The Melvyl Recommender Project

Online finding aids, internet search tools, and increased access to the World Wide Web have greatly changed how patrons find archival collections. Through analyzing eighteen months of access data collected via web analytics tools, this article examines how patrons discover archival materials. Contrasts are drawn between access from library catalogs and from online search engines, with the latter outweighing the former by an overwhelming margin, and argues whether archival description practices should change accordingly. The past decade brought a revolution in intellectual access to archival collections through the emergence of the online finding aid. These tools, created in a multiplicity of formats (EAD, HTML, PDF, and others), have made it possible for researchers anywhere to use an Internet connection to find materials in archives. Since archivists first introduced this genre of digital documents to support unmediated discovery, they have been accompanied by questions about their use and usability. Tools exist to measure access counts and the pathways followed to these finding aids, but to date very little has been published to show whether these tools are being used for this purpose, or what the results are if they are being used. This article will examine the experience of one repository, Manuscripts, Archives, and Special Collections (MASC) at the Washington State University Libraries, in collecting and analyzing data about the use of online finding aids. By 2008, MASC had well over eight hundred HTML finding aids online but no real numeric data 1 O'English: Applying Web Analytics to Online Finding Aids Published by DigitalCommons@USU, 2011 either on how often they were being accessed or on how they were found. In an effort to gather this data, MASC staff placed code from Google Analytics, a statisticsgathering Web service, on each finding aid webpage, as well as on other local gateway and digital collections pages. The intent was to explore the paths patrons follow to finding aids with the hope of improving access, as well as gaining patron-derived information that would help establish priorities for processing collections from the repository backlog and to determine which processed collections might benefit from further attention. The results of the statistical analysis provide useful data on where and how MASC users access our finding aids. Prior to the age of digital finding aids, their paper predecessors were found through various methods, which were generally predictable and well-defined, including online and analog library catalogs, published listings, and mediation by archives staff in person or by telephone. As the digital age emerged, many archives channeled substantial resources into major conversion projects, digitizing finding aids and often creating access to them through existing or new MARC records in online library catalogs. Digital search tools have become increasingly powerful and widespread, and social media have emerged as another path to online finding aids. Archivists with quantitative and qualitative data about the use of online finding aids can make better-informed decisions about the most effective ways to support discovery. The findings in this study suggest that it may again be time to change focus, shifting resources away from catalogs and toward the finding aids themselves.

Introduction: This paper explores the potential of Linked Data to enhance library catalog records, using the internationally recognized song "The Girl from Ipanema". Method: The famous song was used as a case study to illustrate the possibilities of the usage of a knowledge graph and metadata (authority and bibliographic data) enrichment. Results: By applying Linked Data principles, the study demonstrates how metadata enrichment can improve access and discoverability in library systems. This is achieved by including International Resource Identifiers (IRIs), the utilization of external sources such as the Virtual International Authority File (VIAF), and the Last.fm application programming interface (API). In addition, a knowledge graph was introduced as a tool for providing structured relationships with bibliographic entities within a catalog. Conclusions: The Last.fm open API is used to enrich metadata, offer track listings, and add album art to search results, thus enhancing user experience and information retrieval. The case study includes a knowledge graph in the online library catalog.

This paper shows a method for building and publishing datasets in commercial services. Datasets contribute to the development of research in machine learning and recommender systems. In particular, because recommender systems play a central role in many commercial services, publishing datasets from the services are in great demand from the recommender system community. However, the publication of datasets by commercial services may have some business risks to those companies. To publish a dataset, this must be approved by a business manager of the service. Because many business managers are not specialists in machine learning or recommender systems, the researchers are responsible for explaining to them the risks and benefits. We first summarize three challenges in building datasets from commercial services: (1) anonymize the business metrics, (2) maintain fairness, and (3) reduce the popularity bias. Then, we formulate the problem of building and publishing datasets as an optimization problem that seeks the sampling weight of users, where the challenges are encoded as appropriate loss functions. We applied our method to build datasets from the raw data of our real-world mobile news delivery service. The raw data has more than 1,000,000 users with 100,000,000 interactions. Each dataset was built in less than 10 minutes. We discussed the properties of our method by checking the statistics of the datasets and the performances of typical recommender system algorithms.

