OpenRaiser/Intern-Atlas huggingface dataset:
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
Authors: Su Yan, Wei Lin, Tianshu Wu, Daorui Xiao, Xu Zheng, Bo Wu, Kaipeng Liu
Venue: WWW
Year: 2018

In most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance.
A NeuRetrieval Model for Human-Computer Conversations
Authors: Rui Yan, Dongyan Zhao
Venue: WWW
Year: 2018

To establish an automatic conversation system between human and computer is regarded as one of the most hardcore problems in computer science. It requires interdisciplinary techniques of information retrieval, natural language processing, data management as well as artificial intelligence. The arrival of big data era reveals the feasibility to create a conversation system empowered by data-driven approaches. Now we are able to collect extremely large conversational data on Web, and organize them to launch a human-computer conversation system. Owing to the diversity of Web resources available, a retrieval-based conversation system will be able to find at least some responses from the massive data repository for any user inputs. Given a human issued utterance, i.e., a query, a retrieval-based conversation system will search for appropriate replies, conduct a relevance ranking, and then output the highly relevant one as the response. In this paper, we propose a novel retrieval model named NeuRetrieval for short text understanding, representation and semantic matching. The proposed model is general and unified for both single-turn and multi-turn conversation scenarios in open domain. In the experiments, we investigate the effectiveness of the proposed deep neural network model for human-computer conversations. We demonstrate performance improvement against a series of baseline methods in several evaluation metrics. In contrast with previously proposed methods, NeuRetrieval is tailored for conversation scenarios and demonstrated to be more effective.
Query for Streaming Information: Dynamic Processing and Adaptive Incremental Maintenance of RDF Stream
Authors: Xuanxing Yang
Venue: WWW
Year: 2018

Recently with dynamic information being ubiquitous on the Web, there have been efforts to extend RDF and SPARQL for representing streaming information and continuous querying functionalities, respectively. While existing works focusing on formalization and implementation of continuous querying process over RDF streams, little attention has deserved the problem of querying the complex temporal correlations among RDF stream tuples and the effective, scalable implementation of RDF stream processing system. To fill this gap, in this paper we propose CT-SPARQL and AIMRS, a language specifying for the compositional stream patterns and an architecture for adaptive incremental maintenance of RDF stream tuples defined in CT-SPARQL. We believe that this work will benefit a wide range of real-time analyzing and future predicting applications.
Any-k: Anytime Top-k Tree Pattern Retrieval in Labeled Graphs
Authors: Xiaofeng Yang, Deepak Ajwani, Wolfgang Gatterbauer, Patrick K. Nicholson, Mirek Riedewald, Alessandra Sala
Venue: WWW
Year: 2018

Many problems in areas as diverse as recommendation systems, social network analysis, semantic search, and distributed root cause analysis can be modeled as pattern search on labeled graphs (also called "heterogeneous information networks" or HINs). Given a large graph and a query pattern with node and edge label constraints, a fundamental challenge is to find the top-k matches according to a ranking function over edge and node weights. For users, it is difficult to select value k. We therefore propose the novel notion of an any-k ranking algorithm: for a given time budget, return as many of the top-ranked results as possible. Then, given additional time, produce the next lower-ranked results quickly as well. It can be stopped anytime, but may have to continue until all results are returned. This paper focuses on acyclic patterns over arbitrary labeled graphs. We are interested in practical algorithms that effectively exploit (1) properties of heterogeneous networks, in particular selective constraints on labels, and (2) that the users often explore only a fraction of the top-ranked results. Our solution, KARPET, carefully integrates aggressive pruning that leverages the acyclic nature of the query, and incremental guided search. It enables us to prove strong non-trivial time and space guarantees, which is generally considered very hard for this type of graph search problem. Through experimental studies we show that KARPET achieves running times in the order of milliseconds for tree patterns on large networks with millions of nodes and edges.
Leveraging Crowdsourcing Data for Deep Active Learning An Application: Learning Intents in Alexa
Authors: Jie Yang, Thomas Drake, Andreas C. Damianou, Yoelle Maarek
Venue: WWW
Year: 2018

