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 ...

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 ...

Recent business studies have shown that social technologies can significantly improve productivity within enterprises by improving access to information, ideas, and collaborators. A manifestation of the growing adoption of enterprise social technologies ...

The rapid proliferation of hand-held devices has led to the development of rich, interactive and immersive applications, such as e-readers for electronic books. These applications motivate retrieval systems that can implicitly satisfy any information ...

Trust plays a crucial role in helping users collect reliable information in an online world, and has attracted more and more attention in research communities lately. As a conceptual counterpart of trust, distrust can be as important as trust. However, ...

Canonical correlation analysis (CCA) has been extensively employed in various real-world applications of multi-label annotation. However, two major challenges are raised by the classical CCA. First, CCA frequently fails to remove noisy and irrelevant ...

Graph-based ranking plays a key role in many applications, such as web search and social computing. Pioneering methods of ranking on graphs (e.g., PageRank and HITS) computed ranking scores relying only on the graph structure. Recently proposed methods, ...

Over the past few years, community QA websites (e.g. Yahoo! Answers) have become a useful platform for users to post questions and obtain answers. However, not all questions posted there receive informative answers or are answered in a timely manner. In ...

The information retrieval (IR) community strives to make evaluation more centered on real users and their needs. The living labs evaluation paradigm, i.e., observing users in their natural task environments, offers great promise in this regard. Yet, ...

The term weighting and document ranking functions used with informational queries are typically optimized for cases in which queries are short and documents are long. It is reasonable to assume that the presence of a term in a short query reflects some ...

When consuming content, users typically encounter entities that they are not familiar with. A common scenario is when users want to find information about entities directly within the content they are consuming. For example, when reading the book "...

In this paper, we study one important source of the mis-match between user data and relevance judgments, those due to the high degree of effort required by users to identify and consume the information in a document. Information retrieval relevance ...

The automatic summarization of long-running events from news steams is a challenging problem. A long-running event can contain hundreds of unique 'nuggets' of information to summarize, spread-out over its lifetime. Meanwhile, information reported about ...

Current movie title retrieval models, such as IMDB, mainly focus on utilizing structured or semi-structured data. However, user queries for searching a movie title are often based on the movie plot, rather than its metadata. As a solution to this problem,...

Tail queries, which occur with low frequency, make up a large fraction of unique queries and often affect a user's experience during Web searching. Because of the data sparseness problem, information that can be leveraged for tail queries is not ...

Semantically searching and navigating products (e.g., on Taobao.com or Amazon.com) with professional metadata and user-generated content from social media is a hot topic in information retrieval and recommendation systems, while most existing methods are ...

Knowing, in real time, whether a current searcher in an information retrieval system finds the search task difficult can be valuable for tailoring the system's support for that searcher. This study investigated searcher's behaviors at different stages of ...

In this paper, we propose a new latent semantic model that incorporates a convolutional-pooling structure over word sequences to learn low-dimensional, semantic vector representations for search queries and Web documents. In order to capture the rich ...

Recent research has shown that the improvement of mean retrieval effectiveness (e.g., MAP) may sacrifice the retrieval stability across queries, implying a tradeoff between effectiveness and stability. The evaluation of both effectiveness and stability ...

Result diversification is a topic of great value for enhancing user experience in many fields, such as web search and recommender systems. Many existing methods generate a diversified result in a sequential manner, but they work well only if the ...