Group Recommendation Method Research Articles

Retraction Note: Interval-valued dual hesitant fuzzy linguistic group recommendation method by considering the double relevance

Retraction Note: Interval-valued dual hesitant fuzzy linguistic group recommendation method by considering the double relevance

Potential factors-embedding group recommendation for online education

Online education platform urgently needs recommendation methods to service learning groups. The existing group recommendation methods rely on member preference aggregation. However, potential factors such as teachers and schools have an important impact on the decision-making of learning groups. Due to the different ways in which potential factors operate, it is very challenging to model the impact these potential factors have on groups. In this paper, we propose a group recommendation method for online education (GEMst) that innovatively designs two modules to capture different types of potential factor embedding. In the subjective guidance module, we construct a heterogeneous information network based on the relationship between guiders (teacher, leader, etc.) and groups, analyze the influence of the guider on the group, and obtain the group representation from this perspective. In the objective environment hypergraph module, we consider the influence mode of the environment (school, platform, etc.) on the group, and propose the objective relationship hypergraph convolution. Compared with hypergraph convolution, it directly learns the influence between groups in the same environment. We provide information fusion strategies that GEMst can collaboratively consider group members’ opinions and the impact of potential factors. In addition, to enhance the learning effect of the model, we design a pre-training strategy based on the user grouping relationship that obtains more accurate user embedding representations. We test GEMst on two real-world datasets, and the results show that GEMst outperforms the current baseline group recommendation method.

A Hierarchical Attention Network for Cross-Domain Group Recommendation.

Many online services allow users to participate in various group activities such as online meeting or group buying, and thus need to provide user groups with services that they are interested. The group recommender systems (GRSs) emerge as required and provide personalized services for various online user groups. Data sparsity is an important issue in GRSs, since even fewer group-item interactions are observed. Moreover, the group and the group members have complex and mutual relationships with each other, which exacerbates the difficulty in modeling the preferences of both a group and its members for recommendation. The cross-domain recommender system (CDRS) is a solution to alleviate data sparsity and assist preference modeling by transferring knowledge from a source domain which has relatively dense data to another. The existing CDRSs are usually developed for individual users and cannot be directly applied for group recommendation. To alleviate the data sparsity issue in GRSs, we first study the cross-domain group recommendation problem and propose a hierarchical attention network-based cross-domain group recommendation method, called HAN-CDGR. HAN-CDGR takes the advantage of data from a source domain to benefit recommendation generation for both the individual users and groups in the target domain which has data sparsity and cannot generate accurate recommendation. In HAN-CDGR, a hierarchical attention network is constructed to learn and model individual and group preferences, with consideration of both group members' interactions and dynamic weights and the complex relationships between individuals and groups. Adversarial learning is used to effectively transfer knowledge from a source domain to the target domain. Extensive experiments, which demonstrate the effectiveness and superiority of our proposal, providing accurate recommendation for both individual users and groups, are conducted on three tasks.

NEURAL COLLABORATIVE FILTERING BASED GROUP RECOMMENDATIONS

Group recommendations, where a system suggests items for a group of users rather than individuals, present a unique challenge in collaborative filtering. Traditional approaches to group recommendations often rely on aggregating individual preferences, which can lead to suboptimal results when preferences are diverse or conflicting. This paper explores a novel approach using Neural Collaborative Filtering (NCF) to improve group recommendation accuracy. NCF, which leverages deep learning techniques to model complex user-item interactions, offers a more nuanced understanding of group dynamics by incorporating both individual and group-level preferences. We propose a new NCF-based framework designed to handle group recommendations by effectively learning and integrating diverse user preferences. Our experimental results demonstrate that the proposed approach outperforms traditional group recommendation methods in terms of prediction accuracy and user satisfaction. This research highlights the potential of neural networks in enhancing group recommendation systems and provides a foundation for future developments in this area.

KTPGN: Novel event-based group recommendation method considering implicit social trust and knowledge propagation

Groups where like-minded people gather to share interests, comments, or participate in activities have recently gained popularity in well-known social platforms, such as Meetup and Douban. Group recommendations are essential in social media to help users identify their preferred groups. Our concern is event-based group recommendations, based on which users join a group primarily to participate in offline events hosted by group members. Traditional methods for group recommendation do not work well in addressing this issue, because they ignore the social relationships among users or multiple attributes of groups. The knowledge-enhanced trust propagation graph neural network (KTPGN) presented in this study exploits social trust, multiple attributes of groups, and user-group interaction history in a unified framework to predict user preferences for groups. First, an implicit social trust graph is constructed by considering two aspects of credibility, because explicit trust relationships are not always available in most real recommender systems. Furthermore, a graph neural network (GNN)-based model is proposed to learn group embeddings by iteratively aggregating multiple linked attributes and user embeddings through recursive propagation of social trust and historical interest. Experimental results on several datasets from Meetup demonstrate that KTPGN significantly outperforms state-of-the-art recommendation methods.

