Search

Problem Statement: The author focuses on the problem of decreasing the number of Library Department of IPDN visitors in 2018. Purpose: This study is focused on answering how the satisfaction of IPDN library users. Method: The author collected data through a questionnaire distributed to 51 respondents in the Praja category. Results: The results showed that the dimensions of collection types, completeness of collections, book search on the shelves, time of visit and borrowing time for library collections needed improvement, while in the dimensions of collection search tools, easiness of library users to obtain information, attitude and behavior of librarians needed improvement. Conclusion: Library users were satisfied with the implementation of the library, especially in the dimensions of the attitude and behavior of librarians, search tools collection and ease of information search. The author suggests that IPDN Libraries need to survey the needs of the type of library collections that are needed by users, update written regulations relating to library service hours, increase lending time for library collections that can be borrowed and brought home, and set shelving schedules on a regular and regular basis.

Prior work on personalizing web search results has focused on consideringquery-and-click logs to capture users individual interests. For product search,extensive user histories about purchases and ratings have been exploited.However, for general entity search, such as for books on specific topics ortravel destinations with certain features, personalization is largelyunderexplored. In this paper, we address personalization of book search, as anexemplary case of entity search, by exploiting sparse user profiles obtainedthrough online questionnaires. We devise and compare a variety of re-rankingmethods based on language models or neural learning. Our experiments show thateven very sparse information about individuals can enhance the effectiveness ofthe search results.

Book shelving and locating is a highly useful book management method, which could shorten the book search time of readers and improve the efficiency of book management. Focusing on the application of book locating technology in the library shelving management of Wuhan University of Technology, this paper explores how different book locating methods bring convenience to the book management of library in universities and colleges. The practical experience reveals the improvement of reader satisfaction and management efficiency by selecting a suitable locating method for books in terms of management mode and circulation.

The proposed work has retrieved tweets on a particular disease and treatment combination from twitter and they were processed to extract the sentiments. Initially the polarity values were set up in a range from weakly negative to strongly positive and the tweets were analyzed. The overall sentiment of the tweets related to breast cancer and chemotherapy was weakly positive. Naive Bayes algorithm was applied on the tweets retrieved on the same disease and treatment combination. Nearly 10,000 tweets were analyzed using the Pubmed and Google book search engine as a training corpus. The sentiments were plotted in the graph which shows that the sentiments were neutral. Lastly, to find the most occurred word in the tweets, bigrams were used and co-occurrence of words were plotted using Natural Language Tool Kit in python.

The library of Faculty of Mathematics and Natural Science (FMIPA) has a collection of books and other print media, total of 2,678 books with 7237 visitors and 2148 borrowers. The available book search system was very helpful for visitors to find the required books. Especially if the system has features recommended of books. In the provision of book recommendations used one of the data mining techniques, namely association rule mining techniques or excavation of association rules. In the development of this recommendation system, KDD (Knowledge Discovery from Database) model was used. The data used was the transaction history of borrowing book with the category of "chemistry", for the last 5 (five) months, that is September 2014 - February 2015. The excavation technique of this association rule has 2 (two) main process, they are: frequent patterns and rules. To find frequent patterns, a CT-PRO algorithm was used. The minimum value of support used was 1 and 2. Once the pattern is found, the confidence value of each pattern was calculated. The minimum value of confidence used ranges from 10% to 100%. The recommendation rule was based on calculating the value of this confidence. The comparison of minimum support values indicates that the greater value of minimum support then the less borrowing pattern was generated, and vice versa. The comparison of minimum confidence value shows that the greater of minimum confidence value then the less recommended rule given.. Keywords: Library, Recommended System, Knowledge Discovery from Database (KDD), Association Rule Mining, CT-PRO Algorithm

Book search is far from a solved problem. Complex information needs often go beyond bibliographic facts and cover a combination of different aspects, such as specific genres or plot elements, engagement or novelty. Conventional book metadata may not be sufficient to address these kinds of information needs. In this paper, we present a large-scale empirical comparison of the effectiveness of book metadata elements for searching complex information needs. Using a test collection of over 2 million book records and over 330 real-world book search requests, we perform a highly controlled and in-depth analysis of topical metadata, comparing controlled vocabularies with social tags. Tags perform better overall in this setting, but controlled vocabulary terms provide complementary information, which will improve a search. We analyze potential underlying factors that contribute to search performance, such as the relevance aspect(s) mentioned in a request or the type of book. In addition, we investigate the possible causes of search failure. We conclude that neither tags nor controlled vocabularies are wholly suited to handling the complex information needs in book search, which means that different approaches to describe topical information in books are needed.

