The rapid expansion of gaming industry requires advanced recommender systems tailored to its dynamic landscape. Existing Graph Neural Network (GNN)-based methods primarily prioritize accuracy over diversity, overlooking their inherent tradeoff. To address this, we previously proposed CPGRec, a balance-oriented gaming recommender system. However, CPGRec fails to account for critical disparities in player–game interactions, which carry varying significance in reflecting players’ personal preferences and may exacerbate over-smoothness issues inherent in GNN-based models. Moreover, existing approaches underutilize the reasoning capabilities and extensive knowledge of large language models (LLMs) in addressing these limitations. To bridge this gap, we propose two new modules. First, Preference-informed Edge Reweighting (PER) module assigns signed edge weights to qualitatively distinguish significant player interests and disinterests while then quantitatively measuring preference strength to mitigate over-smoothing in graph convolutions. Second, Preference-informed Representation Generation (PRG) module leverages LLMs to generate contextualized descriptions of games and players by reasoning personal preferences from comparing global and personal interests, thereby refining representations of players and games. Experiments on two Steam datasets demonstrate CPGRec+’s superior accuracy and diversity over state-of-the-art models. The code is accessible at https://github.com/HsipingLi/CPGRec-Plus .

Sequential recommendation aims to predict users’ next interactions by analyzing historical behavioral data. Traditional methods typically focus on learning fine-grained feature representations or extracting high-level user preferences to enhance recommendation accuracy. However, they often overlook the dynamic nature of user demand, which can shift over short periods and may resemble random noise. In our previous work, we introduced CoDeR, a framework that captures demand shifts and mitigates confounding biases through backdoor adjustment. Despite its effectiveness, CoDeR has limitations in its causal relation modeling, particularly in neglecting the role of user interest as a confounder. In this work, we propose CoDeR+, an enhanced framework that refines key components of CoDeR. First, we extend the original User Demand Extraction module into Interest-aware User Demand Modeling, introducing two submodules that explicitly model user interest and integrate it into demand representations. Second, we introduce a new Robust Counterfactual Demand Reasoning module, where user interest is treated as an additional confounder alongside demand drift, improving the causal correction process. Additionally, we provide a rigorous theoretical analysis of the updated backdoor adjustment and propose a simplified probability estimation method that reduces computational complexity. Extensive experiments on four real-world datasets demonstrate the effectiveness of CoDeR+. The source code for both CoDeR and CoDeR+ is publicly available at https://github.com/hellolst23/CoDeR .

Long-term time series forecasting (LTSF) aims to predict future values for a long time based on historical data. The period term is an essential component of the time series, which is complex yet important for LTSF. Although existing studies have achieved promising results, they still have limitations in modeling dynamic complicated periods. Most studies only focus on static periods with fixed time steps, while very few studies attempt to capture dynamic periods in the time domain. In this paper, we dissect the original time series in time and frequency domains and empirically find that changes in periods are more easily captured and quantified in the frequency domain. Based on this observation, we propose to explore dynamic period features using rotation in the frequency domain. To this end, we develop the frequency-based rotation network (FRNet), a novel LTSF method to effectively capture the features of the dynamic complicated periods. FRNet decomposes the original time series into period and trend components. Based on the complex-valued linear networks, it leverages a period frequency rotation module to predict the period component and a patch frequency rotation module to predict the trend component, respectively. Extensive experiments on seven real-world datasets consistently demonstrate the superiority of FRNet over various state-of-the-art methods. The source code is available at https://github.com/SiriZhang45/FRNet.

In recent years, the video game industry has experienced substantial growth, presenting players with a vast array of game choices. This surge in options has spurred the need for a specialized recommender system tailored for video games. However, current video game recommendation approaches tend to prioritize accuracy over diversity, potentially leading to unvaried game suggestions. In addition, the existing game recommendation methods commonly lack the ability to establish strict connections between games to enhance accuracy. Furthermore, many existing diversity-focused methods fail to leverage crucial item information, such as item category and popularity during neighbor modeling and message propagation. To address these challenges, we introduce a novel framework, called CPGRec, comprising three modules, namely accuracy-driven, diversity-driven, and comprehensive modules. The first module extends the state-of-the-art accuracy-focused game recommendation method by connecting games in a more stringent manner to enhance recommendation accuracy. The second module connects neighbors with diverse categories within the proposed game graph and harnesses the advantages of popular game nodes to amplify the influence of long-tail games within the player-game bipartite graph, thereby enriching recommendation diversity. The third module combines the above two modules and employs a new negative-sample rating score reweighting method to balance accuracy and diversity. Experimental results on the Steam dataset demonstrate the effectiveness of our proposed method in improving game recommendations. The dataset and source codes are anonymously released at: https://github.com/CPGRec2024/CPGRec.git.

