Data clustering aims to partition the input data entities into several disjoint categories, where similar entities are grouped together while dissimilar ones are pulled apart. In general, the existing data clustering methods merely depend on the attribute information or constructed similarity graph information of the input data entities, and such one-sided cues may result in an incomplete understanding and potentially biased conclusions. How to well consider the inherent heterogeneous knowledge of data while maintaining the local semantics remains a challenging problem. To address this, we propose a novel knowledge-aware clustering (KAC) method, where the attribute information and inherent heterogeneous knowledge of data are jointly considered for better cluster structure recovery. Within this framework, there are four major modules. They are: 1) the target attribute autoencoder to capture a customizable target feature representation; 2) the meta-path aggregated knowledge graph encoder to learn an informative graph feature representation; 3) the dual information maximization to ensure the consistent semantics between two embedding representations; and 4) the self-training clustering to self-supervise the clustering learning. Experiments on several real-world datasets are conducted to validate the superiority of KAC, indicating the significance of explicitly considering the heterogeneous knowledge while maintaining local semantics.

In the digital age, recommendation systems are crucial for enhancing user experiences, with bundle recommendations playing a key role by integrating complementary products. However, existing methods fail to accurately identify user preferences for specific items within bundles, making it difficult to design bundles containing more items of interest to users. Additionally, these methods do not leverage similar preferences among users of the same category, resulting in unstable and incomplete preference expressions. To address these issues, we propose Preference Identification by Interaction Overlap for Bundle Recommendation (PIIO). The data augmentation module analyzes the overlap between bundle-item inclusions and user-item interactions to calculate the interaction probability of non-interacted bundles, selecting the bundle with the highest probability as a positive sample to enrich user-bundle interactions and uncover user preferences for items within bundles. The preference aggregation module utilizes the overlap in user-item interactions to select similar users, aggregates preferences using an autoencoder, and constructs comprehensive preference profiles. The optimization module predicts user-bundle matching scores based on a user interest boundary loss function. The proposed PIIO model is applied to two bundle recommendation datasets, and experiments demonstrate the effectiveness of the PIIO model, surpassing state-of-the-art models.

In the evolving landscape of sequential recommendation systems, the application of Large Language Models (LLMs) is increasingly prominent. However, current attempts typically utilize general-purpose LLMs, which present a mismatch in capability and a large semantic gap relative to the specialized needs of recommendation tasks. To tackle these issues, we introduce RecCoder, an innovative model that reformulates sequential recommendation as a code completion task. This approach leverages the superior reasoning capability of code LLMs as a backbone, aligning well with the requirements of recommendation systems. To bridge the semantic gap, RecCoder creates extra tokens for each item and employs item content to initialize token embeddings. Furthermore, we have developed a suite of Semantic Adaptation Fine-tuning tasks, tailored to enhance the model's acquisition of both content and collaborative semantic information, thus aligning the model's intrinsic capabilities with the unique demands of recommendation tasks. Through extensive testing on three public datasets, RecCoder has shown remarkable improvements over existing models in terms of recommendation accuracy and efficiency. This success highlights the substantial yet previously underexplored potential of code LLMs in improving recommendation accuracy and efficiency, suggesting a promising new direction for future research in this area. The implementation code is accessible at https://github.com/AllminerLab/Code-for-RecCoder-master.

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive Learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks.

Next-basket recommendation methods focus on the inference of the next basket by considering the corresponding basket sequence. Although many methods have been developed for the task, they usually suffer from data sparsity. The number of interactions between entities is relatively small compared to their huge bases, so it is crucial to mine as much hidden information as possible from the limited historical interactions for prediction. However, the existing methods mainly just treat the next-basket recommendation task as a single-view sequential prediction problem, which leads to the inadequate mining of the information hidden in multiple views, and the mining of other patterns in the historical interactions is neglected, thus making it difficult to learn high-quality representations and limiting the recommendation effect. To alleviate the above issues, we propose a novel method named HapCL for next-basket recommendation, which mines information from multiple views and patterns with the help of polar contrastive learning. A hierarchical module is designed to mine multiple patterns of historical interactions from different views at two levels. In order to mine self-supervised signals, we design a polar contrastive learning module with a novel graph-based augmentation approach. Experiments on three real-world datasets validate the effectiveness of HapCL.

Community detection has become a prominent task in complex network analysis. However, most of the existing methods for community detection only focus on the lower order structure at the level of individual nodes and edges and ignore the higher order connectivity patterns that characterize the fundamental building blocks within the network. In recent years, researchers have shown interest in motifs and their role in network analysis. However, most of the existing higher order approaches are based on shallow methods, failing to capture the intricate nonlinear relationships between nodes. In order to better fuse higher order and lower order structural information, a novel deep learning framework called motif-based contrastive learning for community detection (MotifCC) is proposed. First, a higher order network is constructed based on motifs. Subnetworks are then obtained by removing isolated nodes, addressing the fragmentation issue in the higher order network. Next, the concept of contrastive learning is applied to effectively fuse various kinds of information from nodes, edges, and higher order and lower order structures. This aims to maximize the similarity of corresponding node information, while distinguishing different nodes and different communities. Finally, based on the community structure of subnetworks, the community labels of all nodes are obtained by using the idea of label propagation. Extensive experiments on real-world datasets validate the effectiveness of MotifCC.