We examined the search behaviors of diverse fiction readers in different search scenarios. The aim was to understand how fiction readers with varied reading preferences are selecting interesting novels in library catalogs. We conducted a controlled user study with 80 participants. Two reader groups were elicited according to similar reading preference patterns. The readers enjoyed the entertainment, escape, and comfort that reading as a pleasurable activity offered. The aesthetic readers valued the artistic and aesthetic pleasures, widening vocabulary, and ability to express oneself through fiction books. We compared the search queries and search actions between the 2 reader groups. Our results demonstrated that preference patterns were associated with readers' search behavior, that is, the number of viewed search result pages, opened book pages, dwell time on book pages, and the type of search queries. Based on the findings, we present 3 search tactics for fiction books in library catalogs: i) focused querying, ii) topical browsing, and iii) similarity-based tactic. The most popular search tactic in each search scenario was “focused querying” with known author in both reader groups.

In this paper, we propose a ranking method for keyword-based book search systems. Because we do not have full text data of all books, we use a database of brief descriptions of books. Such a brief description of the target book may not include all the query keywords given by the user. In addition, a query formulated by a user based on a vague memory may include wrong keywords. In both cases, the target book does not match with the query. To solve that problem, our method ranks candidate books in two steps. We first generate relaxed queries by removing some keywords from the given original query, and rank the queries based on how likely the remaining keywords are to appear in the brief descriptions. In the second step, we rank the results of each query. For each query, we retrieve matching books, find words in the description of each book that are most likely to be mistaken for the removed keywords by the user, and rank the books based on how likely they are mistaken for the removed keywords. By combining these two rankings, i.e., the ranking of relaxed queries, and the ranking of books matching with each query, we produce the final ranking. In this paper, we focus on the ranking method for the second step. Our experiment shows that our method improves ranking for some queries where the original query includes many keywords that do not appear in the description of the target book.

This paper describes our experiences with the interactive IR tracks organized at CLEF from 2013-2016 and aggregates the lessons learned with each consecutive instance of the lab. We end with a summary of practical insights and lessons for future collaborative interactive IR evaluation exercises and for potential re-use scenarios.

Purpose – The purpose of this paper is to investigate fiction readers’ interest criteria when selecting novels in library catalogs for various search tasks. Design/methodology/approach – The data of the book selection behavior from 80 genuine fiction readers were collected using recorded interviews and conversations. The data were qualitatively analyzed. Reuter’s categorization of the components of aesthetic relevance has contributed to the construction of interest dimensions. Findings – A five-dimension categorization of interest criteria is presented based on fiction readers’ interpretations of the influential factors in fiction book selection in different search tasks. The findings revealed that readers apply the identified interest criteria in a flexible and multiphase way depending to the search task and the system used. The findings showed a context-related pattern in readers’ fiction book selections. A combination of readers’ search capacities, “behind the eyes” knowledge, affective factors and a well-functioning interaction with a system used results in a successful book selection. Originality/value – A five-dimension categorization of adult fiction readers’ interest criteria was created based on their search behaviors in library catalogs. The results provide a systematic step toward a comprehensive understanding of readers’ fiction book selection in digital environments.

ARKit and ARCore, key technologies in recent augmented reality (AR) development, have allowed AR to become more integrated in our lives. However, how effective AR is in an auxiliary role in venue guidance and how to collect the actual behaviors of users in physical venues are worth exploring. This study used navAR, a spatial behavior analysis system app that our research team developed, to collect the actual behaviors of participants in physical space via a smartphone, such as time, distance travelled, and trajectory, and compared their book-finding behaviors in a library venue in a text scenario and an AR scenario without any additional sensors or cameras. The experiment results revealed that (1) AR targets made a significant difference in book search time, and the participants found some of the books significantly faster, (2) the participants presented no significant differences in distance travelled, (3) with an AR target, the book-finding trajectories of the participants were significantly more regular, (4) the AR guidance system had good usability. The results of this study can facilitate planning with AR in indoor venue routes, improve venue and exhibition tour experiences, and enable AR to be used for crowd flow diversion. Furthermore, this study provides a methodology for future analyses on user behavior in physical spaces.

Books are the source of knowledge and information. Users search relevant books based on their need. In this paper we develop method for book suggestion. In this paper, we combine Sequential Dependence Model with heterogeneous social information such as tags, reviews and ratings. These social informations are assigned by users. Therefore, we use CLEF-2016 Social Book Search track (Suggestion task). We train our proposed method on CLEF-2015 Social Book Search (Suggestions task) datasets and tested on CLEF-2016 Social book Search datasets. We get better result compared to other systems.

Unanswered questions remain regarding how to design search result pages in library catalogues that offer effective library seeking experiences for users – especially those designed for small screen mobile devices. This paper reports a snapshot interview of the user habits and preferences of tertiary library patrons during book searching and browsing and provides recommendations for library catalogue design and further research.

