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

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.

Traditional music recommender systems are mainly based on users’ interactions, which limit their performance. Particularly, various kinds of content information, such as metadata and description can be used to improve music recommendation. However, it remains to be addressed how to fully incorporate the rich auxiliary/side information and effectively deal with heterogeneity in it. In this paper, we propose a M ulti-view E nhanced G raph A ttention N etwork (named MEGAN ) for session-based music recommendation. MEGAN can learn informative representations (embeddings) of music pieces and users from heterogeneous information based on graph neural network and attention mechanism. Specifically, the proposed approach MEGAN firstly models users’ listening behaviors and the textual content of music pieces with a Heterogeneous Music Graph (HMG). Then, a devised Graph Attention Network is used to learn the low-dimensional embedding of music pieces and users and by integrating various kinds of information, which is enhanced by multi-view from HMG in an adaptive and unified way. Finally, users’ hybrid preferences are learned from users’ listening behaviors and music pieces that satisfy users real-time requirements are recommended. Comprehensive experiments are conducted on two real-world datasets, and the results show that MEGAN achieves better performance than baselines, including several state-of-the-art recommendation methods.

Group Recommendation (GR) is the task of suggesting relevant items/events for a group of users in online systems, whose major challenge is to aggregate the preferences of group members to infer the decision of a group. Prior group recommendation methods applied predefined static strategies for preference aggregation. However, these static strategies are insufficient to model the complicated decision making process of a group, especially for occasional groups which are formed adhoc. Compared to conventional individual recommendation task, GR is rather dynamic and each group member may contribute differently to the final group decision. Recent works argue that group members should have non-uniform weights in forming the decision of a group, and try to utilize a standard attention mechanism to aggregate the preferences of group members, but they do not model the interaction behavior among group members, and the decision making process is largely unexplored.In this work, we study GR in a more general scenario, that is Occasional Group Recommendation (OGR), and focus on solving the preference aggregation problem and the data sparsity issue of group-item interactions. Instead of exploring new heuristic or vanilla attention-based mechanism, we propose a new social self-attention based aggregation strategy by directly modeling the interactions among group members, namely Group Self-Attention (GroupSA). In GroupSA, we treat the group decision making process as multiple voting processes, and develop a stacked social self-attention network to simulate how a group consensus is reached. To overcome the data sparsity issue, we resort to the relatively abundant user-item and user-user interaction data, and enhance the representation of users by two types of aggregation methods. In the training process, we further propose a joint training method to learn the user/item embeddings in the group-item recommendation task and the user-item recommendation task simultaneously. Finally, we conduct extensive experiments on two real-world datasets. The experimental results demonstrate the superiority of our proposed GroupSA method compared to several state-of-the-art methods in terms of HR and NDCG.

Cold-start problem is a fundamental challenge for recommendation tasks. Despite the recent advances on Graph Neural Networks (GNNs) incorporate the high-order collaborative signal to alleviate the problem, the embeddings of the cold-start users and items aren't explicitly optimized, and the cold-start neighbors are not dealt with during the graph convolution in GNNs. This paper proposes to pre-train a GNN model before applying it for recommendation. Unlike the goal of recommendation, the pre-training GNN simulates the cold-start scenarios from the users/items with sufficient interactions and takes the embedding reconstruction as the pretext task, such that it can directly improve the embedding quality and can be easily adapted to the new cold-start users/items. To further reduce the impact from the cold-start neighbors, we incorporate a self-attention-based meta aggregator to enhance the aggregation ability of each graph convolution step, and an adaptive neighbor sampler to select the effective neighbors according to the feedbacks from the pre-training GNN model. Experiments on three public recommendation datasets show the superiority of our pre-training GNN model against the original GNN models on user/item embedding inference and the recommendation task.

