Leveraging user interaction and collaboration for improving multilingual information access in digital libraries

Digital libraries remove physical barriers to accessing information, but the language barrier still remains due to multilingual collections and the linguistic diversity of users. This study aims at understanding the effect of users’ language skills and field of knowledge on their language preferences when searching for information online and to provide new insights on the access to multilingual digital libraries. Both quantitative and qualitative data were gathered using a questionnaire and results show that the language skills and the field of knowledge have an impact on the language choice for searching online. These factors also determine the interest in cross-language information retrieval: language-related fields constitute the best potential group of users, followed by the Arts and Humanities and the Social Sciences.

This paper presents an iterative and interactive information retrieval (IR) system for Web search using formal concept analysis (FCA). FCA provides a natural way to organise objects according to their properties and it has been used in recent work to organise search engine results. The navigation over the lattice helps the user to explore a structured and synthetic result. Such a lattice contains concepts that are relevant and others that are not relevant regarding a given IR task. In this way, lattices are introduced in an interactive and iterative system. The user expresses his negative or positive agreement with some concept of the lattice in respect of his objective of IR. These user choices are converted into operations over the lattice. The lattice is dynamically updated for a better fit to the request.

This review examines a sample of interactive information retrieval (IR) systems within the framework of a design challenge set forth by John Bennett in 1971. Bennett directed his challenge to a group of information scientists who attended The User Interface for Interactive Search of Bibliographic Data Bases Workshop. During the workshop, participants exchanged their insights and experiences on various aspects of user interface requirements for interactive IR systems and discussed system design features that could result in the design of more effective user interfaces for information retrieval. The main goals of this review are to characterize interactive IR design trends over time, examine the extent to which IR systems have responded to Bennett's design challenge, and to suggest potential future research directions and challenges.

Since Information Retrieval (IR) is an interactive process in general, it is important to study Interactive Information Retrieval (IIR), where we would attempt to model and optimize an entire interactive retrieval process (rather than a single query) with consideration of many different ways a user can potentially interact with a search engine. This tutorial systematically reviews the progress of research in IIR with an emphasis on the most recent progress in the development of models, algorithms, and evaluation strategies for IIR. It starts with a broad overview of research in IIR and then gives an introduction to formal models for IIR using a cooperative game framework and covering decision-theoretic models such as the Interface Card Model and Probability Ranking Principle for IIR. Next, it provides a review of some representative specific techniques and algorithms for IIR, such as various forms of feedback techniques and diversification of search results, followed by a discussion of how an IIR system should be evaluated and multiple strategies proposed recently for evaluating IIR using user simulation. The tutorial ends with a brief discussion of the major open challenges in IIR and some of the most promising future research directions.

Explicit and implicit feedback exhibits different characteristics of users' preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user preference model presents a number of challenges but can also overcome the problems associated with each other. In order to build an effective RS on combination of both types of feedback, we need to have comparative data allowing an understanding of the computation of user preferences. In this paper, we provide an overview of the differentiating characteristics of explicit and implicit feedback using datasets mined from Last.fm, an online music station and recommender service. The datasets consisted of explicit positive feedback (by loving tracks) and implicit feedback which is inherently positive (the number of times a track is played). Rather than relying on just one type of feedback, we present techniques for extracting user preferences from both. In order to compare and contrast the performances of these techniques, we carried out experiments using the Taste recommender system engine and the Last.fm datasets. Our results show that implicit and explicit positive feedback complements each other, with similar performances despite their different characteristics.

Purpose of Article. The study aims to analyse and summarize existing experience and technologies of the implementation strategies of library (international and national) digital projects. Methodology. The research methodology is a systematic approach to identifying and specifying technological basic requirements to create and organize library digital resources in accordance with international requirements and standards. Scientific Novelty. The scientific novelty is to create united technical and technological approaches to digitization of library collections, providing access to online digital library, digital resources integration to the international digital library projects. Conclusions. The large-scale digital library projects are implemented based on corporate involvement institutions of national memory in addition to libraries. The realization of such projects requires coordination and resolve a number of technical issues and developing common standards. The legal issues of access to digital library resources are recommended to decide on the basis of a reasonable balance between copyright creators of documents and the rights of users to access digital resources.

Modern search engines make extensive use of people's contextual information to finesse result rankings. Using a searcher's location provides an especially strong signal for adjusting results for certain classes of queries where people may have clear preference for local results, without explicitly specifying the location in the query direct-ly. However, if the location estimate is inaccurate or searchers want to obtain many results from a particular location, they have limited control on the location focus in the search results returned. In this paper we describe a user study that examines the effect of offering searchers more control over how local preferences are gathered and used. We studied providing users with functionality to offer explicit relevance feedback (ERF) adjacent to results automatically identi-fied as location-dependent (i.e., more from this location). They can use this functionality to indicate whether they are interested in a particular search result and desire more results from that result's location. We compared the ERF system against a baseline (NoERF) that used the same underlying mechanisms to retrieve and rank results, but did not offer ERF support. User performance was as-sessed across 12 experimental participants over 12 location-sensitive topics, in a fully counter-balanced design. We found that participants interacted with ERF frequently, and there were signs that ERF has the potential to improve success rates and lead to more efficient searching for location-sensitive search tasks than NoERF.

