Shuguang Han

Modeling search processes using hidden states in collaborative exploratory web search

Investigations of search processes that involve complex interactions, such as collaborative search processes, are important research topics. Previous approaches of directly applying individual search process models into collaborative settings have proven to be problematic. In this paper, we proposed an innovative approach to model collaborative search processes using Hidden Markov Model (HMM), which is an automatic technique for analyzing temporal sequential data. Obtained through a user study, the data used in this paper consist of two different tasks in both collaborative exploratory Web search and individual exploratory Web search conditions. Our results showed that the identified hidden patterns of search process through HMM are compatible with previous well-known models. In addition, HMM generates detailed information on the transitions of hidden patterns in search processes, which demonstrated to be useful for analyzing task differences, and for determining the correlation of search process with search performance. The findings can be used for evaluating collaborative search systems as well as providing guidance for the system design.

Understanding and Supporting Cross-Device Web Search for Exploratory Tasks with Mobile Touch Interactions

Mobile devices enable people to look for information at the moment when their information needs are triggered. While experiencing complex information needs that require multiple search sessions, users may utilize desktop computers to fulfill information needs started on mobile devices. Under the context of mobile-to-desktop web search, this article analyzes users’ behavioral patterns and compares them to the patterns in desktop-to-desktop web search. Then, we examine several approaches of using Mobile Touch Interactions (MTIs) to infer relevant content so that such content can be used for supporting subsequent search queries on desktop computers. The experimental data used in this article was collected through a user study involving 24 participants and six properly designed cross-device web search tasks. Our experimental results show that (1) users’ mobile-to-desktop search behaviors do significantly differ from desktop-to-desktop search behaviors in terms of information exploration, sense-making and repeated behaviors. (2) MTIs can be employed to predict the relevance of click-through documents, but applying document-level relevant content based on the predicted relevance does not improve search performance. (3) MTIs can also be used to identify the relevant text chunks at a fine-grained subdocument level. Such relevant information can achieve better search performance than the document-level relevant content. In addition, such subdocument relevant information can be combined with document-level relevance to further improve the search performance. However, the effectiveness of these methods relies on the sufficiency of click-through documents. (4) MTIs can also be obtained from the Search Engine Results Pages (SERPs). The subdocument feedbacks inferred from this set of MTIs even outperform the MTI-based subdocument feedback from the click-through documents.

Coauthor prediction for junior researchers

Research collaboration can bring in different perspectives and generate more productive results. However, finding an appropriate collaborator can be difficult due to the lacking of sufficient information. Link prediction is a related technique for collaborator discovery; but its focus has been mostly on the core authors who have relatively more publications. We argue that junior researchers actually need more help in finding collaborators. Thus, in this paper, we focus on coauthor prediction for junior researchers. Most of the previous works on coauthor prediction considered global network feature and local network feature separately, or tried to combine local network feature and content feature. But we found a significant improvement by simply combing local network feature and global network feature. We further developed a regularization based approach to incorporate multiple features simultaneously. Experimental results demonstrated that this approach outperformed the simple linear combination of multiple features. We further showed that content features, which were proved to be useful in link prediction, can be easily integrated into our regularization approach.

Influences on Query Reformulation in Collaborative Web Search

Past analysis has considered query reformulation primarily from the perspective of individual Web searches. Findings from a recent study suggest ways that collaboration during the search process influences how users generate new terms for query reformulation.

A Comparison of Action Transitions in Individual and Collaborative Exploratory Web Search

Collaboration in Web search can be characterized as implicit or explicit in terms of intent, and synchronous or asynchronous in terms of concurrency. Different collaboration style may greatly affect search actions. This paper presents a user study aiming to compare search processes in three different conditions: pair of users working on the same Web search tasks synchronously with explicit communication, pair of users working on the same Web search tasks asynchronously without explicit communication and single users work separately. Our analysis of search processes focused on the transition of user search actions logged in our exploratory Web search system called CollabSearch. The results show that the participants exhibited different patterns of search actions under different conditions. We also found that explicit communication is one of the possible sources for users to obtain ideas of queries, and the explicit communication between users also promotes their implicit communication. Finally this study provides some guidance on the range of behaviors and activities that a collaborative search system should support.