Group recommendation aims to recommend items to a group of users. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, where groups are formed ad hoc and users may just constitute a group for the first time—that is, the historical group-item interaction records are highly limited. Most state-of-the-art works have addressed the challenge by aggregating group members’ personal preferences to learn the group representation. However, the representation learning for a group is most complex beyond the aggregation or fusion of group member representation, as the personal preferences and group preferences may be in different spaces and even orthogonal. In addition, the learned user representation is not accurate due to the sparsity of users’ interaction data. Moreover, the group similarity in terms of common group members has been overlooked, which, however, has the great potential to improve the group representation learning. In this work, we focus on addressing the aforementioned challenges in the group representation learning task, and devise a hierarchical hyperedge embedding-based group recommender, namely HyperGroup. Specifically, we propose to leverage the user-user interactions to alleviate the sparsity issue of user-item interactions, and design a graph neural network-based representation learning network to enhance the learning of individuals’ preferences from their friends’ preferences, which provides a solid foundation for learning groups’ preferences. To exploit the group similarity (i.e., overlapping relationships among groups) to learn a more accurate group representation from highly limited group-item interactions, we connect all groups as a network of overlapping sets (a.k.a. hypergraph), and treat the task of group preference learning as embedding hyperedges (i.e., user sets/groups) in a hypergraph, where an inductive hyperedge embedding method is proposed. To further enhance the group-level preference modeling, we develop a joint training strategy to learn both user-item and group-item interactions in the same process. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our proposed HyperGroup in comparison to the state-of-the-art baselines.

The session-based recommendation aims to predict users' immediate next actions based on their short-term behaviors reflected by past and ongoing sessions. Graph neural networks (GNNs) recently dominated the related studies, yet their performance heavily relies on graph structures, which are often predefined, task-specific, and designed heuristically. Furthermore, existing graph-based methods either neglect implicit correlations among items or consider explicit and implicit relationships altogether in the same graphs. We propose to decouple explicit and implicit relationships among items. As such, we can capture the prior knowledge encapsulated in explicit dependencies and learned implicit correlations among items simultaneously in a flexible and more interpretable manner for effective recommendations. We design a dual graph neural network that leverages the feature representations extracted by two GNNs: a graph neural network with a single gate (SG-GNN) and an adaptive graph neural network (A-GNN). The former models explicit dependencies among items. The latter employs a self-learning strategy to capture implicit correlations among items. Our experiments on four real-world datasets show our model outperforms state-of-the-art methods by a large margin, achieving 18.46% and 70.72% improvement in [email protected], and 49.10% and 115.29% improvement in [email protected] on Diginetica and LastFM datasets.

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

Graph neural networks induced by concept lattices for classification

Sequential recommendation and group recommendation are two important branches in the field of recommender system. While considerable efforts have been devoted to these two branches in an independent way, we combine them by proposing the novel sequential group recommendation problem which enables modeling group dynamic representations and is crucial for achieving better group recommendation performance. The major challenge of the problem is how to effectively learn dynamic group representations based on the sequential user-item interactions of group members in the past time frames. To address this, we devise a Group-aware Long- and Short-term Graph Representation Learning approach, namely GLS-GRL, for sequential group recommendation. Specifically, for a target group, we construct a group-aware long-term graph to capture user-item interactions and item-item co-occurrence in the whole history, and a group-aware short-term graph to contain the same information regarding only the current time frame. Based on the graphs, GLS-GRL performs graph representation learning to obtain long-term and short-term user representations, and further adaptively fuse them to gain integrated user representations. Finally, group representations are obtained by a constrained user-interacted attention mechanism which encodes the correlations between group members. Comprehensive experiments demonstrate that GLS-GRL achieves better performance than several strong alternatives coming from sequential recommendation and group recommendation methods, validating the effectiveness of the core components in GLS-GRL.

A service-recommendation method for the Internet of Things leveraging implicit social relationships

Social trust has been an important mechanism in overcoming crises throughout history. Several societies are now emphasizing its role in combating the COVID-19 pandemic. This study aims to investigate how variations in social trust across 68 countries are related to the transmission speed of COVID-19. Specifically, using cross-national index data from the World Value Survey, the study tests how variations in social trust across countries generate different time durations at which each country reaches the peak in terms of increases in new infections of COVID-19. Using data drawn between December 31, 2019 and July 31, 2020, this study found that in countries with a high level of social trust, particularly trust among ingroup members, or with a narrower or wider range than the intermediate range of trustees, the number of new infections tended to reach the first peak within a shorter time duration than in other countries. These results imply that in such societies, on the one hand, high cooperation among people to achieve common goals and strong compliance to social norms may allow them to begin neutralizing COVID-19 faster. On the other hand, however, the low risk perception and prevalence of cohesive relationships among people may lead to speedier transmission of COVID-19 before neutralization takes place.

