Overcoming Data Sparsity in Group Recommendation

Abstract

It has been an important task for recommender systems to suggest satisfying activities to a group of users in people's daily social life. The major challenge in this task is how to aggregate personal preferences of group members to infer the decision of a group. Conventional group recommendation methods applied a predefined strategy for preference aggregation. However, these static strategies are too simple to model the real and complex process of group decision-making, especially for occasional groups which are formed ad-hoc. Moreover, group members should have non-uniform influences or weights in a group, and the weight of a user can be varied in different groups. Therefore, an ideal group recommender system should be able to accurately learn not only users' personal preferences but also the preference aggregation strategy from data. In this paper, we propose a novel end-to-end group recommender system named CAGR (short for "Centrality-Aware Group Recommender"), which takes Bipartite Graph Embedding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way. Specifically, we first extend BGEM to model group-item interactions, and then in order to overcome the limitation and sparsity of the interaction data generated by occasional groups, we propose a self-attentive mechanism to represent groups based on the group members. In addition, to overcome the sparsity issue of user-item interaction data, we leverage the user social networks to enhance user representation learning, obtaining centrality-aware user representations. To further alleviate the group data sparsity problem, we propose two model optimization approaches to seamlessly integrate the user representations learning process. We create three large-scale benchmark datasets and conduct extensive experiments on them. The experimental results show the superiority of our proposed CAGR by comparing it with state-of-the-art group recommender models.

Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation

“…Early aggregation methods are also known as preference aggregationbased methods [8,39,55,66,68] that aim at aggregating preferences of individual group members as the profile of a group. Compared with late aggregation methods, these methods first aggregate the preference (or representation) of group members, and then make group recommendations (or produce prediction scores) accordingly.…”

A Biased Proportional-Integral-Derivative-Incorporated Latent Factor Analysis Model

“…The experiments use a dataset from the Movielens [36] series, which was collected from the Movielens system maintained by the Grouplens research team, with a score on the scale of 1 to 5 (1,2,3,4,5). To explore the recommendation effect of the biased PSL model on datasets of different sizes and densities, this experiment selects 3 different sizes of subsets of the dataset.…”

“…In this situation, cold start [3], reduced coverage, neighbor transitivity and a series of problems need to be solved. Data sparsity [4] brings tons of difficulties to the recommendation. In other words, the user provides little information for reference by the recommendation system, but the number of items that are needed to predict the scores is quite large.…”

A Novel Travel Group Recommendation Model Based on User Trust and Social Influence

“…Previous literature [19] has elaborated on common fusion strategies. Generally, they can be categorized as single-preference [20] and mixed [21] fusion strategies. Tang et al [15] proposed a preference fusion strategy based on user interaction behaviors, but this strategy did not consider the influence of the consumed items in the group on the recommendation results.…”