Supporting Cross-Device Web Search with Social Navigation-Based Mobile Touch Interactions

The wide adoption of smartphones eliminates the time and location barriers for people’s daily information access, but also limits users’ information exploration activities due to the small mobile screen size. Thus, cross-device web search, where people initialize information needs on one device but complete them on another device, is frequently observed in modern search engines, especially for exploratory information needs. This paper aims to support the cross-device web search, on top of the commonly used context-sensitive retrieval framework, for exploratory tasks. To better model users’ search context, our method not only utilizes the search history (query history and click-through) but also employs the mobile touch interactions (MTI) on mobile devices. To be more specific, we combine MTI’s ability of locating relevant subdocument content [10] with the idea of social navigation that aggregates MTIs from other users who visit the same page. To demonstrate the effectiveness of our proposed approach, we designed a user study to collect cross-device web search logs on three different types of tasks from 24 participants and then compared our approach with two baselines: a traditional full text based relevance feedback approach and a self-MTI based subdocument relevance feedback approach. Our results show that the social navigation-based MTIs outperformed both baselines. A further analysis shows that the performance improvements are related to several factors, including the quality and quantity of click-through documents, task types and users’ search conditions.

Where do the query terms come from?: an analysis of query reformulation in collaborative web search

This paper presents a user study aiming to investigate the query reformulation in collaborative Web search. 7 pairs of participants were recruited and each pair worked as a team on two collaborative exploratory Web search tasks. Through the log analysis, we compared possible sources for participants to draw query terms from. The results show that both search and collaborative actions are possible resources for new query terms. Traditional resources for query expansion such as previous search histories and relevant documents are still important resources for new query terms. The content in chat and workspace generated by participants themselves seems more likely to be the resource for new query terms than that of their partners. Task types also affect the influences on query reformulations. For the academic task, previously saved relevance documents are the most important resources for new query terms while chat histories are the most important resources for the leisure task.

Deep keyphrase generation

Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as deep keyphrase generation since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at this https URL.

Network-Based Social Search

With the wide adoption of social media in recent years, researchers on social information access are gaining more interests on applying various of social interactions (e.g., friendship, bookmarking, tagging) for satisfying people’s information needs. In this chapter, we focus on methods and technologies to boost information retrieval performance based on the idea of representing social information as networks. We study three different types of networks: people-centric networks, document-centric networks and heterogeneous networks combining both. Information from these networks has been utilized to compute vertex similarity (at the individual level), identify network clusters (at the community level) and calculate entire network measurements (at the network level), which are further applied to help search problems not only for seeking documents but also when searching for people. This chapter provides an extensive reviews of existing methods and technologies for performing such two search topics using networks. Through this chapter, our goal is to provide readers with introductory review of the existing work, and provide concrete presentations of relevant technologies for designing and developing network-based social search systems. Finally, we also point out potential remaining challenges on this topic.

A Study of Mobile Information Exploration with Multi-touch Interactions

Compared to desktop interfaces, touch-enabled mobile devices allow richer user interaction with actions such as drag, pinch-in, pinch-out, and swipe. While these actions have been already used to improve the ranking of search results or lists of recommendations, in this paper we focus on understanding how these actions are used in exploration tasks performed over lists of items not sorted by relevance, such as news or social media posts. We conducted a user study on an exploratory task of academic information, and through behavioral analysis we uncovered patterns of actions that reveal user intention to navigate new information, to relocate interesting items already explored, and to analyze details of specific items. With further analysis we found that dragging direction, speed and position all implied users’ judgment on their interests and they offer important signals to eventually learn user preferences.