A Novel Recommendation Approach For Groups Based On Aggregating Top-k Lists

One of the most challenging issues in group recommendation is how to consider the dispersion in members’ preferences to reach a joint recommendation satisfying all the group. The group recommendation is generated by aggregating either the users’ preferences or the individual recommendations. In this paper, we focus on the recommendations aggregation strategy which consists in generating a recommendation list for each group member, then combining these individual lists to produce a single group recommendation list. Many aggregation functions (such as average, least misery, Borda, etc.) were used to resolve the ranking aggregation problem. However, they cannot support the partial information presented over the top-k recommendation lists which may not contain the same set of alternatives. To this end, we propose a group recommendation method based on a novel aggregation function that considers the partial rankings and computes each item's relevance to the target group based on both the users’ preferences and the items’ positions. We conduct a deep experiment to study the impact of some parameters (such as the individual recommendation algorithms, the aggregation functions, and the group size) on the group recommendations quality. We have shown that the accuracy of the group recommendations does not depend on the algorithm used to generate individual recommendations. However, it is strongly affected by the method used to aggregate data and the group size. Experimental results show a considerable improvement in the group recommendation performance using the proposed solution.

Self‐supervised graph learning for occasional group recommendation

As an important branch in Recommender System, occasional group recommendation has received more and more attention. In this scenario, each occasional group (cold-start group) has no or few historical interacted items. As each occasional group has extremely sparse interactions with items, traditional group recommendation methods can not learn high-quality group representations. The recent proposed Graph Neural Networks (GNNs), which incorporate the high-order neighbors of the target occasional group, can alleviate the above problem in some extent. However, these GNNs still can not explicitly strengthen the embedding quality of the high-order neighbors with few interactions. Motivated by the self-supervised learning technique, which is able to find the correlations within the data itself, we propose a self-supervised graph learning framework, which takes the user/item/group embedding reconstruction as the pretext task to enhance the embeddings of the cold-start users/items/groups. To explicitly enhance the high-order cold-start neighbors' embedding quality, we further introduce an embedding enhancer, which leverages the self-attention mechanism to improve the embedding quality for them. Comprehensive experiments show the advantages of our proposed framework than the state-of-the-art methods.

Social dual-effect driven group modeling for neural group recommendation

Frequent group activities of human beings have become an indispensable part of human daily life. Group recommendation aims to recommend preferred items to a group of users in recommender systems. The existing solutions on group recommendation are to explore group modeling with or without the help of auxiliary information on social networks. However, we observe that the social factors can be explored directly in the group without employing social information on social networks. Towards this end, we study the social effect-based design guideline to drive group modeling. In this work, we propose a novel Social dual-Effect driven Attentive Group Recommendation method (SEAGR) that well utilizes social selection effect and social influence effect from sociology to explore group representation learning for neural group recommendation. Specifically, we construct the social selection-driven group inherent modeling from interaction-level and user-level. To mimic interaction-based dynamic group decision-making, we also design a social influence-driven attentive influence mining model in terms of users’ influence distinction in different groups. Based on these two components, an aggregative group representation is obtained. Moreover, neural recommendation for groups and users could be intensified reciprocally considering the impact of groups on users. The experimental results validate the effectiveness of the proposed method on three real-world datasets, and demonstrate its advantages over state-of-the-art methods in accuracy through extensive experiments.

Profile Aggregation-Based Group Recommender Systems: Moving From Item Preference Profiles to Deep Profiles

To meet the increasing demand for group activities, single-user recommender systems need to be scaled up to provide recommendations to groups of users. This issue is solved by aggregating item preference profiles of individual group members into a single item preference profile, thereby allowing recommendations to be created for this item preference profile. In this paper, we introduce the concept of deep profiles of users, and we propose group recommendation methods based on the aggregation of group members’ deep profiles, instead of item preference profiles as in previous studies. The term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deep profile</i> refers to the users’ profiles that lie deep within the recommendation algorithms. Experiments have shown that group recommendations based on deep profiles give higher efficiency in terms of F1-score and nDCG than those based on item preference profiles.

Group recommendation method combining short-term interest and long-term preference

Based on self-attention and outer product-based neural collaborative filtering,this paper proposed a SLAR model.The model uses the recent interaction information of each user in the group and self-attention mechanism to obtain the short-term interest vector of the group.The attention mechanism and self-attention mechanism are used to calculate the influence of each user and the influence between members during the interaction between the target group and item, so as to aggregate them into the long-term preference vector of the group, and then the sum of short-term interest and long-term preference is input into ONCF model as the embedding vector of the group to mine the interaction between the group and the project from the data, and finally complete the group recommendation. Compared with the traditional group fusion strategy on CAMR2011 data set, the experimental results show that SLGR model achieves better results.