No abstract available.

In this paper, we present our study and characterisation of explicit and implicit feedback on Last.fm, an online music station and recommender service. The dataset consisted of explicit positive feedback (through loved tracks) and implicit positive feedback (the number of times a track is played). As one would expect, our analysis shows that explicit feedback is very scarce. However, we also found that the rate at which a user provides explicit feedback decreases with time, and that overall leaving explicit feedback has a negative effect on the user's behaviour.

Digital collections are increasingly used for a variety of purposes. In Europe only, we can conservatively estimate that tens of thousands of users consult digital libraries daily. The usages are often motivated by qualitative and quantitative research. However, caution must be advised as most digitized documents are indexed through their OCRed version, which is far from perfect, especially for ancient documents. In this paper, we aim to estimate the impact of OCR errors on the use of a major online platform: The Gallica digital library from the National Library of France. It accounts for more than 100M OCRed documents and receives 80M search queries every year. In this context, we introduce two main contributions. First, an original corpus of OCRed documents composed of 12M characters along with the corresponding gold standard is presented and provided, with an equal share of English- and French-written documents. Next, statistics on OCR errors have been computed thanks to a novel alignment method introduced in this paper. Making use of all the user queries submitted to the Gallica portal over 4 months, we take advantage of our error model to propose an indicator for predicting the relative risk that queried terms mismatch targeted resources due to OCR errors, underlining the critical extent to which OCR quality impacts on digital library access.

The two main tasks addressed by collaborative filtering approaches are rating prediction and ranking. Rating prediction models leverage explicit feedback (e.g. ratings), and aim to estimate the rating a user would assign to an unseen item. In contrast, ranking models leverage implicit feedback (e.g. clicks) in order to provide the user with a personalized ranked list of recommended items. Several previous approaches have been proposed that learn from both explicit and implicit feedback to optimize the task of ranking or rating prediction at the level of recommendation algorithm. Yet we argue that these two tasks are not completely separate, but are part of a unified process: a user first interacts with a set of items and then might decide to provide explicit feedback on a subset of items. We propose to bridge the gap between the tasks of rating prediction and ranking through the use of a novel weak supervision approach that unifies both explicit and implicit feedback datasets. The key aspects of the proposed model is that (1) it is applied at the level of data pre-processing and (2) it increases the representation of less popular items in recommendations while maintaining reasonable recommendation performance. Our experimental results - on six datasets covering different types of heterogeneous user's interactions and using a wide range of evaluation metrics - show that, our proposed approach can effectively combine explicit and implicit feedback and improve the effectiveness of the baseline explicit model on the ranking task by covering a broader range of long-tail items.

Videotex system is an interactive two-way information retrieval system providing a host of information from its vast database via the telephone network. It allows the subscriber to request information by dialling up the videotex computer; select pages of text and pictorial information and have them displayed on the screens of adapted home television sets.

A general-purpose, interactive computerized information retrieval system specially suitable for the retrieval of bibliographic information from a small scale database. (DB) containing up to 15000 records is described. The user defines fields in terms of tags through the data definition processor which provides flexibility for the record structure. Another processor creates the DB from an off-line input file containing the tag(s) followed by the data for the field(s) supplied by the user. Retrieval of information is carried out by the query processor in an interactive manner. It provides options to (i) select a descriptor (the postings are displayed; (ii) combine 2 sets at a time; (iii) display the different sets chosen; and iv) print the chosen set(s) with full record details or specified tag fields only. The package is developed on an Usha-Alpha 1042 system using Alpha BASIC language under AMOS operating system.

Cross lingual Information Retrieval (CLIR) refers to the information retrieval activities in which the query and/or documents may appear in different languages. Dictionary-based query translation has been a common method in CLIR systems. In these methods we face with the problem of translation ambiguity in which a single word in one language has more than one translation in the other language. In this paper we propose a hybrid approach to retrieve English documents relevant to Persian queries. In this approach we exploit a combination of phrase reorganization, pattern based phrase translation and query expansion before and after translation to improve the dictionary-based query translation. We also propose an improved probabilistic algorithm to choose the best translation of words and phrases. Finally, the documents will be ranked according to statistical language model with some translation steps. Our experimental results show that each of the mentioned methods can bring significant improvement over simple dictionary approaches.

We report early results of our investigation of help-seeking behaviors during interactive information retrieval, with specific attention to one aspect of context, affective utterances made by searchers during the process of completing an assigned search task Searchers' utterances were coded as positive and negative assessments of the results of using automatic Help functionalities in two digital libraries. Although preliminary at this time, data presented in this paper indicate that affective utterances can be mapped to specific help-seeking events and therefore can be construed as important aspects of contextual feedback, worthy of further exploration.