In recent years, the gaming industry has flourished. Therefore, game manufacturers need to strive to improve the gaming experience of users in the game. Social recommendation tasks in game scenes have become increasingly important. In this work, we focus on community recommendation scenario. A distinctive feature of game community recommendation is that each member can only belong to one gang for a certain duration, which we refer to as uniqueness of communities. The problem caused by uniqueness is that for users to be recommended, there are no positive samples available for training. The challenge caused by uniqueness is that there are no positive samples available for training when users are recommended. Therefore, the collaborative filtering information between the user and the community is very sparse. Meanwhile, existing methods fail to fully model communities and users based on their features and profiles. To address these problems, we propose Hypergraph Attribute Attention Network (HATT) framework. In order to fully exploit user profiles and similarity between users, we discretize user features into entity nodes and model the heterogeneous relationships between users and communities by hyperedge. We propose a hypergraph attention-based message passing mechanism to capture the high-order relationships and obtain embedding with more semantics. At last, we design contrastive learning paradigms to enhance the model’s representation ability and apply a multi task training strategy to train the model. Extensive experiments on two real world game datasets are conducted and the results demonstrate the superiority of our method in community recommendation.

Bundle recommendation focuses on recommending users with associated item sets at once. Recently, some works utilize Graph Neural Network (GNN), which has a solid power for mining information behind the topological structure, to enhance bundle recommendation performance. The previous GNN-based methods focus on designing the bundle-item association mechanism and fusing the extra information from the item view into the final prediction. However, the crucial component in GNN, namely the neighborhood aggregation mechanism, is yet to be explored under the bundle recommendation scenario. In this work, we propose a bundle-specific neighborhood aggregation mechanism named Auto Graph Filtering (AGF). The AGF refines the neighborhood aggregation mechanism from two aspects. (1) AGF utilizes the 2-hop meta paths in the bundle recommendation scenario instead of user interactions directly, which alleviates the extreme sparsity in the user-bundle graph. (2) AGF automatically reweights all the meta paths during the training. With training procedure completed, AGF optimizes the user-bundle graph to meet the bundle recommendation requirement. The experimental results show that our simplest AGF version, AGFN, consistently outperforms all the baselines. Moreover, the user-bundle graph learned by AGFN could also boost the existing GNN-based methods to achieve a better performance.

In recent years, various recommendation methods are proposed to capture user preferences more accurately, with the assumption that different types of records reflects the positive intention of users to buy items in different degree. However, the records of different channels may denote positive or negative impacts on users’ willingness to buy items in multi-channel scenario, which is a salient features of games. Making recommendation only with the records of buy channel makes it difficult to capture cross-channel impact of items. To solve the issue, this paper proposes a multi-channel gated gifts recommendation method, named MCRec, which is able to mine the impact of acquisition in different channels on buy channel from multi-channel records and generate personalized gifts for users. The MCRec method extracts channel-aware correlation of items from channel-aware item-item graphs. The contribution of different items in various sessions is distinguished with item gate, and the impact of the context of different channels on buy channel is measured with channel gate. Finally, personalized gifts will be generated hierarchically with different purposes. Extensive experiments are conducted on two datasets that are constructed with the data collected from two massively multiplayer online (MMO) games. The results demonstrate the superiority of our MCRec over state-of-the-art recommendation methods in gifts recommendation. Further ablation studies validate the effectiveness of the design of MCRec in modeling cross-channel impact of items.

Ensemble clustering has shown its promising ability in fusing multiple base clusterings into a probably better and more robust clustering result. Typically, the co-association matrix based ensemble clustering methods attempt to integrate multiple connective matrices from base clusterings by weighted fusion to acquire a common graph representation. However, few of them are aware of the potential noise or corruption from the common representation by direct integration of different connective matrices with distinct cluster structures, and further consider the mutual information propagation between the input observations. In this paper, we propose a Graph Tensor Learning based Ensemble Clustering (GTLEC) method to refine multiple connective matrices by the substantial rank recovery and graph tensor learning. Within this framework, each input connective matrix is dexterously refined to approximate a graph structure by obeying the theoretical rank constraint with an adaptive weight coefficient. Further, we stack multiple refined connective matrices into a three-order tensor to extract their higher-order similarities via graph tensor learning, where the mutual information propagation across different graph matrices will also be promoted. Extensive experiments on several challenging datasets have confirmed the superiority of GTLEC compared with the state-of-the-art.

