The serendipity recommendation tries to burst the filter bubble while still meeting user interests. However, serendipity itself has not been well understood in the recommendation system. Thus, factor investigation in recommendation serendipity has attracted much attention, for which two challenges hinder follow-up research: (1) Ambiguity of factors . Different works exploit different factors, and the meanings of factors are inconsistent in various works. (2) Lack of complete impact validation . The importance of these factors in different domains is not yet fully understood. The common approach of user surveys costs much, but the results are usually less objective and limited in quantity. To this end, we strive to comprehensively identify and clarify serendipity factors and explore objective data-driven approaches to validate factor impacts in large-scale cross-domain scenarios. We first conduct a comprehensive literature review to identify all possible factors, from which we find that some factors are being used indistinguishably. To address this issue, we propose two principles of meaning coverage and factor independence to clarify and disentangle serendipity factors. Next, we propose a general experimental framework to explore the impacts of factors. Then, we implement one such framework and run experiments on nine representative datasets to study factor importance on serendipity. We also propose a quantitative method to measure the degree of disentanglement of factors and to test the effects of factor combinations. We gain several useful findings: (1) relevance , diversity , and random are critical factors affecting serendipity; (2) domain features affect factor importance and can guide serendipity recommendation; (3) the disentanglement quantification method benefits the understanding of serendipity and the combination of factors. To our knowledge, this is the first work to comprehensively investigate serendipity factors and experimentally compare their impacts in an objective data-driven approach.

Diversity is increasingly recognized as a crucial factor in recommendation systems for enhancing user satisfaction. However, existing studies on diversity-enhanced recommendation systems primarily focus on designing recommendation strategies, often overlooking the development of evaluation metrics. Widely used diversity metrics such as CC, ILAD, and ILMD are typically assessed independently of accuracy. This separation leads to a critical limitation: existing diversity measures are unable to distinguish between diversity improvements from effective recommendations and those from in effective recommendations. Our evaluations reveal that the diversity improvements are primarily contributed by ineffective recommendations, which often do not positively contribute to user satisfaction. Furthermore, existing diversity metrics disregard the feature distribution of ground-truth items, potentially skewing the assessment of diversity performance. To address these limitations, we design three new accuracy-aware metrics: DCC, FDCC, and DILAD, and conduct a re-evaluation using these metrics. Surprisingly, our results illustrate that the diversity improvements of existing diversity-enhanced approaches are limited and even negative compared to those of accurate recommendations. This finding underscores the need to explore more sophisticated diversity-enhanced techniques for improving the diversity within effective recommendations.

In recommendation systems, Graph Convolutional Networks (GCNs) often suffer from significant computational and memory cost when propagating features across the entire user-item graph. While various sampling strategies have been introduced to reduce the cost, the challenge of neighbor explosion persists, primarily due to the iterative nature of neighbor aggregation. This work focuses on exploring subgraph propagation for scalable recommendation by addressing two primary challenges:efficient and effective subgraph constructionandsubgraph sparsity. To address these challenges, we propose a novelGCNmodel for recommendation based onSubgraph propagation, called SubGCN. One key component of SubGCN is BiPPR, a technique that fuses both source- and target-based Personalized PageRank (PPR) approximations, to overcome the challenge ofefficient and effective subgraph construction. Furthermore, we propose a source-target contrastive learning scheme to mitigate the impact ofsubgraph sparsityfor SubGCN. We conduct extensive experiments on two large and two medium-sized datasets to evaluate the scalability, efficiency, and effectiveness of SubGCN. On medium-sized datasets, compared to full-graph GCNs, SubGCN achieves competitive accuracy while using only 23.79% training time on Gowalla and 16.3% on Yelp2018. On large datasets, where full-graph GCNs ran out of the GPU memory, our proposed SubGCN outperforms widely used sampling strategies in terms of training efficiency and recommendation accuracy.

Graph convolutional network (GCN) has been extensively applied to recommender systems (RS) and achieved significant performance improvements through iteratively aggregating high-order neighbors to model the relevance between users and items as well as their characteristics. In the aggregation process, GCN models usually give neighbors the same or trainable weights based on implicit features, ignoring explicit ones. In this work, we take the features with explicit meanings or extracted with specific purpose as explicit ones (e.g., temporal features) and the others contained in user-item network as implicit ones (e.g., user preferences). However, some explicit features and knowledge play an essential role in improving the model representation ability and explainability in recommendation systems. To deal with the limitation, we propose a GCN based framework to embed the explicit features or those extracted with explicit intentions in this work. We also provide specific implementations based on two commonly researched features, temporal evolution and popularity bias. Specifically, we first experimentally analyze the popularity bias of the representation learning in RS based on two commonly used GCN models. Secondly, we propose a general framework to weigh neighbors based on explicit features or intentions. Thirdly, we implement a Temporal and Popularity weighted Aggregator (TPA) for GCN. The Interest-Forgetting Curve is utilized to capture temporal evolution as temporal weights and the data-driven Beta distribution is employed to tune the weights based on the node popularity flexibly. At last, we conduct extensive experiments on three real-world datasets to demonstrate the effectiveness of TPA in improving recommendation accuracy and alleviating the popularity bias.

Serendipity recommendation has attracted more and more attention in recent years; it is committed to providing recommendations which could not only cater to users’ demands but also broaden their horizons. However, existing approaches usually measure user-item relevance with a scalar instead of a vector, ignoring user preference direction, which increases the risk of unrelated recommendations. In addition, reasonable explanations increase users’ trust and acceptance, but there is no work to provide explanations for serendipitous recommendations. To address these limitations, we propose a Directional and Explainable Serendipity Recommendation method named DESR. Specifically, we extract users’ long-term preferences with an unsupervised method based on GMM (Gaussian Mixture Model) and capture their short-term demands with the capsule network at first. Then, we propose the serendipity vector to combine long-term preferences with short-term demands and generate directionally serendipitous recommendations with it. Finally, a back-routing scheme is exploited to offer explanations. Extensive experiments on real-world datasets show that DESR could effectively improve the serendipity and explainability, and give impetus to the diversity, compared with existing serendipity-based methods.

Recommendation systems provide good guidance for users to find their favorite movies from an overwhelming amount of options. However, most systems excessively pursue the recommendation accuracy and give rise to over-specialization, which triggers the emergence of serendipity. Hence, serendipity recommendation has received more attention in recent years, facing three key challenges: subjectivity in the definition, the lack of data, and users' floating demands for serendipity. To address these challenges, we introduce a new model called HAES, a H ybrid A pproach for movie recommendation with E lastic S erendipity, to recommend serendipitous movies. Specifically, we (1) propose a more objective definition of serendipity, \em content difference and \em genre accuracy, according to the analysis on a real dataset, (2) propose a new algorithm named JohnsonMax to mitigate the data sparsity and build weak ties beneficial to finding serendipitous movies, and (3) define a novel concept of elasticity in the recommendation, to adjust the level of serendipity flexibly and reach a trade-off between accuracy and serendipity. Extensive experiments on real-world datasets show that HAES enhances the serendipity of recommendations while preserving recommendation quality, compared to several widely used methods.