Fashion recommendation has become a prominent focus in the realm of online shopping, with various tasks being explored to enhance the customer experience. Recent research has particularly emphasized fashion recommendation based on body shapes, yet a critical aspect of incorporating multimodal data relevance has been overlooked. In this paper, we present the Contrastive Multimodal Cross-Attention Network, a novel approach specifically designed for fashion recommendation catering to diverse body shapes. By incorporating multimodal representation learning and leveraging contrastive learning techniques, our method effectively captures both inter- and intra-sample relationships, resulting in improved accuracy in fashion recommendations tailored to individual body types. Additionally, we propose a locality-aware cross-attention module to align and understand the local preferences between body shapes and clothing items, thus enhancing the matching process. Experimental results conducted on a diverse dataset demonstrate the state-of-the-art performance achieved by our approach, reinforcing its potential to significantly enhance the personalized online shopping experience for consumers with varying body shapes and preferences.

Collaborative filtering (CF) has been extensively studied in recommendation, spawning various solutions. While graph convolution networks (GCNs) are effective at representation learning, their efficiency is lacking. Comparatively, item similarity model efficiently establishes direct relationships between items. In spite of this, the modeling problem grows quadratically as the number of items increases. This poses critical scalability issues. In this paper, through an investigation of the latest GCN model, we reveal the feasibility of optimizing the process of similarity modeling using the underlying group structure in the item set. Based on these findings, we propose a novel model which introduces graph partitioning to reduce the scale of similarity modeling problem, dubbed FPSR+. Specifically, we divide similarity modeling of items into sub-problems within each partition, and incorporate global and local prior knowledge to alleviate information loss. Following an analysis of the properties of different items in partitioning, we propose a new hub set selection strategy that improves the robustness of FPSR+ in the small partition case. Extensive experiments on four real-world datasets demonstrate the superior performance of FPSR+ compared with state-of-the-art GCN models and item similarity models, as well as several-fold speedups and reductions in parameter storage.

In collaborative filtering, distance metric learning has been applied to matrix factorization techniques with promising results. However, matrix factorization lacks the ability of capturing collaborative information, which has been remarked by recent works and improved by interpreting user interactions as signals. This paper aims to find out how metric learning connect to these signal-based models. By adopting a generalized distance metric, we discovered that in signal-based models, it is easier to estimate the residual of distances, which refers to the difference between the distances from a user to a target item and another item, rather than estimating the distances themselves. Further analysis also uncovers a link between the normalization strength of interaction signals and the novelty of recommendation, which has been overlooked by existing studies. Based on the above findings, we propose a novel model to learn a generalized distance user-item distance metric to capture user preference in interaction signals by modeling the residuals of distance. The proposed CoRML model is then further improved in training efficiency by a newly introduced approximated ranking weight. Extensive experiments conducted on 4 public datasets demonstrate the superior performance of CoRML compared to the state-of-the-art baselines in collaborative filtering, along with high efficiency and the ability of providing novelty-promoted recommendations, shedding new light on the study of metric learning-based recommender systems.

Collaborative filtering (CF) is widely searched in recommendation with various types of solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effectiveness of modeling high-order relationships through graphs, while repetitive graph convolution and iterative batch optimization limit their efficiency. Instead, item similarity models attempt to construct direct relationships through efficient interaction encoding. Despite their great performance, the growing item numbers result in quadratic growth in similarity modeling process, posing critical scalability problems. In this paper, we investigate the graph sampling strategy adopted in latest GCN model for efficiency improving, and identify the potential item group structure in the sampled graph. Based on this, we propose a novel item similarity model which introduces graph partitioning to restrict the item similarity modeling within each partition. Specifically, we show that the spectral information of the original graph is well in preserving global-level information. Then, it is added to fine-tune local item similarities with a new data augmentation strategy acted as partition-aware prior knowledge, jointly to cope with the information loss brought by partitioning. Experiments carried out on 4 datasets show that the proposed model outperforms state-of-the-art GCN models with 10x speed-up and item similarity models with 95% parameter storage savings.