Student Information Use and Decision-Making in Innovation Competitions

At a large university in the Midwestern United States, librarians work closely with an undergraduate agricultural innovation competition. Librarians serve as entrepreneurial information guides, providing business information instruction and consulting with student groups to mentor them through the innovation process. The competition, with a winning prize of $20,000, focuses on developing new products from soybeans to foster environmental stewardship and reduce reliance on petroleum. Competitions are a form of experiential learning, allowing students to fully experience the product design process and practice making evidence-based decisions. In order to progress through this competition, the students’ inventions must be shown to have environmental benefits, technical benefits, feasibility, and novelty. Early stages of the competition require students to investigate and report on the marketability and patentability of their inventions. As a part of this process, students meet with a business librarian and a patent librarian. The business librarian consults with the students on how to conduct market research, and discusses concepts such as ideation, evidence-based decision-making, performing a market analysis, and determining a target market for their products. The patent librarian consults with the students on intellectual property and the United States patent system, focusing on basic patent searching tools and methods and discussing the concept of “novelty” in the area of new inventions. In 2018, the authors conducted focus groups of students who had competed in that year’s competition, to learn how students find and use information in a competition setting and to determine the extent of the impact library support had on the students’ use of information. The groups included students at all points in their undergraduate careers, and some students who had participated in the competition multiple times. This presentation will present the results of the focus groups and how they will inform continued assessment in future iterations of the competition. Themes explored will include information resources, such as market research databases, search engines, and patent search tools; decision-making; the use of information in the design process; librarians as consultants; and future improvements

RESOURCE DESCRIPTION & ACCESS: CATALOGING STANDARDS AFFECT REFERENCE SERVICE

The aim of this paper is to highlight the relationship between cataloging data and reference services and the importance of including reference librarians in general RDA trainin

Dissecting users' needs for search result explanations

There is a growing demand for transparency in search engines to understand how search results are curated and to enhance users' trust. Prior research has introduced search result explanations with a focus on how to explain, assuming explanations are beneficial. Our study takes a step back to examine if search explanations are needed and when they are likely to provide benefits. Additionally, we summarize key characteristics of helpful explanations and share users' perspectives on explanation features provided by Google and Bing. Interviews with non-technical individuals reveal that users do not always seek or understand search explanations and mostly desire them for complex and critical tasks. They find Google's search explanations too obvious but appreciate the ability to contest search results. Based on our findings, we offer design recommendations for search engines and explanations to help users better evaluate search results and enhance their search experience

User-centered semantic dataset retrieval

Finding relevant research data is an increasingly important but time-consuming task in daily research practice. Several studies report on difficulties in dataset search, e.g., scholars retrieve only partial pertinent data, and important information can not be displayed in the user interface. Overcoming these problems has motivated a number of research efforts in computer science, such as text mining and semantic search. In particular, the emergence of the Semantic Web opens a variety of novel research perspectives. Motivated by these challenges, the overall aim of this work is to analyze the current obstacles in dataset search and to propose and develop a novel semantic dataset search. The studied domain is biodiversity research, a domain that explores the diversity of life, habitats and ecosystems. This thesis has three main contributions: (1) We evaluate the current situation in dataset search in a user study, and we compare a semantic search with a classical keyword search to explore the suitability of semantic web technologies for dataset search. (2) We generate a question corpus and develop an information model to figure out on what scientific topics scholars in biodiversity research are interested in. Moreover, we also analyze the gap between current metadata and scholarly search interests, and we explore whether metadata and user interests match. (3) We propose and develop an improved dataset search based on three components: (A) a text mining pipeline, enriching metadata and queries with semantic categories and URIs, (B) a retrieval component with a semantic index over categories and URIs and (C) a user interface that enables a search within categories and a search including further hierarchical relations. Following user centered design principles, we ensure user involvement in various user studies during the development process

Group recommendation with automatic detection and classification of groups

This PhD thesis presents ART (Automatic Recommendation Technologies), a set of group recommendation algorithms that detect groups of users with similar preferences. With respect to classic group recommendation, the first step that such systems have to compute is the detection of groups of people with similar preferences, in order to respect the constraint on the number of recommendations that can be produced and maximize users’ satisfaction