Using Citation Analysis Techniques for Computer-Assisted Legal Research in Continental Jurisdictions

Advances in Web 2.0 technologies have led to unstructured information overload in the complex and multidimensional datasets that originate in social networks. This overload can diminish the quality of web-related services such as recommender systems in the social web and can overwhelm users with irrelevant information. Such overload indicates the need to design and develop efficient and accurate information management and retrieval systems in social networks and anticipate recommendations in the semantic context of user interest. Community detection in social networks can help identify higher-order structures that unveil insight into networks and their functional organization and reduce irrelevant information received by the user. In this study, the application of community detection in online social networks is investigated within the framework of topic discovery-based user link identification and is consequently used to uncover implicit user communities (ad hoc communities). In the proposed approach, we use a graph-based information extraction technique that provides for a personalized information retrieval (recommender) system. We hypothesize that the ad hoc communities of users sharing similar interests embedded in a folksonomy-based social network can be identified by overlapping tag clusters in the tag concept hierarchy. Our methodology incorporates the novel information extraction techniques of topic modeling for topic extraction (feature extraction), user profile modeling for user profile extraction, and community extraction from the social graph, modeled in a framework to derive relevant ad hoc user communities of social interest from folksonomy data. Our experimental results demonstrate an accuracy of 70–98 % in community detection using data obtained from CiteULike®.

Evaluating the effectiveness of visual user interfaces for information retrieval

In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing tasks, such as language modelling and machine translation. This suggests that neural models will also achieve good performance on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using a semantic rather than lexical matching. Although initial iterations of neural models do not outperform traditional lexical-matching baselines, the level of interest and effort in this area is increasing, potentially leading to a breakthrough. The popularity of the recent SIGIR 2016 workshop on Neural Information Retrieval provides evidence to the growing interest in neural models for IR. While recent tutorials have covered some aspects of deep learning for retrieval tasks, there is a significant scope for organizing a tutorial that focuses on the fundamentals of representation learning for text retrieval. The goal of this tutorial will be to introduce state-of-the-art neural embedding models and bridge the gap between these neural models with early representation learning approaches in IR (e.g., LSA). We will discuss some of the key challenges and insights in making these models work in practice, and demonstrate one of the toolsets available to researchers interested in this area.

Data about people are constantly collected and stored to help understand patterns which can then be leveraged by information retrieval systems (IRS), such as search engines or recommender systems, to identify and rank resources that respond to diverse users' needs. As a significant group of technology users, children's data are also collected for IRS. In the 4th edition of our proposed workshop, we seek to continue to build professional community connected to children's IRS and expand on the framework identified in the 3rd KidRec that outlines how to evaluate good IRS. This time, we are particularly interested in exploring how design, research, and practice perspectives can cohesively define policy in this area.

The development and evaluation of Information Retrieval and Recommender Systems has traditionally focused on the relevance and accuracy of retrieved documents and recommendations, respectively. However, there is an increasing realization that accuracy alone might be a sub-optimal strategy for a successful user experience. Properties such as novelty and diversity have been explored in both fields for assessing and enhancing the usefulness of search results and recommendations. In this doctoral research we study the assessment and enhancement of both properties in the confluence of Information Retrieval and Recommender Systems.

This report documents the program and the outcomes of Dagstuhl Seminar 23031 "Frontiers of Information Access Experimentation for Research and Education", which brought together 38 participants from 12 countries. The seminar addressed technology-enhanced information access (information retrieval, recommender systems, natural language processing) and specifically focused on developing more responsible experimental practices leading to more valid results, both for research as well as for scientific education. The seminar featured a series of long and short talks delivered by participants, who helped in setting a common ground and in letting emerge topics of interest to be explored as the main output of the seminar. This led to the definition of five groups which investigated challenges, opportunities, and next steps in the following areas: reality check, i.e. conducting real-world studies, human-machine-collaborative relevance judgment frameworks, overcoming methodological challenges in information retrieval and recommender systems through awareness and education, results-blind reviewing, and guidance for authors. Date: 15--20 January 2023. Website: https://www.dagstuhl.de/23031.

