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Traditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations.

The authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings.

The authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG.

It requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge.

The embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences.

The proposed method is a practical embedding-driven model that accurately captures diverse user preferences.

Moves towards more interactive services on the web have led libraries to add an increasing range of functionality to their OPACS. Given the prevalence of recommender systems on the wider web, especially in e‐commerce environments, this paper aims to review current research in this area that is of particular relevance to the library community. It attempts to gauge the uptake of recommender systems in exiting OPAC services, and identify issues that might be responsible for inhibiting wider uptake.

This paper draws on an extensive literature review, as well as original research comparing the functionality of 211 public and 118 university library OPACs in the UK. Examining current recommender systems research, it outlines the most significant recommendation models and reviews research in two key areas of recommender systems design: data acquisition, and the explanation of recommendations. It discusses three existing library recommendation systems: BibTip, LibraryThing for Libraries and the in‐house system at the University of Huddersfield.

The authors' analysis indicates that the incorporation of recommender systems into library services is extremely low, with only 2 per cent of public libraries and 11 per cent of university libraries in the UK offering the feature. While system limitations and budget constraints are perhaps partly to blame, it is suggested that library professionals have perhaps yet to be persuaded that the value of recommendations to library users is great enough to warrant their inclusion becoming a priority.

This paper represents the first study of UK library OPACs to focus on the prevalence of recommender systems.

This paper seeks to suggest a model for location‐based recommendation services that enable greater access to print and electronic resources.

The paper takes the form of a synthesis of previous work in basic and applied collections‐based wayfinding incorporating library and information science (LIS) literature on user context and system recommendations.

The paper identifies problems that will need to be solved before implementation of the production‐level recommendation service and suggests possible implications the system may have on reference and instruction services.

The paper provides computing workflows necessary to implement a library recommendation service based on user location. iPhone Software Developer Kit templates are leveraged for modeling data and interface prototypes. Use cases and user models are developed.

It is obvious that when resources are insufficient to meet all legitimate demands, priorities should be established. During the last decade, funds available to academic institutions in English‐speaking countries have been reduced while the information sources on which they depend continue to increase and the changing technology of access provides new challenges for academics and librarians.

The New York Stale Library and the library systems in New York State have a long‐standing commitment to the use of technology to improve services, increase efficiency, and constrain cost increases. In 1986, the Board of Regents of the State Education Department adopted a three‐part program for automation. Pursuant to this program, several important committees have been appointed that have subsequently issued key recommendations. This article addresses the formation and role of the committees and their recommendations covering: general issues, databases, linking, training and consulting, and operational objectives.

This article describes a service for providing literature recommendations, which is part of a networked digital library project whose principal goal is to develop technologies for supporting digital services. The proposed literature recommendation system makes use of the Web usage logs of a literature digital library. The recommendation framework consists of three sequential steps: data preparation of the Web usage log, discovery of article associations, and article recommendations. We discuss several design alternatives for conducting these steps. These alternatives are evaluated using the Web logs of our university’s electronic thesis and dissertation (ETD) system. The proposed literature recommendation system has been incorporated into our university’s ETD system, and is currently operational.

This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation.

The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets.

The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields.

The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future.

The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

The purpose of this paper is to develop a cross‐language personalized recommendation model based on web log mining, which can recommend academic articles, in different languages, to users according to their demands.

The proposed model takes advantage of web log data archived in digital libraries and learns user profiles by means of integration analysis of a user's multiple online behaviors. Moreover, keyword translation was carried out to eliminate language dissimilarity between user and item profiles. Finally, article recommendation can be achieved using various existing algorithms.

The proposed model can recommend articles in different languages to users according to their demands, and the integration analysis of multiple online behaviors can help to better understand a user's interests.

This study has a significant implication for digital libraries in non‐English countries, since English is the most popular language in current academic articles and it is a very common phenomenon for users in these countries to obtain literatures presented by more than one language. Furthermore, this approach is also useful for other text‐based item recommendation systems.

A lot of research work has been done in the personalized recommendation area, but few works have discussed the recommendation problem under multiple linguistic circumstances. This paper deals with cross‐language recommendation and, moreover, the proposed model puts forward an integration analysis method based on multiple online behaviors to understand users' interests, which can provide references for other recommendation systems in the digital age.

New York State has established the goal of enabling all libraries in the state—some 7,000—to become electronic doorway libraries. An electronic doorway library is a library enhanced and transformed by the use of computer and telecommunications technology to provide electronic services for its users.

As any library strives to improve services and make them increasingly relevant, planning for change has become routine. During 2011, the University of Arizona's Libraries undertook extensive assessments in order to develop and improve services in support of research and grant services so that campus-wide achievements in research, scholarship, and creative works could improve. A project explored ways for the library to become more effective at increasing research and grant support to faculty, researchers, and graduate students in a scalable way, and to help the campus increase achievements in research, scholarship, and creative works. The project defined the library's role in research and grant activities and explored ways for the library to be involved at optimal points in these cycles. This chapter discusses the process developed for assessing what new research and grant support services the library might want to develop. This involved interviewing peer university libraries and surveying faculty and graduate students at the University of Arizona about their research and grant needs. The chapter also describes how results were analyzed to identify potential new library services. The project team recommended new services which were presented to the library for inclusion in its Strategic Plan. The methodology presented in this chapter can be used by any type of library for developing new services to include in their strategic plans.