This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted crowdsourcing approach, where multiple annotators with unknown expertise contribute an uncontrolled amount (often limited) of annotations. Our framework leverages the low-rank structure in annotations to learn individual annotator expertise, which then helps to infer the true labels from noisy and sparse annotations. It provides a unified Bayesian model to simultaneously infer the true labels and train the deep learning model in order to reach an optimal learning efficacy. Finally, our framework exploits the uncertainty of the deep learning model during prediction as well as the annotators» estimated expertise to minimize the number of required annotations and annotators for optimally training the deep learning model. We evaluate the effectiveness of our framework for intent classification in Alexa (Amazon»s personal assistant), using both synthetic and real-world datasets. Experiments show that our framework can accurately learn annotator expertise, infer true labels, and effectively reduce the amount of annotations in model training as compared to state-of-the-art approaches. We further discuss the potential of our proposed framework in bridging machine learning and crowdsourcing towards improved human-in-the-loop systems.
PhotoReply: Automatically Suggesting Conversational Responses to Photos
Authors: Ning Ye, Ariel Fuxman, Vivek Ramavajjala, Sergey Nazarov, J. Patrick McGregor, Sujith Ravi
Venue: WWW
Year: 2018

We introduce the problem of automatically suggesting conversational responses to photos and present an intelligent assistant called PhotoReply that solves the problem in the context of a messaging application. For example, when a user receives a photo showing a dog, PhotoReply suggests responses such as "Aww»» and "Cute terrier!»» This simplifies composing responses on constrained mobile keyboards, and delights users with uncanny insights into the photos they receive. PhotoReply is an integral part of the Allo chat application, being its predictive assistance feature with highest click-through rate. We formalize the problem of suggesting responses to images as an instance of multimodal learning which is akin to caption generation models, a topic that has recently received significant attention \citeKarpathy2015,Vinyals2015,Xu2015. We then present a system that "translates»» image pixels to text responses. The system includes a conditioned language model, based on an LSTM, which, given an embedding of image pixels and the previous predicted words, calculates the probability of all words in a vocabulary of being the next word in the generated response and a triggering model trained with image embeddings and concept labels from a large concept taxonomy. We describe training of the models and a thorough experimental evaluation based on crowdsourced datasets and live traffic.
Detecting Personal Life Events from Twitter by Multi-Task LSTM
Authors: An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: WWW
Year: 2018

People are used to log their life on the social media platform. Life event can be expressed explicitly or implicitly in a text description. However, a description does not always contain life events related to a specific individual. To tell if there exist any life events and further know their categories is indispensable for event retrieval. This paper explores various LSTM models to detect and classify life events in tweets. Experiments show that the proposed Multi-Task LSTM model with attention achieves the best performance.
That Makes Sense: Joint Sense Retrofitting from Contextual and Ontological Information
Authors: Ting-Yu Yen, Yang-Yin Lee, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: WWW
Year: 2018

While recent word embedding models demonstrate their abilities to capture syntactic and semantic information, the demand for sense level embedding is getting higher. In this study, we propose a novel joint sense embedding learning model that retrofits the word representation into sense representation from contextual and ontological information. The experiment shows the effectiveness and robustness of our model that outperforms previous approaches in four public available benchmark datasets.
Profiling OSN Users Based on Temporal Posting Patterns
Authors: Qiufang Ying, Dah Ming Chiu, Srinivasan Venkatramanan, Xiaopeng Zhang
Venue: WWW
Year: 2018

In this paper, we study the posting behavior of OSN users, in particular the posting frequency and temporal patterns, and consider possible interpretations of how users use the platform. At the aggregate (macro) level, we find two distinct peaks, one during morning working hours, and one in the evening. The morning peak is more pronounced for frequent posters, while the evening peak is pronounced in the remaining users. We postulate that this difference results from qualitatively different usage of the OSN platform (e.g. for work, with customers, etc.) than purely social interactions (e.g., friends, family, etc.). We also study user posting behavior at an individual (micro) level and apply LDA to cluster user temporal patterns, interpret our results. Our study provides possibly new insights into user activity in today's OSNs, and suggests a framework for profiling users based on their posting activities. In the process, we provide a novel application of LDA, to temporal user posting behavior by equating the time epochs of posts to words in documents. We believe our approach will complement other methods of user profiling based on static demographic information and friendship network information.
Customer Sentiment in Web-Based Service Interactions: Automated Analyses and New Insights
Authors: Galit B. Yom-Tov, Shelly Ashtar, Daniel Altman, Michael Natapov, Neta Barkay, Monika Westphal, Anat Rafaeli
Venue: WWW
Year: 2018