A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

GRBMC: An effective crowdsourcing recommendation for workers groups

Crowdsourcing as an important computing resource for the Crowd-based Cooperative, crowdsourcing workers, due to the complexity of their personnel composition and the ambiguity of group characteristics, make it difficult to accurately recommend suitable worker groups for the task. Recommending crowdsourcing tasks to suitable workers can greatly improve the efficiency and quality of the execution of crowdsourcing tasks. The current crowdsourcing recommendation algorithm is based on the single characteristics of the workers, ignoring the multi-dimensional characteristics of the workers and the community characteristics of the crowd. This paper proposes a worker group recommendation method based on multi-community collaboration (GRMBC) by utilizing the worker characteristics information extracted from crowdsourcing platform. Based on the characteristics of workers'reputation, preference and activity, the method divides the worker group into several characteristics communities with similar behavior to discover the potential multi-community structure in the crowdsourcing worker group, and then selects Top-N worker group for recommendation through the interaction among multi-communities. At the same time, we also proposed two mitigation strategies for the cold start problem of data in crowdsourcing. This paper uses the public data collected by AMT to do the experiments, and compares the aggregation results of the recommendations generated by different algorithms. The results show the recommendations generated by the GRBMC algorithm proposed in this paper performs the best comprehensively. When the GRBMC algorithm that considers both worker attributes and communi-ty characteristics has an accuracy improvement of 0.03 ~ 0.04 compared with not using the recommendation algorithm on each index, the accuracy of 0.01 ~ 0.02 is improved com-pared with the method considering single characteristic. Moreover, compared with the individual recommendation method of the workers, the recommendation method of the workers' group is more in line with the platform requirements, and can well reflect the wisdom of crowd wisdom.

A POI group recommendation method in location-based social networks based on user influence

Group recommendation has attracted researchers’ attention in various domains, specifically such approaches utilizing location-based social networks (LBSNs). However, point of interest (POI) group recommendation faces the challenge of aggregating diverse user preferences, while group members have different influences on the final decision of the group. Besides, the recommendation of spatial items is different from non-spatial items and the unique features of the spatial items such as distance must be considered in the recommendation. In this paper, a POI group recommendation method is proposed to tackle this problem. User influence is modeled fuzzy and taken into account the difference of users’ personality and their preferences when are alone or in a group, by using historical check-in data in LBSNs and in terms of category, distance and time. The proposed method is integrated with the weighted average aggregation to improve the efficiency of the POI group recommendation. Experimental results in a real dataset show improvement in the accuracy of POI group recommendations in varying sizes of groups. The results also get better when the user influence is calculated using the fuzzy approach. Besides, studying user behavior differences to choose the place to visit when alone or in a group shows that i) the flexibility of users in distance is less than time and category. It is also in the category less than time. ii) Time has a greater range of behavioral change than distance and category. iii) Users who actively participate in group decision making have a more significant number of visits in groups than when they are alone.

A new point-of-interest group recommendation method in location-based social networks

POI group recommendation is one of the hottest research topics in location-based social networks, which recommends the most agreeable places for a group of users. However, traditional POI group recommendation methods only generate a consensus function to aggregate individual preference into group preference and they do not consider all the factors that can determine the results of POI group recommendation, which leads to a low recommendation accuracy. What’s more, these methods have a long running time. Therefore, in this paper, we propose a new POI group recommendation method with an extreme learning machine (ELM) called PGR-ELM. The PGR-ELM method regards POI group recommendation as a binary classification problem. First, three features are extracted from three factors: POI popularity, group members’ distance to POI, members’ interest preferences combined affinity between group members. These features simultaneously consider all the factors that can determine the results of recommendation and guarantee the effectiveness of POI group recommendation. Then, the extracted features are input to train an ELM classifier because of its fast learning speed, which guarantees the efficiency of POI group recommendation. Finally, extensive experiments verify the accuracy and efficiency of PGR-ELM method.

User recommendation method based on joint probability matrix decomposition in CPS networks

In recent years, with the rapid development of the Internet, various virtual communities continue to emerge, and the phenomenon of user groups working together is gradually increasing. People begin to pay more attention to group-oriented recommendation. Most of existing group recommendation methods are improved on the memory-based collaborative filtering recommendation method, or think that the members of the group are independent of each other, ignoring the impact of association among the members of the group on the results of group recommendation. In this paper, a group recommendation method based on joint probability matrix decomposition is proposed to better model the group recommendation problem. Firstly, the user-plus-person group information is used to calculate the correlation between users. Secondly, the user correlation matrix is integrated into the process of probability matrix decomposition to get the individual prediction score. Finally, the group-to-item prediction score is obtained by using the common synthesis strategy in group-oriented recommendation problem. Furthermore, the proposed method is compared with existing group recommendation methods. Experiments on CiteULike dataset show that the proposed method achieves better recommendation results in accuracy, recall and other evaluation indicators.