Unveiling the power of knowledge graph embedding in knowledge aware deep recommender systems for e-commerce: A comparative study

With the rapid growth of information in the Internet era, people are in great need of recommendation methods to filter information. At present, recommendation methods which based on heterogeneous information network (HIN) have attracted wide attention. Recently, HIN-based recommendation methods need to be modeled from two aspects: node structural association and semantic association. To this end, we propose a graph contrastive learning model based on structural and semantic view for HIN recommendation (GCL-SS). GCL-SS utilizes U-I interactive view to obtain node structural embeddings, and utilizes U-I semantic view to obtain node semantic embeddings. Based on these two kinds of embeddings, we establish a self-supervised contrastive learning mechanism to effectively integrate structural information and semantic information of user (item) nodes in HIN, and finally learn a more discriminative user (item) embedding. In addition, in order to strengthen the semantic association between nodes, we innovatively utilize time sequence encoder (TSE), such as LSTM, to encode semantic homogeneous network decomposed by HIN in U-I semantic view. At last, based on the user and item embeddings, we adopt bilinear decoder to model the potential association between user and item, so as to realize rating prediction of user to item. The experimental results on three real datasets confirm that our GCL-SS model performs better than state-of-the-art recommendation methods in rating prediction task. In addition, the results of four ablation experiments indicate that our GCL-SS model can effectively improve the performance of rating prediction in recommendation.

Introduction Social capital, defined as trust, norms, and networks that facilitate coordinated action for a mutual benefit, has drawn attention because it highlights the importance of participation, cooperation, and trust amongst economic agents in overcoming social dilemmas and producing public goods that maximize social welfare. It has been argued that generalized participation of individuals across social groups and organizations can offset the negative effects of special-interest groups and patron-client relations that promote particularist interests against public welfare, and thus inhibit wider social collaboration, reform, and development (for example, Putnam 1993; Olson 1996; Woolcock 1998). This paper empirically explores the determinants of social capital in the form of group membership in a sample of European countries. The aim of this paper is to contribute to the development of a consistent and integrated framework of social capital formation, which incorporates both individual and aggregate determinants of participatory behavior. Some studies tend to emphasize the role of individual characteristics and social determinants of income, education, gender, and employment (e.g., Glaeser et al. 2002; Oorschot etal. 2006; Halman and Luijkx 2006). Others give more weight to institutional or systemic factors, such as income inequality, confidence in government, social trust, human development, civil liberties, and regional differences (see Brehm and Rahn 1997; Costa and Kahn 2003; Bartkowski and Jasinska-Kania 2004). This study argues that social capital does not depend only on individual preferences, beliefs, interests, and strategies, but is also influenced by more institutional and systemic factors that characterize the social, political, and cultural context in which individuals interact. This hypothesis is tested by applying binary logistic regression models to regress an index of individuals' group membership on individual factors (income, education, employment, age, marriage, and gender), as well as aggregate socio-economic/socio-political factors (GDP per capita, unemployment, income inequality, social trust, trust in political institutions, corruption, and violation of political and civil rights). Data on individuals is derived from the European Community Household Panel (ECHP, hereafter), Wave 6, 1999, which covers a sample of European countries from the former EU-15. The benefit of this database is that the sample of individuals used in both pooled and country regression analyses is much larger compared to previous studies in the relevant literature. Additionally, it provides data on membership that differs from standard social surveys, in that it does not conform to a common list of social organizations, and thus might allow us to capture more locally-determined conceptualizations of networking and association across countries. The next section begins with an overview of the empirical literature on the determinants of group membership. The research in the third section conducts regression analyses for our sample of European countries and finds that both individual and aggregate factors have an important influence on group membership. Finally, based on empirical results, this paper discusses possible ways by which countries, such as Southern European countries, could change traditions of clientelism and nepotism, and expand opportunities for social participation and cooperation to promote social welfare and development. Earlier Work on Determinants of Social Capital Studies on the determinants of social capital focus on different sets of individual and aggregate factors, and view social participation as an outcome of investment decisions or contextual influences. Glaeser et al. (2002) adopt an individual-oriented approach based on a standard optimal investment model. They use pooled cross-sectional data from the General Social Survey (GSS) (1) for the 1972-1998 period to investigate the impact of individual and group characteristics on individuals' membership, measured as the number of social organizations to which individual respondents belong. …

A survey of graph neural network based recommendation in social networks