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Measuring the similarity between two resources is considered difficult due to a lack of reliable information and a wide variety of available information regarding the resources. Many approaches have been devised to tackle such difficulty. Although content-based approaches, which adopted resource-related data in comparing resources, played a major role in similarity measurement methodology, the lack of semantic insight on the data may leave these approaches imperfect. The purpose of this paper is to incorporate data semantics into the measuring process.

The emerged linked open data (LOD) provide a practical solution to tackle such difficulty. Common methodologies consuming LOD mainly focused on using link attributes that provide some sort of semantic relations between data. In this work, methods for measuring semantic distances between resources using information gathered from LOD were proposed. Such distances were then applied to music recommendation, focusing on the effect of various weight and level settings.

This work conducted experiments using the MusicBrainz dataset and evaluated the proposed schemes for the plausibility of LOD on music recommendation. The experimental result shows that the proposed methods electively improved classic approaches for both linked data semantic distance (LDSD) and PathSim methods by 47 and 9.7%, respectively.

The main contribution of this work is to develop novel schemes for incorporating knowledge from LOD. Two types of knowledge, namely attribute and path, were derived and incorporated into similarity measurements. Such knowledge may reflect the relationships between resources in a semantic manner since the links in LOD carry much semantic information regarding connecting resources.

Social robot, a subtype of robots that is designed for the various interactive services for human, which must deliver superior user experience (UX) by expressing human-like social behavior or service and emotional sensitivity. This study develops a social robot app called the “Music Buddy” in ASUS Zenbo that provides a situational music based on the users' electroencephalogram (EEG) data. The research uses this app to explore its UX criteria and the prioritization of human robot interaction (HRI).

The research methodologies include the both system development and decision analysis for the social robot. The first part is to design and develop a social robot app. The second part is to investigate the criteria of HRI through the Analytic Hierarchy Process (AHP) from UX aspects.

In view of the results of the AHP, the first-layer criteria consist of personalized function, easy-to-use the system and intelligent process. In terms of prioritization of multi-criteria, the overall ranking discloses the nine criteria in order including autonomy for robot, easy-to-use EEG device, accurate music preference, simple operations for brainwave device and easy-to-use applications, active music recommendation, automatic updates of music and easy-to-use robot as well as fast detection for emotion.

This research includes a self-developed social robot app and its UX research using AHP. This paper contributes to the improvement and innovation of the social robot design according to the results of UX research on HRI of social robot.

Recommender agents are used to make recommendations of interesting items in a wide variety of application domains, such as web page recommendation, music, e‐commerce, movie recommendation, tourism, restaurant recommendation, among others. Despite the various and different domains in which recommender agents are used and the variety of approaches they use to represent user interests and make recommendations, there is some functionality that is common to all of them, such as user model management and recommendation of interesting items. This paper aims at generalizing these common behaviors into a framework that enables developers to reuse recommender agents' main characteristics in their own developments.

This work presents a framework for recommendation that provides the control structures, the data structures and a set of algorithms and metrics for different recommendation methods. The proposed framework acts as the base design for recommender agents or applications that want to add the already modeled and implemented capabilities to their own functionality. In contrast with other proposals, this framework is designed to enable the integration of diverse user models, such as demographic, content‐based and item‐based. In addition to the different implementations provided for these components, new algorithms and user model representations can be easily added to the proposed approach. Thus, personal agents originally designed to assist a single user can reuse the behavior implemented in the framework to expand their recommendation strategies.

The paper describes three different recommender agents built by materializing the proposed framework: a movie recommender agent, a tourism recommender agent, and a web page recommender agent. Each agent uses a different recommendation approach. PersonalSearcher, an agent originally designed to suggest interesting web pages to a user, was extended to collaboratively assist a group of users using content‐based algorithms. MovieRecommender recommends interesting movies using an item‐based approach and Traveller suggests holiday packages using demographic user models. Findings encountered during the development of these agents and their empirical evaluation are described here.

The advantages of the proposed framework are twofold. On the one hand, the functionality provided by the framework enables the development of recommender agents without the need for implementing its whole set of capabilities from scratch. The main processes and data structures of recommender agents are already implemented. On the other hand, already existing agents can be enhanced by incorporating the functionality provided by the recommendation framework in order to act collaboratively.

Social question and answer (social Q&A) sites have become a popular tool for obtaining music information. The purpose of this paper is to investigate what users ask about, what experience the questions convey, and how users specify their questions.

