A study of selection noise in collaborative web search

Collaborative Web search uses the past search behaviour (queries and selections) of a community of users to promote search results that are relevant to the community. The extent to which these promotions are likely to be relevant depends on how reliably past search behaviour can be captured. We consider this issue by analysing the results of collaborative Web search in circumstances where the behaviour of searchers is unreliable

The other side of the social web: A taxonomy for social information access

The power of the modern Web, which is frequently called the Social Web or Web 2.0, is frequently traced to the power of users as contributors of various kinds of contents through Wikis, blogs, and resource sharing sites. However, the community power impacts not only the production of Web content, but also the access to all kinds of Web content. A number of research groups worldwide explore what we call social information access techniques that help users get to the right information using "collective wisdom" distilled from actions of those who worked with this information earlier. This invited talk offers a brief introduction into this important research stream and reviews recent works on social information access performed at the University of Pittsburgh's PAWS Lab lead by the author. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM)

Search trails using user feedback to improve video search

In this paper we present an innovative approach for aiding users in the difficult task of video search. We use community based feedback mined from the interactions of previous users of our video search system to aid users in their search tasks. This feedback is the basis for providing recommendations to users of our video retrieval system. The ultimate goal of this system is to improve the quality of the results that users find, and in doing so, help users to explore a large and difficult information space and help them consider search options that they may not have considered otherwise. In particular we wish to make the difficult task of search for video much easier for users. The results of a user evaluation indicate that we achieved our goals, the performance of the users in retrieving relevant videos improved, and users were able to explore the collection to a greater extent

PowerSearch: Augmenting mobile phone search through personalization

Cell phone has become a fundamental element of people\u27s life. People use it to call each other, browse websites, send text messages, etc. Among all the functionalities, the most important and frequently used is the search functionality. Based on ComScore, in July 2008, Google was estimated to host 235 millions searches per day. However, unlike the search on desktop, the search on cell phone has one critical constrain: battery. Cell phone performing a normal Google search, the battery drains very fast. The reason is that when sending a query to and fetching the results from Google, cell phone keeps communicating to the website through networks such as WiFi and 3G. Yet, due to the limited bandwidth of the network and the large amount of the results, the time of communication will be very long. As a result, the battery dies very quickly. In order to prevent this fast drain of battery, a new program is proposed to personalize the search criteria and fetch the most precise and personalized results, instead of all the results, from the web. Because only a few results are fetched, cell phone will not be communicating with the Internet. Hence, the battery will not die very fast. The program can increase the energy-efficiency of the battery and, thus, lengthen the running time of the cell phone

A Social Framework for Set Recommendation in Group Recommender Systems

This research article presents a study about the background in Group Recommender Systems and how social factors are directly related to these applications. Some important group recommender systems in academia are described to exemplify their contribution in different domains. Besides, a framework that is intended to improve group recommender systems is proposed. The main idea of the framework is to enhance social cognition to help the group members agree and make a decision. Its structure includes a process where an influential group is detected among the target groups of people to recommend to. Social influence detection uses the knowledge behind online social connections and interactions. Trying to understand human behavior and ties among groups in a social network and how to use this to improve group recommender systems is considered the main challenge for future research. Combining this with the kind of item recommendation which involves a temporal sequence of ordered elements will present a novel and original path in Group Recommender Systems design. &nbsp

An ant-colony based approach for real-time implicit collaborative information seeking

This document is an Accepted Manuscript of the following article: Alessio Malizia, Kai A. Olsen, Tommaso Turchi, and Pierluigi Crescenzi, ‘An ant-colony based approach for real-time implicit collaborative information seeking’, Information Processing & Management, Vol. 53 (3): 608-623, May 2017. Under embargo until 31 July 2018. The final, definitive version of this paper is available online at doi: https://doi.org/10.1016/j.ipm.2016.12.005, published by Elsevier Ltd.We propose an approach based on Swarm Intelligence — more specifically on Ant Colony Optimization (ACO) — to improve search engines’ performance and reduce information overload by exploiting collective users’ behavior. We designed and developed three different algorithms that employ an ACO-inspired strategy to provide implicit collaborative-seeking features in real time to search engines. The three different algorithms — NaïveRank, RandomRank, and SessionRank — leverage on different principles of ACO in order to exploit users’ interactions and provide them with more relevant results. We designed an evaluation experiment employing two widely used standard datasets of query-click logs issued to two major Web search engines. The results demonstrated how each algorithm is suitable to be employed in ranking results of different types of queries depending on users’ intent.Peer reviewedFinal Accepted Versio

Division of labour and sharing of knowledge for synchronous collaborative information retrieval

Synchronous collaborative information retrieval (SCIR) is concerned with supporting two or more users who search together at the same time in order to satisfy a shared information need. SCIR systems represent a paradigmatic shift in the way we view information retrieval, moving from an individual to a group process and as such the development of novel IR techniques is needed to support this. In this article we present what we believe are two key concepts for the development of effective SCIR namely division of labour (DoL) and sharing of knowledge (SoK). Together these concepts enable coordinated SCIR such that redundancy across group members is reduced whilst enabling each group member to benefit from the discoveries of their collaborators. In this article we outline techniques from state-of-the-art SCIR systems which support these two concepts, primarily through the provision of awareness widgets. We then outline some of our own work into system-mediated techniques for division of labour and sharing of knowledge in SCIR. Finally we conclude with a discussion on some possible future trends for these two coordination techniques

Conference Navigator 2.0: Community-Based Recommendation for Academic Conferences

As the sheer volume of information grows, information overload challenges users in many ways. Large conferences are one of the venues suffering from this overload. Faced with several parallel sessions and large volumes of papers covering diverse areas of interest, conference participants often struggle to identify the most relevant sessions to attend. The Conference Navigator 2.0 system was created to help conference participants go examine the schedule of paper presentation, add most interesting papers to individual schedule, and export this schedule to a calendar application. In addition, as a social system, the Conference Navigator 2.0 collects the wisdom of the user community and make it available through community-based recommendation interface to help individuals in making scheduling decisions