Matches in ScholarlyData for { ?s ?p ?o. }
- 11 creator dan-pelleg.
- 11 creator gideon-dror.
- 11 creator idan-szpektor.
- 11 creator oleg-rokhlenko.
- 11 type InProceedings.
- 11 label "Churn Prediction in New Users of Yahoo! Answers".
- 11 sameAs 11.
- 11 abstract "One of the important targets of community-based question answering (CQA)services, such as Yahoo! Answers, Quora and Baidu Zhidao, is to maintain and evenincrease the number of active answerers, that is the users who provide answersto open questions. The reasoning is that they are the engine behind satisfiedaskers, which is the overall goal behind CQA. Yet, this task is not an easy one.Indeed, our empirical observation shows that many users provide just one or twoanswers and then leave.In this work we try to detect answerers that are about to quit, a task known aschurn prediction, but unlike prior work, we focus on new users. To address thetask of churn prediction in new users, we extract a variety of features to modelthe behavior of Yahoo! Answers users over the first week of their activity, including personal information, rate of activity, and social interaction with other users. Several classifiers trained on the data show that there is a statisticallysignificant signal for discriminating between users who are likely to churn andthose who are not. A detailed feature analysis shows that the two most importantsignals are the total number of answers given by the user, closely related tothe motivation of the user, and attributes related to the amount of recognitiongiven to the user, measured in counts of best answers, thumbs up and positiveresponses by the asker.".
- 11 hasAuthorList authorList.
- 11 isPartOf proceedings.
- 11 keyword "churn prediction".
- 11 keyword "community question answering".
- 11 keyword "online user behavior".
- 11 title "Churn Prediction in New Users of Yahoo! Answers".
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- item-1 hasContent gideon-dror.
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- 13 creator ashwin-ram.
- 13 creator saurav-sahay.
- 13 type InProceedings.
- 13 label "Socio-semantic Conversational Information Access".
- 13 sameAs 13.
- 13 abstract "We develop an innovative approach to delivering relevant information using a combination of socio-semantic search and filtering approaches. The goal is to facilitate timely and relevant information access through the medium of conversations by mixing past community specific conversational knowledge and web information access to recommend and connect users and information together. Conversational Information Access is a socio-semantic search and recommendation activity with the goal to interactively engage people in conversations by receiving agent supported recommendations. It is useful because people engage in online social discussions unlike a solitary search; the agent brings in relevant information as well as identifies relevant users; participants provide feedback during the conversation that the agent uses to improve it's recommendations.".
- 13 hasAuthorList authorList.
- 13 isPartOf proceedings.
- 13 keyword "Content based Recommendation".
- 13 keyword "Semantic Search".
- 13 keyword "Social Search".
- 13 keyword "User Modeling".
- 13 title "Socio-semantic Conversational Information Access".
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- 2 creator baichuan-li.
- 2 creator barley-mak.
- 2 creator irwin-king.
- 2 creator michael-r-lyu.
- 2 creator tan-jin.
- 2 type InProceedings.
- 2 label "Analyzing and Predicting Question Quality in Community Question Answering Services".
- 2 sameAs 2.
- 2 abstract "Users tend to ask and answer questions in community question answering (CQA) service to seek information and share knowledge. A corollary is that myriad of questions and answers appear in CQA service. Accordingly, volumes of studies have been taken to explore the answer quality so as to provide a preliminary screening for better answers. However, to our knowledge, less attention has so far been paid to question quality in CQA. Knowing question quality provides us with finding and recommending good questions together with identifying bad ones which hinder the CQA service. In this paper, we are conducting two studies to investigate the question quality issue. The first study analyzes the factors of question quality and finds that the interaction between askers and topics results in the differences of question quality. Based on this finding, in the second study we propose a Mutual Reinforcement-based Label Propagation (MRLP) algorithm to predict question quality. We experiment with Yahoo! Answers data and the results demonstrate the effectiveness of our algorithm.".
- 2 hasAuthorList authorList.
- 2 isPartOf proceedings.
- 2 keyword "Analysis".
- 2 keyword "Community Question Answering".
- 2 keyword "Prediction".
- 2 keyword "Question Quality".
- 2 title "Analyzing and Predicting Question Quality in Community Question Answering Services".
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- item-1 hasContent baichuan-li.
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- item-2 hasContent tan-jin.
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- item-5 hasContent barley-mak.