Shared-account cross-domain sequential recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains. It is gaining immense research attention ...as more and more users tend to sign up on different platforms and share accounts with others to access domain-specific services. Existing works on SCSR mainly rely on mining sequential patterns via recurrent neural network (RNN)-based models, which suffer from the following limitations: 1) RNN-based methods overwhelmingly target discovering sequential dependencies in single-user behaviors and they are not expressive enough to capture the relationships among multiple entities in SCSR; 2) all existing methods bridge two domains via knowledge transfer in the latent space and ignore the explicit cross-domain graph structure; and 3) none existing studies consider the time interval information among items, which is essential in the sequential recommendation for characterizing different items and learning discriminative representations for them. In this work, we propose a new graph-based solution, namely, time interval-enhanced domain-aware graph convolutional network (TiDA-GCN), to address the above challenges. Specifically, we first link users and items in each domain as a graph. Then, we devise a domain-aware graph convolution network to learn user-specific node representations. To fully account for users' domain-specific preferences on items, two effective attention mechanisms are further developed to selectively guide the message-passing process. Moreover, to further enhance item-and account-level representation learning, we incorporate the time interval into the message passing and design an account-aware self-attention module for learning items' interactive characteristics. Experiments demonstrate the superiority of our proposed method from various aspects.
Recurrent neural networks (RNN) based recommendation algorithms have been introduced recently as sequence information plays an increasingly important role when modeling user preferences. However, ...these methods have numerous limitations: they usually give undue importance to sequential changes and place insufficient emphasis on the correlation between adjacent items; additionally, they typically ignore the impacts of context information. To address these issues, we propose an attention-based context-aware sequential recommendation model using Gated Recurrent Unit (GRU), abbreviated as ACA-GRU. First, we consider the impact of context information on recommendations and classify them into four categories, including input context, correlation context, static interest context, and transition context. Then, by redefining the update and reset gate of the GRU unit, we calculate the global sequential state transition of the RNN determined by these contexts, to model the dynamics of user interest. Finally, by leveraging the attention mechanism in the correlation context, the model is able to distinguish the importance of each item in the rating sequence. The impact of outliers that are less informative or less predictive decreases or is ignored. Experimental results indicate that ACA-GRU outperforms state-of-the-art context-aware models as well as sequence recommendation algorithms, demonstrating the effectiveness of the proposed model.
•Student attributes, relations and comment are used to construct feature network.•The network provides students’ personalization and their mastery of knowledge.•With network feature, a ...“user-item-personalization” rating tensor is constructed.•The rating tensor is decomposed by tensor factorization methods via learning.•A course recommendation model is proposed based on aforementioned processes.
Course recommendation systems are applied to help students with different needs select courses in a large range of course resources. However, a student’s needs are not always determined by their personal interests, they are also influenced by teachers, peers etc. Unlike online courses, user behavior and user satisfaction of offline courses often have serious sparse and cold start issues, which cause overfitting problems in previous neural network and matrix factorization (MF) models. Additionally, the interpersonal relations, evaluation text and existing “user-item” formatted rating matrix constitute a multi-source and multi-modal data structure, so a systematic data fusion method is needed to establish recommendations based on these heterogeneous characteristics. Therefore, a hybrid recommendation model by fusing network structured feature with graph neural networks and user interactive activities with tensor factorization was proposed in this paper. First, a graph structured teaching evaluation network is proposed to describe students, courses, and other entities by using the students’ rating, commentary text, grading and interpersonal relations. Then, a random walk based neural network is employed to generate the vectorized representation of students by learning their own relational structure. Finally, by recognizing these personalization features as the third dimension of the rating tensor, a Bayesian Probabilistic Tensor Factorization-based tensor factorization is applied to learn and predict students’ ratings for classes they have not taken. Experiments on a real-world evaluation of teaching system including 532 participants with 7,453 rating records show that the proposed method outperforms other existing neural network and matrix factorization models including xSVD++, RTTF and DSE with a smaller predictive error as well as better recommendation accuracy.
•An interactive mood based music recommendation system is developed and evaluated.•A visualization of moods and artists in the same space enables user interaction.•Moods form a hierarchy and help to ...explain the recommendations.•Novel interaction mechanisms allow users to explore and tweak recommendations.•Comprehensive evaluation resulted in empirical evidence for avoiding cognitive load.
A large amount of research in recommender systems focuses on algorithmic accuracy and optimization of ranking metrics. However, recent work has unveiled the importance of other aspects of the recommendation process, including explanation, transparency, control and user experience in general. Building on these aspects, this paper introduces MoodPlay, an interactive music-artists recommender system which integrates content and mood-based filtering in a novel interface. We show how MoodPlay allows the user to explore a music collection by musical mood dimensions, building upon GEMS, a music-specific model of affect, rather than the traditional Circumplex model. We describe system architecture, algorithms, interface and interactions followed by use-case and offline evaluations of the system, providing evidence of the benefits of our model based on similarities between the typical moods found in an artist’s music, for contextual music recommendation. Finally, we present results of a user study (N = 279) in which four versions of the interface are evaluated with varying degrees of visualization and interaction. Results show that our proposed visualization of items and mood information improves user acceptance and understanding of both the underlying data and the recommendations. Furthermore, our analysis reveals the role of mood in music recommendation, considering both artists’ mood and users’ self-reported mood in the user study. Our results and discussion highlight the impact of visual and interactive features in music recommendation, as well as associated human-cognitive limitations. This research also aims to inform the design of future interactive recommendation systems.