A total of 3,897 music questions from the social Q&A site Yahoo! Answers were categorized according to their question type, user experience, and question specification.

The music questions were diverse with (dis)approval (42 percent), factual (21 percent), and advice (15 percent) questions as the most frequent types. Advice questions were the longest and roughly twice as long as (dis)approval and factual questions. The user experience associated with the questions was most often pragmatic (24 percent) or senso-emotional (12 percent). Pragmatic questions were typically about the user’s own performance of music, while senso-emotional questions were about finding music for listening. Notably, half of the questions did not convey information about the user experience but the absence of such information did not reduce the number of answers. In specifying the questions, the most frequent information was about the music context and the user context.

This study suggests a division of labor between social Q&A sites and search engines for music information retrieval. It should be noted that the study is restricted to one social Q&A site.

Social Q&A sites provide an opportunity for studying what information real users seek about music and what information they specify to retrieve it, thereby elucidating the role of social Q&A in music information seeking.

The purpose of this paper is to propose an approach to incorporate contextual information into collaborative filtering (CF) based on the restricted Boltzmann machine (RBM) and deep belief networks (DBNs). Traditionally, neither the RBM nor its derivative model has been applied to modeling contextual information. In this work, the authors analyze the RBM and explore how to utilize a user’s occupation information to enhance recommendation accuracy.

The proposed approach is based on the RBM. The authors employ user occupation information as a context to design a context-aware RBM and stack the context-aware RBM to construct DBNs for recommendations.

The experiments on the MovieLens data sets show that the user occupation-aware RBM outperforms other CF models, and combinations of different context-aware models by mutual information can obtain better accuracy. Moreover, the context-aware DBNs model is superior to baseline methods, indicating that deep networks have more qualifications for extracting preference features.

To improve recommendation accuracy through modeling contextual information, the authors propose context-aware CF approaches based on the RBM. Additionally, the authors attempt to introduce hybrid weights based on information entropy to combine context-aware models. Furthermore, the authors stack the RBM to construct a context-aware multilayer network model. The results of the experiments not only convey that the context-aware RBM has potential in terms of contextual information but also demonstrate that the combination method, the hybrid recommendation and the multilayer neural network extension have significant benefits for the recommendation quality.

The purpose of this paper is to address the scalability issue and produce high-quality recommendation that best matches the user’s current preference in the dynamically growing datasets in the context of memory-based collaborative filtering methods using temporal information.

The proposed method is formalized as time-dependent collaborative filtering method. For each item, a set of influential neighbors is identified by using the truncated version of similarity computation based on the timestamp. Then, recent n transactions are used to generate the recommendation that reflect the recent preference of the active user. The proposed method, lazy collaborative filtering with dynamic neighborhoods (LCFDN), is further scaled up by implementing in spark using parallel processing paradigm MapReduce. The experiments conducted on MovieLens dataset reveal that LCFDN implemented on MapReduce is more efficient and achieves good performance than the existing methods.

The results of the experimental study clearly show that not all ratings provide valuable information. Recommendation system based on LCFDN increases the efficiency of predictions by selecting the most influential neighbors based on the temporal information. The pruning of the recent transactions of the user also addresses the user’s preference drifts and is more scalable when compared to state-of-art methods.

In the proposed method, LCFDN, the neighborhood space is dynamically adjusted based on the temporal information. In addition, the LCFDN also determines the user’s current interest based on the recent preference or purchase details. This method is designed to continuously track the user’s preference with the growing dataset which makes it suitable to be implemented in the e-commerce industry. Compared with the state-of-art methods, this method provides high-quality recommendation with good efficiency.

The LCFDN is an extension of collaborative filtering with temporal information used as context. The dynamic nature of data and user’s preference drifts are addressed in the proposed method by dynamically adapting the neighbors. To improve the scalability, the proposed method is implemented in big data environment using MapReduce. The proposed recommendation system provides greater prediction accuracy than the traditional recommender systems.

This study aims to create an idea and a framework to enhance customer stickiness and improve transformation efficiency flow of tourism products from online to offline platforms through the application of personalized recommendation technology.