Chain-based recommendations

Recommender systems are discovery tools. Typically, they infer a user's preferences from her behaviour and make personalized suggestions. They are one response to the overwhelming choices that the Web affords its users. Recent studies have shown that a user of a recommender system is more likely to be satisfied by the recommendations if the system provides explanations that allow the user to understand their rationale, and if the system allows the user to provide feedback on the recommendations to improve the next round of recommendations so that they take account of the user's ephemeral needs. The goal of this dissertation is to introduce a new recommendation framework that offers a better user experience, while giving quality recommendations. It works on content-based principles and addresses both the issues identified in the previous paragraph, i.e.\ explanations and recommendation feedback. We instantiate our framework to produce two recommendation engines, each focusing on one of the themes: (i) the role of explanations in producing recommendations, and (ii) helping users to articulate their ephemeral needs. For the first theme, we show how to unify recommendation and explanation to a greater degree than has been achieved hitherto. This results in an approach that enables the system to find relevant recommendations with explanations that have a high degree of both fidelity and interpretability. For the second theme, we show how to allow users to steer the recommendation process using a conversational recommender system. Our approach allows the user to reveal her short-term preferences and have them taken into account by the system and thus assists her in making a good decision efficiently. Early work on conversational recommender systems considers the case where the candidate items have structured descriptions (e.g.\ sets of attribute-value pairs). Our new approach works in the case where items have unstructured descriptions (e.g.\ sets of genres or tags). For each of the two themes, we describe the problem settings, the state-of-the-art, our system design and our experiment design. We evaluate each system using both offline analyses as well as user trials in a movie recommendation domain. We find that the proposed systems provide relevant recommendations that also have a high degree of serendipity, low popularity-bias and high diversity

Design and Evaluation of User Interfaces for Mobile Web Search

Mobiili tiedonhaku on jatkuvasti kasvava ja monimuotoistuva osa jokapäiväistä tiedonhankintaa. Aikaisemman tutkimuksen mukaan tarvitaan kuitenkin parempia käyttöliittymäratkaisuja tukemaan mobiililaitteilla tapahtuvaa verkkotiedonhakua. Väitöskirjatutkimuksessa suunniteltiin ja toteutettiin kaksi uutta hakukäyttöliittymää, joita arvioitiin käyttäjätutkimuksissa. Ensimmäinen käyttöliittymä perustuu siihen, että hakutulokset luokitellaan ryhmiin niissä esiintyvien avainsanojen perusteella. Käyttäjätutkimusten tulokset osoittavat, että luokittelulla voidaan tukea mobiilikäyttäjien tutkivaa tiedonhakua. Toinen käyttöliittymä antaa hakutulosten yhteydessä yleiskuvan hakulauseen sijaintikohdista tulosdokumenteissa. Vaikkakin menetelmän käyttö vaatii opettelua, käyttäjäarviot osoittavat että se voi auttaa sivuuttamaan huonot hakutulokset, etenkin silloin kun muut hakutulosta kuvaavat tiedot ovat epäselviä. Lisäksi väitöskirjassa tutkittiin aktiivisten mobiili-Internetin käyttäjien tiedontarpeita verkkotiedonhaun käytön ymmärtämiseksi. Tutkimustulosten mukaan hakujen tekeminen ja verkon selaaminen ovat näiden käyttäjien tärkeimpiä tiedonhankintatapoja. Niillä pyritään vastaamaan tiedontarpeisiin heti niiden ilmaantuessa, olipa käyttäjä sitten kotona, liikkeessä tai sosiaalisessa vuorovaikutustilanteessa. Mobiili tiedonhankinta on vahvasti sidoksissa käyttötilanteeseen, mikä tulee huomioida hakukäyttöliittymien suunnittelussa. Tulevaisuuden hakukäyttöliittymät voivat esimerkiksi tukea tiedonhankintaa hyödyntämällä tietoa käyttäjän sijainnista ja aktiviteeteista. Myös epämuodollisten ja tutkivien tiedontarpeiden kasvava rooli asettaa uusia haasteita vuorovaikutuksen suunnittelulle.Mobile Web search is a rapidly growing information seeking activity employed across different locations, situations, and activities. Current mobile search interfaces are based on the ranked result list, dominant in desktop interfaces. Research suggests that new paradigms are needed for better support of mobile searchers. For this dissertation, two such novel search interface techniques were designed, implemented, and evaluated. The first method, a clustering search interface that presents a category- based overview of the results, was studied both in a task-based experiment in a laboratory setting and in a longitudinal field study wherein it was used to address real information needs. The results indicate that clustering can support exploratory search needs when the searcher has trouble defining the information need, requires an overview of the search topic, or is interested in multiple results related to the same topic. The findings informed design guidelines for category-based search interfaces. How and when categorization is presented in the search interface needs to be carefully considered. Categorization methods should be improved, for better response to diverse information needs. Hybrid approaches employing contextually informed clustering, classification, and faceted browsing may offer the best match for user needs. The second presentation method, a visualization of the occurrences of the user s query phrase in a result document, can be incorporated into the ranked result list as an additional, unobtrusive result descriptor. It allows the searcher to see how often the query phrase appears in the result document, enabling the use of various evaluation strategies to assess the relevance of the results. Several iterations of the visualization were studied with users to form an understanding of the potential of this approach. The results suggest that a novel visualization can be useful in ruling out non-relevant results and can assist when the other result descriptors do not provide for a conclusive relevance assessment. However, users familiarity with well-established result descriptors means that users have to learn how to integrate the visualization into their search strategies and reconcile situations in which the visualization is in conflict with other metadata. In addition, the contextual triggers and information behaviors of mobile Internet users were studied, for understanding of the role of Web search as a mobile information seeking activity. The results from this study show that mobile Web search and browsing are important information seeking activities. They are engaged in to resolve emerging information needs as they appear, whether at home, on the go, or in social situations