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Many applications require semantic understanding of short texts, and inferring discriminative and coherent latent topics is a critical and fundamental task in these applications. Conventional topic models largely rely on word co-occurrences to...
Using Replicates in Information Retrieval Evaluation
Ellen M. Voorhees, Daniel Samarov, Ian Soboroff
Article No.: 12
This article explores a method for more accurately estimating the main effect of the system in a typical test-collection-based evaluation of information retrieval systems, thus increasing the sensitivity of system comparisons. Randomly...
Similarity search is a fundamental problem in network analysis and can be applied in many applications, such as collaborator recommendation in coauthor networks, friend recommendation in social networks, and relation prediction in medical...
Social Influence Spectrum at Scale: Near-Optimal Solutions for Multiple Budgets at Once
Hung T. Nguyen, Preetam Ghosh, Michael L. Mayo, Thang N. Dinh
Article No.: 14
Given a social network, the Influence Maximization (InfMax) problem seeks a seed set of k people that maximizes the expected influence for a viral marketing campaign. However, a solution for a particular seed size k is often not...
Active Learning for Classification with Maximum Model Change
Wenbin Cai, Yexun Zhang, Ya Zhang, Siyuan Zhou, Wenquan Wang, Zhuoxiang Chen, Chris Ding
Article No.: 15
Most existing active learning studies focus on designing sample selection algorithms. However, several fundamental problems deserve investigation to provide deep insight into active learning. In this article, we conduct an in-depth investigation...
DBpedia-Based Entity Linking via Greedy Search and Adjusted Monte Carlo Random Walk
Ming Liu, Lei Chen, Bingquan Liu, Guidong Zheng, Xiaoming Zhang
Article No.: 16
Facing a large amount of entities appearing on the web, entity linking has recently become useful. It assigns an entity from a resource to one name mention to help users grasp the meaning of this name mention. Unfortunately, many possible entities...
Parallelization of Massive Textstream Compression Based on Compressed Sensing
Min Peng, Wang Gao, Hua Wang, Yanchun Zhang, Jiajia Huang, Qianqian Xie, Gang Hu, Gang Tian
Article No.: 17
Compressing textstreams generated by social networks can both reduce storage consumption and improve efficiency such as fast searching. However, the compression process is a challenge due to the large scale of textstreams. In this article, we...
Modeling and Mining Domain Shared Knowledge for Sentiment Analysis
Guang-You Zhou, Jimmy Xiangji Huang
Article No.: 18
Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of user generated sentiment data (e.g., reviews, blogs). In real applications, these user-generated sentiment data can span so many different...
What Does Affect the Correlation Among Evaluation Measures?
Article No.: 19
Information Retrieval (IR) is well-known for the great number of adopted evaluation measures, with new ones popping up more and more frequently. In this context, correlation analysis is the tool used to study the evaluation measures and to...
AWARE: Exploiting Evaluation Measures to Combine Multiple Assessors
Marco Ferrante, Nicola Ferro, Maria Maistro
Article No.: 20
We propose the Assessor-driven Weighted Averages for Retrieval Evaluation (AWARE) probabilistic framework, a novel methodology for dealing with multiple crowd assessors that may be contradictory and/or noisy. By modeling relevance...
Understanding and Leveraging the Impact of Response Latency on User Behaviour in Web Search
Xiao Bai, Ioannis Arapakis, B. Barla Cambazoglu, Ana Freire
Article No.: 21
The interplay between the response latency of web search systems and users’ search experience has only recently started to attract research attention, despite the important implications of response latency on monetisation of such systems....
Local Representative-Based Matrix Factorization for Cold-Start Recommendation
Lei Shi, Wayne Xin Zhao, Yi-Dong Shen
Article No.: 22
Cold-start recommendation is one of the most challenging problems in recommender systems. An important approach to cold-start recommendation is to conduct an interview for new users, called the interview-based approach. Among the...