We adjust sentiment analysis techniques to automatically detect customer emotion in on-line service interactions of multiple business domains. Then we use the adjusted sentiment analysis tool to report insights about the dynamics of emotion in on-line service chats, using a large data set of Telecommunication customer service interactions. Our analyses show customer emotions starting out negative and evolving into positive as the interaction ends. Also, we identify a close relationship between customer emotion dynamicsduring the service interaction and the concepts of service failure and recovery. This connection manifests in customer service quality evaluationsafter the interaction ends. Our study shows the connection between customer emotion and service quality as service interactions unfold, and suggests the use of sentiment analysis tools for real-time monitoring and control of web-based service quality.
Anxiety and Information Seeking: Evidence From Large-Scale Mouse Tracking
Authors: Brit Youngmann, Elad Yom-Tov
Venue: WWW
Year: 2018

People seeking information through search engines are assumed to behave similarly, regardless of the topic which they are searching. Here we use mouse tracking, which is correlated with gaze, to show that the information seeking patterns of people differ dramatically depending on their level of anxiety at the time of the search. We investigate the behavior of people during searches for medical symptoms, ranging from benign indications, where users are not usually anxious, to ones which could harbinger life-threatening conditions, where extreme anxiety is expected. We show that for the latter, 90% of people never saw more than the top 67% of the screen, compared to over 95% scanned by people seeking information on benign symptoms, even though relevant documents are similarly distributed in the results pages to these queries. Based on this observation, we develop a model which can predict the level of anxiety experienced by a user, using attributes derived from mouse tracking data and other user interactions. The model achieves Kendall's Tau of 0.48 with the medical severity of the symptoms searched. We show the importance of using information about the users? level of anxiety as predicted by the model, when measuring search engine performance. Our results prove that ignoring this information can lead to significant over-estimation of performance. Additionally, we show the utility of the model in three special instances: where multiple symptoms are searched concurrently; where the searcher has an underlying medical condition; and when users seek information on ways to commit suicide. In the latter, our results demonstrate the importance of help-line notices, and emphasize the need to measure the effective number of results seen by the user. Our results indicate that measures of relevance which use anxiety information can lead to more accurate understanding of the quality of search results, especially when delivering potentially life-saving information to users.
Fast Exact CoSimRank Search on Evolving and Static Graphs
Authors: Weiren Yu, Fan Wang
Venue: WWW
Year: 2018

In real Web applications, CoSimRank has been proposed as a powerful measure of node-pair similarity based on graph topologies. However, existing work on CoSimRank is restricted to static graphs. When the graph is updated with new edges arriving over time, it is cost-inhibitive to recompute all CoSimRank scores from scratch, which is impractical. In this study, we propose a fast dynamic scheme, \DCoSim for accurate CoSimRank search over evolving graphs. Based on \DCoSim, we also propose a fast scheme, \FCoSim, that greatly accelerates CoSimRank search over static graphs. Our theoretical analysis shows that \DCoSim and \FCoSim guarantee the exactness of CoSimRank scores. On the static graph G, to efficiently retrieve CoSimRank scores $\mathbfS $, \FCoSim is based on three ideas: (i) It first finds a "spanning polytree»» T over G. (ii) On T, a fast algorithm is designed to compute the CoSimRank scores $\mathbfS (T)$ over the "spanning polytree»» T. (iii) On G, \DCoSim is employed to compute the changes of $\mathbfS (T)$ in response to the delta graph $(G øminus T)$. Experimental evaluations verify the superiority of \DCoSim over evolving graphs, and the fast speedup of \FCoSim on large-scale static graphs against its competitors, without any loss of accuracy.
A Discovery and Analysis Engine for Semantic Web
Authors: Semih Yumusak, Andreas Kamilaris, Erdogan Dogdu, Halife Kodaz, Elif Uysal, Riza Emre Aras
Venue: WWW
Year: 2018

