Group Recommendation Using External Followee for Social TV

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.

Social media can no longer be described as a fad, because they are becoming part of the social infrastructure. It has been pointed out that there is a high degree of affinity between social media and TV viewing. In this paper, by analyzing the results of experiments conducted with the participation of large numbers of users on "teleda" (a large-scale social TV system), we discuss the viewing behavior of users in social TV systems and the future shape of TV media in the social media era.

Random group recommendation model based on fuzzy clustering

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.

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.

Group recommendation has attracted significant research efforts for its importance in benefiting a group of users. This paper investigates the Group Recommendation problem from a novel aspect, which tries to maximize the satisfaction of each group member while minimizing the unfairness between them. In this work, we present several semantics of the individual utility and propose two concepts of social welfare and fairness for modeling the overall utilities and the balance between group members. We formulate the problem as a multiple objective optimization problem and show that it is NP-Hard in different semantics. Given the multiple-objective nature of fairness-aware group recommendation problem, we provide an optimization framework for fairness-aware group recommendation from the perspective of Pareto Efficiency. We conduct extensive experiments on real-world datasets and evaluate our algorithm in terms of standard accuracy metrics. The results indicate that our algorithm achieves superior performances and considering fairness in group recommendation can enhance the recommendation accuracy.

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

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

Report of the ISHLT Working Group on Primary Lung Graft Dysfunction Part I: Introduction and Methods

We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users. We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations. Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email.

Group status refers to the extent to which members of a group are respected and admired by others. All known societies are characterized by status stratifications, with the most advantaged groups enjoying a more-than-fair share of the total wealth and prestige. Most ordinary criteria to categorize people into groups possess value connotations that eventually uphold prestige hierarchies. Gender, ethnicity, and age—but also disability, weight, sexual orientation, and of course education, income, and class background—are major criteria of social stratification. Established status characteristics may consist of ascribed (e.g., gender) or achieved (e.g., occupation) qualities. They may further consist of groups with more (e.g., gender) or less (e.g., race, social class) contact and mutual interdependence. Status hierarchies are manifold, and the best metaphor encompassing their diversity is that of a vertical dimension that ranks groups’ status and prestige. Generally, members of high-status groups praise individualistic and autonomous self-conceptions and show self-directedness, whereas the opposite tendencies prevail toward the bottom of the status hierarchy. Socialization practices (e.g., parental education, peers, school, and the workplace) take center stage in explaining how members of status groups acquire these contrasting habits and characteristics. However, recent social psychological research sheds light on more general processes related to how people interpret and react to specific situations. Major contributions of social psychological analyses of group status are found in social identity theory, social role theory, status construction theory, the stereotype content model, and social dominance and system justification theories. Despite substantial differences, these perspectives complement each other to account for the formation, the maintenance, and the change of status hierarchies. Status hierarchies are not only pervasive and inevitable but also crucial in their consequences. Status contributes to a wealth of phenomena, including subjective well-being, mental, and physical health, etc. Important for the present discussion is research investigating how group status affects verbal and nonverbal communication between members of high- and low-status groups.

Group recommendations have become an increasingly important application of social tagging systems in recent years because of the rapid development and growth of groups. In contrast to current studies on group recommendation approaches that applied users-groups binary relations or users-tags-groups ternary relations, this work proposes a novel method named 4-order tensor reduction orthogonal iteration algorithm, which can recommend groups to users based on the consistent fusion of the items and tags information that exist in social tagging system. Four types of entities (users, tags, items, and groups) are integrated into a 4-order tensor recommendation framework, and then, Higher Order Singular Value Decomposition and Higher Order Orthogonal Iteration methods are performed to reveal the latent semantic association among these entities and recommend groups to users. The results of our experiments on a Flickr dataset show that the proposed group recommendation approach outperforms certain popular methods that only based on users-groups binary relations or users-tags-groups ternary relations in terms of mean average precision (MAP).

Investigating and counteracting popularity bias in group recommendations

Multi-view group representation learning for location-aware group recommendation

Novel automatic group identification approaches for group recommendation