Studies on an overview of progress in current personalized recommendation research, business scenario analysis of online tourism and some possible logical limitations discussion are required for improvement. This study clarifies concepts including online tourism user behavior and generated data, user preference themes and spaces, user models and image and user-product (two-dimensional matrix, etc.). The author then creates a user portrait based on behavior data convergence to locate the user's role from both horizontal and vertical dimensions and also clear the logical levels and associations among them, verifying the similarity in measurement and calculation and optimizing the implementation of the personalized recommendation program under online tourism business scenarios.

By providing a framework design about personalized recommendations of online tourism including a flow from data collection to a personalized recommendation algorithm selection, logical analysis is established while the corresponding personalization algorithm is improved.

This study show a logical shift of personalized recommendations in online tourism management from focusing on the simple collection of travel information and the logical speculation of tourism products to focusing on the individual behavior of potential travelers.

The purpose of this paper is to introduce and examine algorithmic culture and consider the implications of algorithms for information literacy practice. The questions for information literacy scholars and educators are how can one understand the impact of algorithms on agency and performativity, and how can one address and plan for it in their educational and instructional practices?

In this study, algorithmic culture and implications for information literacy are conceptualised from a sociocultural perspective.

To understand the multiplicity and entanglement of algorithmic culture in everyday lives requires information literacy practice that encourages deeper examination of the relationship among the epistemic views, practical usages and performative consequences of algorithmic culture. Without trying to conflate the role of the information sciences, this approach opens new avenues of research, teaching and more focused attention on information literacy as a sustainable practice.

The concept of algorithmic culture is introduced and explored in relation to information literacy and its literacies.

The paper aims to understand the book subscription characteristics of the students at each college and help the library administrators to conduct efficient library management plans for books in the library. Unlike the traditional association rule mining (ARM) techniques which mine patterns from a single data set, this paper proposes a model, recency-frequency-college (RFC) model, to analyse book subscription characteristics of library users and then discovers interesting association rules from equivalence-class RFC segments.

A framework which integrates the RFC model and ARM technique is proposed to analyse book subscription characteristics of library users. First, the author applies the RFC model to determine library users’ RFC values. After that, the author clusters library users’ transactions into several RFC segments by their RFC values. Finally, the author discovers RFC association rules and analyses book subscription characteristics of RFC segments (library users).

The paper provides experimental results from the survey data. It shows that the precision of the frequent itemsets discovered by the proposed RFC model outperforms the traditional approach in predicting library user subscription itemsets in the following time periods. Besides, the proposed approach can discover interesting and valuable patterns from library book circulation transactions.

Because RFC thresholds were assigned based on expert opinion in this paper, it is an acquisition bottleneck. Therefore, researchers are encouraged to automatically infer the RFC thresholds from the library book circulation transactions.

The paper includes implications for the library administrators in conducting library book management plans for different library users.

This paper proposes a model, the RFC model, to analyse book subscription characteristics of library users.

The purpose of this paper is to study the users’ willingness for acceptance of background music service in university libraries based on intelligent campus and to improve the intelligence level of university libraries and provide a reference for the atmosphere.

The research method of combining theory with practice is applied, and field distribution method and network survey method are used. An algorithm model is established to investigate relevant users, and statistical analysis of the data obtained is made.

The results show that in the questionnaire survey, girls are more inclined to study in the environment of library than boys; for grade, sophomores and juniors are more inclined to go to library than other grades; through model analysis, the target users are more inclined to choose light music as background music, accounting for 65 percent. Heavy metals and other users have fewer choices, accounting for only 8 percent.

This questionnaire is mainly filled out in paper form on site, so it only selects some nearby students as the survey objects. However, the condition of university libraries across the country must be different due to regional differences, disciplinary differences and funding differences, so the representative sample may be insufficient. Therefore, in the follow-up research, the scope of the survey should be expanded, especially the geographical scope. It should collect as much data as possible for students of different types and genders, so as to expand the applicable scope and explanatory power of the model.

Starting from the library scene, this research studies the acceptance intention of users of background music service in the library, which provides reference for the improvement of the intellectualization of university libraries and their atmosphere. Although different university libraries have different operation modes and service characteristics, the conclusions of this study have certain practical significance for the library industry because the library industry has many commonalities.

At present, the research on the background music service of University Library based on the smart campus is relatively rare and limited to the theoretical stage. Few people have deeply explored the background music service of the library, and no scholars have quantitatively studied it. In this study, based on the questionnaire and from the perspective of users, the acceptance intention of background music service users is investigated, which provides a reference for the improvement of the intellectualization of university libraries and its atmosphere. It is a research topic of great practical significance.