals derived from the entity pane log. We further provide an inference technique to learn the parameters in TEM, and propose a principled preference learning method specifically designed for ranking related entities. Extensive experiments with two real-world datasets show that TEM with our probabilistic framework significantly outperforms a state of the art baseline, confirming the effectiveness of TEM and our probabilistic framework in related entity recommendation.
On building entity recommender systems using user click log and freebase knowledge
Authors: Xiao Yu, Hao Ma, Bo-June Paul Hsu, Jiawei Han
Venue: Conference WSDM 2014
Year: 2014

Due to their commercial value, search engines and recommender systems have become two popular research topics in both industry and academia over the past decade. Although these two fields have been actively and extensively studied separately, researchers are beginning to realize the importance of the scenarios at their intersection: providing an integrated search and information discovery user experience. In this paper, we study a novel application, i.e., personalized entity recommendation for search engine users, by utilizing user click log and the knowledge extracted from Freebase. To better bridge the gap between search engines and recommender systems, we first discuss important heuristics and features of the datasets. We then propose a generic, robust, and time-aware personalized recommendation framework to utilize these heuristics and features at different granularity levels. Using movie recommendation as a case study, with user click log dataset collected from a widely used commercial search engine, we demonstrate the effectiveness of our proposed framework over other popular and state-of-the-art recommendation techniques.