The impact of the World Wide Web goes beyond communicating between computers. The web changed the way we interact with organizations, people, documents, and data. Social media is one of the new buzzwords of the web. It is a comparatively new term that covers a wide range of online applications, platforms, and media that support online interactions, collaboration, and the sharing of content. It includes all that constitutes human interaction in online platforms. Social media has had grown explosively in a short time and has a profound impact on advertising, publicity, marketing, public opinion, entertainment, software services, and decision-making. The content offered in social networks is exciting to users. However, it is challenging, as users need to spend time and effort finding content that might be of their interest. Navigating such an extensive collection of items becomes difficult if there are no tools to help the users. To overcome this problem, social media relies on data mining solutions such as recommender systems and information retrieval systems. In particular, recommender systems have found applications in many domains including movies, music, videos, news, books, and products in general. Depending on the context they produce a list of recommended items using a variety of techniques. The goal of this dissertation is to research new approaches in recommender systems that help to satisfy the needs of users in social media. Specifically, we examine the role of recommender systems for users in two types of social media, Location-Based Social Networks and academic social networks. LBSNs enable their users to share the places they go to and with whom they are. Academic social networks help modern researchers organize their scientific libraries and discover relevant papers to their research. For both types of social media most of the existing work focuses on recommendations for individual users. The proposed approaches are distinguishable from others in that we focus on providing recommendations to a group of users, rather than to individuals. Moreover, we investigate the importance of item-to-item recommendations and propose a new method for recommending on infrequent items.

Online forums enable users to discuss together around various topics. One of the serious problems of these environments is high volume of discussions and thus information overload problem. Unfortunately without considering the users interests, traditional Information Retrieval (IR) techniques are not able to solve the problem. Therefore, employment of a Recommender System (RS) that could suggest favorite's topics of users according to their tastes could increases the dynamism of forum and prevent the users from duplicate posts. In addition, consideration of semantics can be useful for increasing the performance of IR based RS. Our goal is study of impact of ontology and data mining techniques on improving of content-based RS. For this purpose, at first, three type of ontologies will be constructed from the domain corpus with utilization of text mining, Natural Language Processing (NLP) and Wordnet and then they will be used as an input in two kind of RS: one, fully ontology-based and one with enriching the user profile vector with ontology in vector space model (VSM) (proposed method). Afterward the results will be compared with the simple VSM based RS. Given results show that the proposed RS presents the highest performance.

This paper describes a course on database construction which is given at the Department of Library and Information Studies, Faculty of Information and Communication, General Polytechnic of Amsterdam. The course is based on the results of an experimen

Numerous widely publicized cases of theft and misuse of private information underscore the need for audit technology to identify the sources of unauthorized disclosure. We present an auditing methodology that ranks potential disclosure sources according to their proximity to the leaked records. Given a sensitive table that contains the disclosed data, our methodology prioritizes by relevance the past queries to the database that could have potentially been used to produce the sensitive table. We provide three conceptually different measures of proximity between the sensitive table and a query result. One measure is inspired by information retrieval in text processing, another is based on statistical record linkage, and the third computes the derivation probability of the sensitive table in a tree-based generative model. We also analyze the characteristics of the three measures and the corresponding ranking algorithms.

Purpose – The purpose of this paper is not only to design a more effective recommendation system for libraries, but also to eliminate many of the weaknesses found in the existing library recommender systems.Design/methodology/approach – A novel library recommender system was designed for English collections by integrating personal ontology model and collaborative filtering model with domain specification.Findings – The trend of the traditional library is evolving toward that of digital library. The personal ontology recommender (PORE) system offers a friendly user interface and provides several personalized services.Research limitations/implications – This system is only implemented and tested in the Library of National Chung Hsing University in Taiwan.Originality/value – The paper demonstrates a good methodology to offer an active, effective, and personalized recommendation system for library patrons.