The Social Media Prediction (SMP) challenge aims to predict the future popularity of online posts by leveraging social media data. Social media data contains multimodal information, such as text, images, time series, etc. Previous methods have proposed many feature extraction and feature construction methods to represent these multimodal information, thereby predicting the popularity of posts. Despite the success of previous methods in extracting features from social media data, these features tend to be predominantly lower-order, posing a challenge in accurately capturing the rich information contained in text and images. In this paper, we propose a more diverse feature mining method and introduce a stacking block module to capture higher-order feature information contained in text and images. "lower-order" refers to the original high-dimensional embedding representation, while "high-order" pertains to the impact on post social popularity captured by tree models from text or image. We conducted massive experiments to evaluate the effectiveness of our proposed method and found that the stacking block module significantly improved performance.

Recently, some attempts have been made in introducing deep neural networks (DNNs) to recommender systems for generating more accurate prediction due to the nonlinear representation learning capability of DNNs. However, they inevitably result in high computational and storage costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may easily suffer from the overfitting issue. To tackle these issues, we propose a novel recommendation framework based on Back Propagation (BP) neural network with attention mechanism, namely BPAM++. In particular, the BP neural network is utilized to learn the complex relationship between the target user and his/her neighbors and the complex relationship between the target item and its neighbors. Compared with DNNs, the shallow neural network, i.e., BP neural network, can not only reduce the computational and storage costs, but also alleviate the overfitting issues in DNNs caused by a relatively small number of ratings. In addition, an attention mechanism is designed to capture the global impact of the nearest users of the target user on their nearest target user sets. Extensive experiments conducted on eight benchmark datasets confirm the effectiveness of the proposed model.

To alleviate the sparsity issue, many recommender systems have been proposed to consider the review text as the auxiliary information to improve the recommendation quality. Despite success, they only use the ratings as the ground truth for error backpropagation. However, the rating information can only indicate the users' overall preference for the items, while the review text contains rich information about the users' preferences and the attributes of the items. In real life, reviews with the same rating may have completely opposite semantic information. If only the ratings are used for error backpropagation, the latent factors of these reviews will tend to be consistent, resulting in the loss of a large amount of review information. In this article, we propose a novel deep model termed deep rating and review neural network (DRRNN) for recommendation. Specifically, compared with the existing models that adopt the review text as the auxiliary information, DRRNN additionally considers both the target rating and target review of the given user-item pair as ground truth for error backpropagation in the training stage. Therefore, we can keep more semantic information of the reviews while making rating predictions. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DRRNN model in terms of rating prediction.

Collaborative filtering (CF) is a classical model for recommender systems. Though neural network-based CF models have been shown to perform well in some cases, they still suffer from the sparsity and cold-start issues. To address these issues, we propose a novel neural network-based CF model, termed the cross-domain explicit–implicit-mixed CF neural network (CEICFNet). The proposed model utilizes deep neural networks to learn latent factors not only from the explicit ratings and the implicit interactions but also in a cross-domain manner. In particular, domain-shared multilayer perception (MLP) networks are designed to learn the user rating latent factors and the user interaction latent factors from the explicit ratings and the implicit interactions, respectively, which act as bridges for transferring knowledge across domains. On the other hand, domain-specific MLP networks are designed to learn the item rating latent factors and the item interaction latent factors from the explicit ratings and the implicit interactions, respectively. Then, in each domain, based on the user rating latent factors and the item rating latent factors, the rating predictive representation for each user–item pair can be learned by an MLP. Similarly, the interaction predictive representation for each user–item pair can be learned. For integrating the explicit ratings and the implicit interactions, in each domain, a fully connected layer is used to automatically assign different weights to the rating predictive representations and the interaction predictive representations, based on which the final interaction probability can be generated. Extensive experiments are conducted on five datasets and the results have confirmed the effectiveness of our model.

The aim of session-based recommendation is to predict the next-clicked item based on the anonymous behavior sequence. The existing works on session-based recommendation mainly capture the user preference within an individual session. This paper proposes a novel approach, called Session-based Recommendation with Heterogeneous Graph Neural Networks (SR-HGNN) to exploit cross-session information for better inferring the user preference of the current session. Specifically, we propose to use a heterogeneous graph to model the current session sequence and cross-session information simultaneously. After that, we come up with a novel model to pass messages along edges of different types hierarchically. Extensive experiments conducted on three real-world datasets demonstrate the superiority of SR-HGNN by comparing with different state-of-the-art baselines.

Inspired by the significant success of deep learning, some attempts have been made to introduce deep neural networks (DNNs) in recommendation systems to learn users' preferences for items. Since DNNs are well suitable for representation learning, they enable recommendation systems to generate more accurate prediction. However, they inevitably result in high computational and storage costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may easily lead to over-fitting. To tackle these problems, we propose a novel recommendation algorithm based on Back Propagation (BP) neural network with Attention Mechanism (BPAM). In particular, the BP neural network is utilized to learn the complex relationship of the target users and their neighbors. Compared with deep neural network, the shallow neural network, i.e., BP neural network, can not only reduce the computational and storage costs, but also prevent the model from over-fitting. In addition, an attention mechanism is designed to capture the global impact on all nearest target users for each user. Extensive experiments on eight benchmark datasets have been conducted to evaluate the effectiveness of the proposed model.