Group recommendation based on hybrid trust metric

Group recommendation is a special service type which has the ability to satisfy a group’s common interest and find the preferred items for group users. Deep mining of trust relationship between group members can contribute to the improvement of accuracy during group recommendation. Most of the existing trust-based group recommendation methods pay little attention to the diversity of trust sources, resulting in poor recommendation accuracy. To address the problem above, this paper proposes a group recommendation method based on a hybrid trust metric (GR-HTM). Firstly, GR-HTM creates an attribute trust matrix and a social trust matrix based on user attributes and social relationships, respectively. Secondly, GR-HTM accomplishes a hybrid trust matrix based on the integration of these two matrices with the employment of the Tanimoto coefficient. Finally, GR-HTM calculates weights for each item in the hybrid trust matrix based on weighted-meanlist and proceeds to group recommendation with a given trust threshold. Simulation experiments demonstrate that the proposed GR-HTM has better performance for group recommendation in accuracy and effectiveness.

Hybrid Deep Framework for Group Event Recommendation

Group recommender systems suggesting items for a group of users have received many attentions recently. Some aggregation-based and model-based group recommendation methods have been proposed. However, the cold-start problem in group recommendation has not been well studied, which limits the application of group recommendation in many important domains, such as recommending offline events for a group of users. In this paper, we propose a new hybrid deep framework to solve cold-start problem of group event recommendation. Our framework incorporates multiple Restricted Boltzmann Machines (RBM) and conditional RBM. The former extracts high latent group preference from user feedback and group feedback. The latter obtains latent event features based on contextual information, such as location and organizer of events. Thus, the hybrid deep framework can utilize user feedback and contextual information of events to overcome cold-start problem. We conduct exhaustive experiments on two real-world datasets and the results show that our proposed framework outperforms the baseline group recommendation methods and alleviates the cold-start problem of group event recommendation effectively.

TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators

In recent years, an increase in group activities on websites has led to greater demand for highly-functional group recommender systems. The goal of group recommendation is to capture and distill the preferences of each group member into a single recommendation list that meets the needs of all group members. Existing aggregation functions perform well in harmonious and congruent scenarios, but tend not to generate satisfactory results when group members hold conflicting preferences. Moreover, most of current studies improve group recommendation only based on a single aggregation strategy and explicit trust information is still ignored in group recommender systems. Motivated by these concerns, this paper presents TruGRC, a novel Trust-aware Group Recommendation method with virtual Coordinators, that combines two different aggregation strategies: result aggregation and profile aggregation. As each individual’s preferences are modeled, a virtual user is built as a coordinator to represent the profile aggregation strategy. This coordinator provides a global view of the preferences for all group members by interacting with each user to resolve conflicting preferences. Then, we also model the impact from group members to the virtual coordinator in accordance with personal social influence inferred by trust information on social networks. Group preferences can be easily generated by the average aggregation method under the effect of the virtual coordinator. Experimental results on two benchmark datasets with a range of different group sizes show that TruGRC method has significant improvements compared to other state-of-the-art methods.

A Topic Modeling Approach for Traditional Chinese Medicine Prescriptions

In traditional Chinese medicine (TCM), prescriptions are the daughters of doctors’ clinical experiences, which have been the main way to cure diseases in China for several thousand years. In the long Chinese history, a large number of prescriptions have been invented based on TCM theories. Regularities in the prescriptions are important for both clinical practice and novel prescription development. Previous works used many methods to discover regularities in prescriptions, but rarely described how a prescription is generated using TCM theories. In this work, we propose a topic model which characterizes the generative process of prescriptions in TCM theories and further incorporate domain knowledge into the topic model. Using 33,765 prescriptions in TCM prescription books, the model can reflect the prescribing patterns in TCM. Our method can outperform several previous topic models and group recommendation methods on generalization performance, herbs recommendation, symptoms suggestion, and prescribing patterns discovery.

Collaborative matrix factorization mechanism for group recommendation in big data-based library systems

PurposeAcademic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups.Design/methodology/approachThe authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed.FindingsEmpirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment.Research limitations/implicationsThe proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts.Practical implicationsThe proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient.Social implicationsThe proposed methods have potential value to improve scientific collaboration and research innovation.Originality/valueThe proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.