In recent decades, information retrieval research has slowly expanded its focus to address the wealth of complex search requests present in our work and leisure environments. A better understanding of such complex needs could aid in the design of more effective, domain-specific search engines. In this paper we take a first step towards such domain-specific understanding. We present an analysis of a random sample of 1000+ complex book and movie search requests posted in the LibraryThing and Internet Movie Database forums. A coding scheme was developed that captures the 29 different relevance aspects expressed in these requests. We find that while the identified relevance aspects are remarkably similar for complex book and movie requests, their relative occurrence does vary considerably from domain to domain.

In recent decades, information retrieval research has slowly expanded its focus to address the wealth of complex search requests present in our work and leisure environments. A better understanding of such complex needs could aid in the design of more effective, domain-specific search engines. In this paper we take a first step towards such domain-specific understanding. We present an analysis of a random sample of 1000+ complex book and movie search requests posted in the LibraryThing and Internet Movie Database forums. A coding scheme was developed that captures the 29 different relevance aspects expressed in these requests. We find that while the identified relevance aspects are remarkably similar for complex book and movie requests, their relative occurrence does vary considerably from domain to domain.

Technology advancements and availability will result in library catalogues becoming more regularly accessed on small screen mobile devices in coming years with academics and students likely to be amongst the earliest adopters. There remain numerous unanswered questions regarding how to design library catalogues which offer effective library search on mobile devices. This broad research area requires the attention of HCI, design, and reading researchers alike. This paper begins to address the user needs of library patrons when searching for books on a mobile device. Recommendations for mobile library catalogue design and further research is provided.

The purpose of this research is to build a system for searching picture books based on the similarity of story lines. Current systems search for books by referring to "outline" sources that relate to the book, such as bibliographic information; content-based searches are difficult to perform. As a consequence, existing book search systems do not respond to user requests like, "Please show me a picture book that gives me a similar postreading feeling as this picture book." To handle such requests, this paper focuses on the sentiment polarity of words: our proposed method enables similarity search of picture books by measuring transitions in the sentiment polarity of words that appear in a picture book's text. In this method, positive and negative words appearing in the text are extracted, and the similarity between picture books is calculated using Bhattacharyya coefficients. Our experiment reveals that the proposed method finds books similar to the query book in terms of story lines.

Research into recommendation algorithms has made great strides in recent years. However, these algorithms are typically applied in relatively straightforward scenarios: given information about a user's past preferences, what will they like in the future? Recommendation is often more complex: evaluating recommended items never takes place in a vacuum, and it is often a single step in the user's more complex background task. In this paper, we define a specific type of recommendation scenario called narrative-driven recommendation, where the recommendation process is driven by both a log of the user's past transactions as well as a narrative description of their current interest(s). Through an analysis of a set of real-world recommendation narratives from the LibraryThing forums, we demonstrate the uniqueness and richness of this scenario and highlight common patterns and properties of such narratives.

In 2004, Google embarked on a massive book digitization project. Forty library partners and billions of scanned pages later, Google Book Search has provided searchable text access to millions of books. While many details of Google’s conversion processes remain proprietary secret, here we piece together their general outlines by closely examining Google Book Search products, Google patents, and the entanglement of libraries and computer scientists in the longer history of digitization work. We argue that far from simply “scanning” books, Google’s efforts may be characterized as algorithmic digitization, strongly shaped by an equation of digital access with full-text searchability. We explore the consequences of Google’s algorithmic digitization system for what end users ultimately do and do not see, placing these effects in the context of the multiple technical, material, and legal challenges surrounding Google Book Search. By approaching digitization primarily as a text extraction and indexing challenge—an effort to convert print books into electronically searchable data—GBS enacts one possible future for books, in which they are defined largely by their textual content.

Book search for information needs that go beyond standard bibliographic data is far from a solved problem. Such complex information needs often cover a combination of di erent aspects, such as specific genres or plot elements, engagement or novelty. By design, subject information in controlled vocabularies is not always adequate in covering such complex needs, and social tags have been proposed as an alternative. In this paper we present a large-scale empirical comparison and in-depth analysis of the value of controlled vocabularies and tags for book retrieval using a test collection of over 2 million book records and over 330 real-world book information needs. We find that while tags and controlled vocabulary terms provide complementary performance, tags perform better overall. However, this is not due to a popularity e ect; instead, tags are better at matching the language of regular users. Finally, we perform a detailed failure analysis and show, using tags and controlled vocabulary terms, that some request types are inherently more di cult to solve than others.