The Semantic Web promotes common data formats and exchange protocols on the web towards better interoperability among systems and machines. Although Semantic Web technologies are being used to semantically annotate data and resources for easier reuse, the ad hoc discovery of these data sources remains an open issue. Popular Semantic Web endpoint repositories such as SPARQLES, Linking Open Data Project (LOD Cloud), and LODStats do not include recently published datasets and are not updated frequently by the publishers. Hence, there is a need for a web-based dynamic search engine that discovers these endpoints and datasets at frequent intervals. To address this need, a novel web meta-crawling method is proposed for discovering Linked Data sources on the Web. We implemented the method in a prototype system named SPARQL Endpoints Discovery (SpEnD). In this paper, we describe the design and implementation of SpEnD, together with an analysis and evaluation of its operation, in comparison to the aforementioned static endpoint repositories in terms of time performance, availability, and size. Findings indicate that SpEnD outperforms existing Linked Data resource discovery methods.
A User-Centric Diversity by Design Recommender System for the Movie Application Domain
Authors: Michele Zanitti, Sokol Kosta, Jannick Kirk Sørensen
Venue: WWW
Year: 2018

Recommender systems (RS) have seen widespread adoption across the Internet. However, by emphasizing personalization through the optimization of accuracy-focused metrics, over-personalization may emerge, with negative effects on the user experience. A countermeasure to the problem is to diversify recommendations. In this paper, we present a solution that addresses the problem in the context of a movie application domain. The solution enhances diversity on four related dimensions, namely global coverage, local coverage, novelty, and redundancy. The proposed solution is designed to diversify users profiles, modeled on categorical preferences, within the same group in the recommendation filtering. We evaluate our approach on the Movielens dataset and show that our algorithm yields better results compared to random selection distant neighbors and performs comparably to one of the current state of the art solutions.
CrowdED: Guideline for Optimal Crowdsourcing Experimental Design
Authors: Amrapali Zaveri, Pedro Hernandez Serrano, Manisha Desai, Michel Dumontier
Venue: WWW
Year: 2018

Crowdsourcing involves the creating of HITs (Human Intelligent Tasks), submitting them to a crowdsourcing platform and providing a monetary reward for each HIT. One of the advantages of using crowdsourcing is that the tasks can be highly parallelized, that is, the work is performed by a high number of workers in a decentralized setting. The design also offers a means to cross-check the accuracy of the answers by assigning each task to more than one person and thus relying on majority consensus as well as reward the workers according to their performance and productivity. Since each worker is paid per task, the costs can significantly increase, irrespective of the overall accuracy of the results. Thus, one important question when designing such crowdsourcing tasks that arise is how many workers to employ and how many tasks to assign to each worker when dealing with large amounts of tasks. That is, the main research questions we aim to answer is: 'Can we a-priori estimate optimal workers and tasks' assignment to obtain maximum accuracy on all tasks'. Thus, we introduce a two-staged statistical guideline, CrowdED, for optimal crowdsourcing experimental design in order to a-priori estimate optimal workers and tasks' assignment to obtain maximum accuracy on all tasks. We describe the algorithm and present preliminary results and discussions. We implement the algorithm in Python and make it openly available on Github, provide a Jupyter Notebook and a R Shiny app for users to re-use, interact and apply in their own crowdsourcing experiments.
Etymo: A New Discovery Engine for AI Research
Authors: Weijian Zhang, Jonathan Deakin, Nicholas J. Higham, Shuaiqiang Wang
Venue: WWW
Year: 2018

We present Etymo (https://etymo.io), a discovery engine to facilitate artificial intelligence (AI) research and development. It aims to help readers navigate a large number of AI-related papers published every week by using a novel form of search that finds relevant papers and displays related papers in a graphical interface. Etymo constructs and maintains an adaptive similarity-based network of research papers as an all-purpose knowledge graph for ranking, recommendation, and visualisation. The network is constantly evolving and can learn from user feedback to adjust itself. A screencast is available at: https://youtu.be/T4FDPk_TmN0
Post Purchase Search Engine Marketing
Authors: Qianyun Zhang, Shawndra Hill, David Rothschild
Venue: WWW
Year: 2018

Although consumer behavior in response to search engine marketing has been studied extensively, few efforts have been made to understand how consumers search and respond to ads post purchase. Advertising to existing customers the same way as to prospective customers inevitably leads to wasteful and inefficient marketing. Employing a unique dataset that combines both search query and purchase data, we examine consumers' searching behavior and response to search engine marketing after purchase. We study large advertising campaigns for two popular technology products. We find that over half of the branded keyword searches come from consumers who already purchased the products, and that advertising response varies based on whether searchers are pre- or post-purchase. In general, post-purchase searchers are less likely to click on focal brand ads (i.e., they are less responsive to ads for products they already own). However, post-purchase searchers are still responsive to advertising and much more likely to click on ads for complementary products (i.e., they are more responsive to ads for relevant products other than the focal product).