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1
February 2017
Information Retrieval: Volume 20 Issue 1, February 2017
Publisher: Kluwer Academic Publishers
Query recommendation has long been considered a key feature of search engines, which can improve users' search experience by providing useful query suggestions for their search tasks. Most existing approaches on query recommendation aim to recommend relevant queries, i.e., alternative queries similar to a user's initial query. However, the ultimate ...
Keywords:
Query recommendation, Query utility, Search behavior, Dynamic Bayesian network
2
January 2017
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue: Mobile Social Multimedia Analytics in the Big Data Era and Regular Papers: Volume 8 Issue 3, April 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 13, Downloads (12 Months): 84, Downloads (Overall): 84
Full text available:
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The queries issued to search engines are often ambiguous or multifaceted, which requires search engines to return diverse results that can fulfill as many different information needs as possible; this is called search result diversification . Recently, the relational learning to rank model, which designs a learnable ranking function following ...
Keywords:
Search result diversification, relational learning to rank, diversity evaluation measure
3
December 2016
Neurocomputing: Volume 218 Issue C, December 2016
Publisher: Elsevier Science Publishers B. V.
Search engine query recommendation based on mining query logs has been considered as an important and useful method of facilitating users to retrieve information. However, the log data evolves quickly. Existing query recommendation approaches have to rebuild the models when new log data arrive. In this paper, we extend the ...
Keywords:
Convex optimization, Query log analysis, Query recommendation, Incremental update method
4
July 2016
IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability ...
5
July 2016
IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
Publisher: AAAI Press
Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interaction between ...
6
July 2016
SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 14, Downloads (12 Months): 224, Downloads (Overall): 224
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Search result diversification has attracted considerable attention as a means to tackle the ambiguous or multi-faceted information needs of users. One of the key problems in search result diversification is novelty, that is, how to measure the novelty of a candidate document with respect to other documents. In the heuristic ...
Keywords:
neural tensor network, search result diversification, relational learning to rank
7
February 2016
AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot well capture the contextualized local information in the matching process. To tackle this problem, we ...
8
February 2016
AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Matching a question to its best answer is a common task in community question answering. In this paper, we focus on the non-factoid questions and aim to pick out the best answer from its candidate answers. Most of the existing deep models directly measure the similarity between question and answer ...
9
February 2016
AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Distributional hypothesis lies in the root of most existing word representation models by inferring word meaning from its external contexts. However, distributional models cannot handle rare and morphologically complex words very well and fail to identify some fine-grained linguistic regularity as they are ignoring the word forms. On the contrary, ...
10
February 2016
AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns ...
11
February 2016
WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining
Publisher: ACM
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 16, Downloads (12 Months): 150, Downloads (Overall): 334
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Demographic attributes play an important role in retail market to characterize different types of users. Such signals however are often only available for a small fraction of users in practice due to the difficulty in manual collection process by retailers. In this paper, we aim to harness the power of ...
Keywords:
demographic attribute, multitask multi-class prediction, structured neural embedding
12
November 2015
Neurocomputing: Volume 167 Issue C, November 2015
Publisher: Elsevier Science Publishers B. V.
Query recommendation technology is of great importance for search engines, because it can assist users to find the information they require. Many query recommendation algorithms have been proposed, but they all aim to recommend similar queries and cannot guarantee the usefulness of the recommended queries. In this paper, we argue ...
Keywords:
Query ranking, Query log analysis, Query recommendation, Recommendation methods
13
October 2015
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 2, Downloads (12 Months): 39, Downloads (Overall): 115
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Ranking SVM, which formalizes the problem of learning a ranking model as that of learning a binary SVM on preference pairs of documents, is a state-of-the-art ranking model in information retrieval. The dual form solution of Ranking SVM model can be written as a linear combination of the preference pairs, ...
Keywords:
parameter interaction, ranking svm
14
August 2015
SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 5
Downloads (6 Weeks): 11, Downloads (12 Months): 83, Downloads (Overall): 349
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In this paper we address the issue of learning a ranking model for search result diversification. In the task, a model concerns with both query-document relevance and document diversity is automatically created with training data. Ideally a diverse ranking model would be designed to meet the criterion of maximal marginal ...
Keywords:
search result diversification, directly optimizing evaluation measures, maximal marginal relevance
15
August 2015
SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher: ACM
Bibliometrics:
Citation Count: 6
Downloads (6 Weeks): 17, Downloads (12 Months): 169, Downloads (Overall): 616
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Next basket recommendation is a crucial task in market basket analysis. Given a user's purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items that the user most probably would like. Ideally, a good recommender should be able to ...
Keywords:
next basket recommendation, hierarchical representation model, sequential behavior, general taste
16
June 2015
Information Retrieval: Volume 18 Issue 3, June 2015
Publisher: Kluwer Academic Publishers
When applying learning to rank algorithms in real search applications, noise in human labeled training data becomes an inevitable problem which will affect the performance of the algorithms. Previous work mainly focused on studying how noise affects ranking algorithms and how to design robust ranking algorithms. In our work, we ...
Keywords:
Label noise, Learning to rank, Robust data
17
May 2015
ICSE '15: Proceedings of the 37th International Conference on Software Engineering - Volume 1
Publisher: IEEE Press
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 5, Downloads (12 Months): 35, Downloads (Overall): 106
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Multi-threaded programs play an increasingly important role in current multi-core environments. Exposing concurrency bugs and debugging such multi-threaded programs have become quite challenging due to their inherent non-determinism. In order to eliminate such non-determinism, many approaches such as record-and-replay and other similar bug reproducing systems have been proposed. However, those ...
Keywords:
bug reproducing, concurrency, local clock
18
February 2015
WSDM '15: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 11, Downloads (12 Months): 57, Downloads (Overall): 310
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Inferring a gold-standard ranking over a set of objects, such as documents or images, is a key task to build test collections for various applications like Web search and recommender systems. Crowdsourcing services provide an efficient and inexpensive way to collect judgments via labeling by sets of annotators. We thus ...
Keywords:
crowdsourced labeling, evaluation measures, rank aggregation
19
January 2015
AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bursty topics discovery in microblogs is important for people to grasp essential and valuable information. However, the task is challenging since microblog posts are particularly short and noisy. This work develops a novel probabilistic model, namely Bursty Biterm Topic Model (BBTM), to deal with the task. BBTM extends the Biterm ...
20
November 2014
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
Publisher: ACM
Bibliometrics:
Citation Count: 3
Downloads (6 Weeks): 6, Downloads (12 Months): 64, Downloads (Overall): 248
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Retail transaction data conveys rich preference information on brands and goods from customers. How to mine the transaction data to provide personalized recommendation to customers becomes a critical task for retailers. Previous recommendation methods either focus on the user-product matrix and ignore the transactions, or only use the partial information ...
Keywords:
association pattern, recommendation, probabilistic model
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