In this paper we suggest a new class of search games, namely, screening and hiding versus search games. This new class of search games is motivated by the book Search and Screening by Koopman who is the founder of the modern search theory. In our game a hider is not just trying to find the best places to hide resources from a searcher, but also it applies efforts to screen their allocations making it more difficult for the searcher to find them. This hiding and screening strategy of the hider versus search strategy of the searcher makes the plot more real-world relative comparing to widely investigated in literature just hiding versus search games. For the suggested game we found the equilibrium strategies of both players as well as we proved uniqueness of the equilibrium.

英米の書籍(基本書)をもとに研究方法論としての文献レビュー(文献研究)について検討した.インターネットで書籍検索を実施した結果,システマティック・レビューやメタアナリシスなど高度な文献研究のみを扱った書籍を除外すると2012年9月現在,文献レビューに関する英文の書籍は13種類25冊であった.各書籍の最新版をもとに内容を検討し以下の知見を得た.書籍の起源は1998年と推定された.文献レビューは,一定の手順と技術に基づいて実施される,それ自体が独立した研究方法論と言える.優れた文献レビューには網羅性(情報量)と独創性の2側面が要求される.大学院生(学位論文の執筆)では必須の素養に位置付けられる.文献レビューのトレーニングは,文献検索(情報収集)能力,文献の品質評価能力,各種データの統合力,オリジナリティの創出力,学術的作文能力など,研究に必要な基礎的能力を習得する機会として有用だと思われる.(著者抄録)

Searching large collections of digitized books is a relatively new area in information-seeking and retrieval research, made possible by initiatives such as Google Books and the HathiTrust Digital Library. The availability of large full-text book collections is transforming how users search and interact with information in books, but the characteristics of these changes are unknown. This paper aims to provide insight into the characteristics of full-text searches in a large collection of digitized books and is the first step in a broader research agenda intended to improve book retrieval. To better understand the types of queries that users are issuing to full-text-book collections, we analyzed a full year of anonymized query logs from the HathiTrust Digital Library full-text search engine. We also manually classified a random sample of 600 queries to develop a taxonomy of book search query types. We found that users are beginning to search for information in books instead of searching for books. Searches still largely follow bibliographic models, but, as expected, new types of searches are beginning to take advantage of full-text capabilities. Additionally, comparing the results of our query log analysis to searches in other domains, we found similar search patterns including short queries, sessions with only a few queries, and users viewing only a few pages of results per query. We discuss how these findings can be used to characterize users of large full-text book collections.

In this paper, we propose a novel framework using the word2vec model, a deep learning method, integrated with a book ontology in order to enhance semantically searching books. The idea starts from constructing a book ontology for reasoning book information efficiently. A deep learning method, namely the word2vec model, is then utilized to represent vectors of words occurring on book descriptions. These vectors would help finding most relevant books given a query string. The integration of the word2vec model and the book ontology is able to achieve high performance in searching books. A database of Amazon books is taken into account examining the proposed method, compared with an advanced keyword matching method. The experimental results show that the proposed method can produce more accurate searching results.

One way to reduce opportunity loss and help stop the proliferation of bad book reviews is to reduce the gap between expectation before reading and satisfaction after reading. A previously developed method for revising book cover images to reflect reader impressions reduces the gap between the impression obtained from a book cover image and the impression after reading the book. A book search system based on this method has now been constructed that enables users to easily search for books on the basis of impressions after reading as reflected by the revised book cover images. Its ring-structured graphical search interface supports multi-faceted search using simple ring operations. Testing showed that it enables users to easily search for books on the basis of impressions after reading and enhances the opportunity to discover new books during the search process.

In this paper we present an overview of the mining track in the Social Book Search (SBS) lab 2016. The mining track addressed two tasks: (1) classifying forum posts as book search requests, and (2) linking book title mentions in forum posts to unique book IDs in a database. Both tasks are important steps in the process of solving complex search tasks within online reader communities. We prepared two data collections for the classification task: posts from the LibraryThing (LT) forum and a smaller number of posts from Reddit. For the linking task we used annotated LT threads. We found that the classification task was relatively straightforward, achieving up to 94% classification accuracy. The book linking task on the other hand turned out to be a difficult task: here the best system achieved an accuracy of 41% and F-score of 33.5%. Both the automatic classification of book search requests as the automatic linking of book mentions could next year be part of the pipeline for processing complex book searches.

Radio Frequency Identification technology (RFID) is the key technology to realize the Internet of things. Currently, using RFID to locate objects indoor is a hot topic in the research on the application of RFID. In this paper, we propose an RFID system based on RFID technology and Wi-Fi wireless communication technology. And we design a corresponding handheld device client software to realize a visual book search and management system. This system can not only greatly improve the efficiency of book search and management but also save the manpower and material resources to a large extent, which has a practical application value.