PIKM workshop offers to Ph.D. students the possibility to bring their work to an international and interdisciplinary research community, and create a network of young researchers to exchange and develop new and promising ideas. Similarly to the CIKM, ...

The LocWeb 2014 workshop continues a successful workshop series at the intersection of geospatial search, information management, and Web architecture with a focus towards location-aware information access. The workshop reflects a multitude of fields ...

There is an increasing amount of structure on the Web as a result of modern Web languages, user tagging and annotation, emerging robust NLP tools, and an ever growing volume of linked data. These meaningful, semantic, annotations hold the promise to ...

Similarity search, or finding approximate nearest neighbors, is an important technique in various large scale information retrieval applications such as document retrieval. Many recent research demonstrate that hashing methods can achieve promising ...

A recent area in which recommender systems have shown their value is in online discussion forums and question-answer sites. Earlier work in this space has focused on the problem of matching participants to opportunities but has not adequately addressed ...

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 ...

Identifying user tasks from query logs has garnered considerable interest from the research community lately. Several approaches have been proposed to extract tasks from search sessions. Current approaches segment a user session into disjoint tasks using ...

Rich contextual information is typically available in many recommendation domains allowing recommender systems to model the subtle effects of context on preferences. Most contextual models assume that the context shares the same latent space with the ...

This paper proposes a novel bootstrapping based framework jointed with automatic refinement to extract opinion words and targets. We employ a reasonable set of opinion seed words and pre-defined rules to start bootstrapping. We leverage statistical word ...

We investigate latent aspect mining problem that aims at automatically discovering aspect information from a collection of review texts in a domain in an unsupervised manner. One goal is to discover a set of aspects which are previously unknown for the ...

Most collaborative filtering (CF) algorithms only make use of the rating scores given by users for items. However, it is often the case that each rating score is associated with a piece of review text. Such review texts, which are capable of providing us ...

Multi-matrix factorization models provide a scalable and effective approach for multi-relational learning tasks such as link prediction, Linked Open Data (LOD) mining, recommender systems and social network analysis. Such models are learned by optimizing ...

Tensor decomposition operation is the basis for many data analysis tasks from clustering, trend detection, anomaly detection, to correlation analysis. One key problem with tensor decomposition, however, is its computational complexity -- especially for ...

For online reviews, sentiment explanations refer to the sentences that may suggest detailed reasons of sentiment, which are very important for applications in review mining like opinion summarization. In this paper, we address the problem of ranking ...

Recent years have witnessed an increasing interest in how to incorporate social network information into recommendation algorithms to enhance the user experience. In this paper, we find the phenomenon that users in the contexts of recommendation system ...

Determining cascade size and the factors affecting cascade size are two fundamental research problems in social network analysis. The commonly considered independent cascade model, when applied to social networks such as Digg, produces a phase-transition ...

We consider the experts recommendation problem for open collaborative projects in large-scale Open Source Software (OSS) communities. In large-scale online community, recommending expert collaborators to a project coordinator or lead developer has two ...

The fast growth of technologies has driven the advancement of our society. It is often necessary to quickly grasp the linkage between different technologies in order to better understand the technical trend. The availability of huge volumes of granted ...

Semantic spaces, a useful learning framework for lexical resources, are typically treated as black boxes and applied using geometric and linear algebraic processing tools. We have found that topological methods are useful for exploring the makeup of a ...

Matrix factorization is one of the most powerful techniques in collaborative filtering, which models the (user, item) interactions behind historical explicit or implicit feedbacks. However, plain matrix factorization may not be able to uncover the ...