Group Recommendation Systems (GRS) is an emerging area in both research and practice and has been successfully developed in many domains as a type of information filter to overcome the information ...overload problem. With the growth of Scientific Social Networks (SSNs), the need for article recommendation is emerging. Considering that researchers can be grouped according to their research interests, and article recommendation to a group of users has not been addressed in the literature, this paper aims to develop and test an inferential model to accurately recommend articles for group researchers in SSNs. In this paper, a novel approach for group article recommendation, referred to as GPRAH_ER, is proposed to improve the processes of both individual prediction and group aggregation. In the stage of individual prediction, the Probabilistic Matrix Factorization method is adopted and is further unified by using articles’ contents and group information. In the stage of group aggregation, the ER rule is introduced in the aggregation process, since it possesses the advantages of identifying group members’ impacts based on the group member’s weight and reliability. To verify the performance of the proposed method, experiments are conducted on a real dataset CiteULike. The experimental results show that the proposed GPRAH_ER method outperforms other benchmark methods, and provides a more effective recommendation of articles to researchers in SSNs.
•Group Recommendation Systems is an emerging area in both research and practice.•A novel approach is proposed for group article recommendation.•Experimental results show GPRAH_ER outperforms other methods.
Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in ...recent years. Due to the great demand for applications, various fashion recommendation tasks, such as personalized fashion product recommendation, complementary (mix-and-match) recommendation, and outfit recommendation, have been posed and explored in the literature. The continuing research attention and advances impel us to look back and in-depth into the field for a better understanding. In this article, we comprehensively review recent research efforts on fashion recommendation from a technological perspective. We first introduce fashion recommendation at a macro level and analyze its characteristics and differences with general recommendation tasks. We then clearly categorize different fashion recommendation efforts into several sub-tasks and focus on each sub-task in terms of its problem formulation, research focus, state-of-the-art methods, and limitations. We also summarize the datasets proposed in the literature for use in fashion recommendation studies to give readers a brief illustration. Finally, we discuss several promising directions for future research in this field. Overall, this survey systematically reviews the development of fashion recommendation research. It also discusses the current limitations and gaps between academic research and the real needs of the fashion industry. In the process, we offer a deep insight into how the fashion industry could benefit from the computational technologies of fashion recommendation.
Bruton's tyrosine kinase inhibitors (BTKis) have revolutionized the treatment of B-cell lymphomas. However, safety issues related to the use of BTKis may hinder treatment continuity and further ...affect clinical efficacy. A comprehensive and systematic expert consensus from a pharmacological perspective is lacking for safety issues associated with BTKi treatment. A multidisciplinary consensus working group was established, comprising 35 members from the fields of hematology, cardiovascular disease, cardio-oncology, clinical pharmacy, and evidence-based medicine. This evidence-based expert consensus was formulated using an evidence-based approach and the Delphi method. The Joanna Briggs Institute Critical Appraisal (JBI) tool and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach were used to rate the quality of evidence and grade the strength of recommendations, respectively. This consensus provides practical recommendations for BTKis medication based on nine aspects within three domains, including the management of common adverse drug events such as bleeding, cardiovascular events, and hematological toxicity, as well as the management of drug-drug interactions and guidance for special populations. This multidisciplinary expert consensus could contribute to promoting a multi-dimensional, comprehensive and standardized management of BTKis.Bruton's tyrosine kinase inhibitors (BTKis) have revolutionized the treatment of B-cell lymphomas. However, safety issues related to the use of BTKis may hinder treatment continuity and further affect clinical efficacy. A comprehensive and systematic expert consensus from a pharmacological perspective is lacking for safety issues associated with BTKi treatment. A multidisciplinary consensus working group was established, comprising 35 members from the fields of hematology, cardiovascular disease, cardio-oncology, clinical pharmacy, and evidence-based medicine. This evidence-based expert consensus was formulated using an evidence-based approach and the Delphi method. The Joanna Briggs Institute Critical Appraisal (JBI) tool and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach were used to rate the quality of evidence and grade the strength of recommendations, respectively. This consensus provides practical recommendations for BTKis medication based on nine aspects within three domains, including the management of common adverse drug events such as bleeding, cardiovascular events, and hematological toxicity, as well as the management of drug-drug interactions and guidance for special populations. This multidisciplinary expert consensus could contribute to promoting a multi-dimensional, comprehensive and standardized management of BTKis.
•Formalization of the CD-CARS problem from two relevant research fields: CDRS and CARS.•The usefulness of the CD-CARS algorithms was demonstrated by a number of experiments.•Definition of real ...datasets for evaluating CD-CARS, once they are scarce in literature.•Comparison with traditional cross-domain collaborative filtering techniques.•Proposal of a systematic approach rather than ad-hoc ones available in the literature.
In this paper, we address two research topics in Recommender Systems (RSs) which have been developed in parallel without a deeper integration: Cross-Domain RS (CDRS) and Context-Aware RS (CARS). CDRS have emerged to enhance the quality of recommendations in a target domain by leveraging sources of information in different domains. CDRS are especially useful to address cold-start, sparsity and diversity problems in target domains with scarce information. CARS, on its turn, have been proposed to consider contextual information for recommendations. Such systems are suitable when the users’ interests change according to factors like time, location, among others. By combining these two approaches, better RSs can be developed, considering both the availability of useful data from multiple domains and the use of contextual information. In this paper, we formalize the combination of CDRS and CARS, which represents a more systematic integration of these approaches compared to previous work. Based on this formulation, we developed novel RSs techniques, named CD-CARS. To evaluate the developed CD-CARS techniques, we performed extensive experimentation through real datasets taking into account several scenarios. The recommendations were evaluated in terms of predictive and ranking performance, respectively achieving up to 62.6% and 45%, depending on the scenario, in comparison to traditional cross-domain collaborative filtering techniques. Therefore, the experimental results have shown that the integration of techniques developed in isolation can be useful in a variety of situations, in which recommendations can be improved by information gathered from different sources and can be refined by considering specific contextual information.
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