Learning User Preferences Research Articles

Federated Latent Dirichlet Allocation for User Preference Mining

In the field of Web services computing, a recent demand trend is to mine user preferences based on user requirements when creating Web service compositions, in order to meet comprehensive and ever evolving user needs. Machine learning methods such as the latent Dirichlet allocation (LDA) have been applied for user preference mining. However, training a high-quality LDA model typically requires large amounts of data. With the prevalence of government regulations and laws and the enhancement of people’s awareness of privacy protection, the traditional way of collecting user data on a central server is no longer applicable. Therefore, it is necessary to design a privacy-preserving method to train an LDA model without massive collecting or leaking data. In this paper, we present novel federated LDA techniques to learn user preferences in the Web service ecosystem. On the basis of a user-level distributed LDA algorithm, we establish two federated LDA models in charge of two-layer training scenarios: a centralized synchronous federated LDA (CSFed-LDA) for synchronous scenarios and a decentralized asynchronous federated LDA (DAFed-LDA) for asynchronous ones. In the former CSFed-LDA model, an importance-based partially homomorphic encryption (IPHE) technique is developed to protect privacy in an efficient manner. In the latter DAFed-LDA model, blockchain technology is incorporated and a multi-channel-based authority control scheme (MCACS) is designed to enhance data security. Extensive experiments over a real-world dataset ProgrammableWeb.com have demonstrated the model performance, security assurance and training speed of our approach.

A VAE-Based User Preference Learning and Transfer Framework for Cross-Domain Recommendation

The core idea of cross-domain recommendation is to alleviate the problem of data scarcity. Previous methods have made brilliant successes. However, many of them mainly focus on learning an ideal mapping function across-domains, ignoring the user preferences within a specific domain, which leads to suboptimal results. In this paper, we propose a Cross-Domain Recommendation Variational AutoEncoder framework (CDRVAE), a novel extension of a variational autoencoder on cross-domain recommendations for user behaviour distribution modeling. It applies a new hybrid architecture of VAE as the backbone and simultaneously constructs two information flows, within-domain and cross-domain modeling. For the former, an asymmetric codec structure is designed to reconstruct preference distribution from domain-specific latent factors. To relieve the posterior collapse dilemma, a combined prior is employed to increase the distribution complexity. The equivalent transition by a transformation matrix and the unobserved interaction generation by cross-domain reconstruction contribute to the latter. We combine all the above components for the more accurate and reliable user features. Extensive experiments are conducted on three public benchmark datasets to validate the effectiveness of the proposed CDRVAE. Experimental results demonstrate that CDRVAE is consistently superior to other state-of-the-art alternative baseline models.

MetaKG: Meta-Learning on Knowledge Graph for Cold-Start Recommendation

Recently, knowledge graphs (KGs) are popularly used as the auxiliary information to enable more accurate, explainable, and diverse user preference recommendations. Specifically, existing KG-based recommendation methods target modeling high-order relations/dependencies from long connectivity user-item interactions hidden in KG. However, most of them ignore the cold-start problems (i.e., user cold-start and item cold-start) of recommendation analytics, which restricts their performance in scenarios when involving new users or new items. Inspired by the success of meta-learning on scarce training samples, we propose a novel meta-learning based framework called MetaKG, which encompasses a collaborative-aware meta learner and a knowledge-aware meta learner, to capture meta users' preference and entities' knowledge for cold-start recommendations. The collaborative-aware meta learner aims to locally aggregate user preferences for each user preference learning task. In contrast, the knowledge-aware meta learner is to globally generalize knowledge representation across different user preference learning tasks. Guided by two meta learners, MetaKG can effectively capture the high-order collaborative relations and semantic representations, which could be easily adapted to cold-start scenarios. Extensive experiments on various cold-start scenarios using three real datasets demonstrate that our presented MetaKG outperforms all the existing state-of-the-art competitors in terms of effectiveness, efficiency, and scalability.

MM-FRec: Multi-Modal Enhanced Fashion Item Recommendation

Existing studies on fashion item recommendation mainly focused on incorporating the visual signals of items to boost the user preference learning, while overlooking the semantic attributes (e.g., material and brand) of fashion items that also contain important cues about items' properties and users' preference. To bridge this gap, we aim to comprehensively explore the attribute and vision modalities of items to improve the fashion item recommendation performance. However, this is non-trivial due to the latent visual-semantic consistency, various relation types, and unique attributes with insufficient samples. To address these challenges, we propose a Multi-Modal enhanced Fashion item Recommendation scheme (MM-FRec). Specifically, to cope with the multi-modal data, we introduce a relation-oriented graph as well as a vision-oriented graph, and design MM-FRec with three key components: attribute-enhanced latent representation learning, visual representation learning, and multi-modal enhanced preference modeling. To deal with the various relation types, we present a new relation-aware propagation method for adaptively aggregating the information from neighbor nodes to promote the user and item representation learning. To cope with the unique attributes, we introduce the deep multi-task learning strategy in the relation-aware confidence assignment. Extensive experiments on a real-world dataset demonstrate the superiority of our model over state-of-the-art methods.

Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation

Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing attention by leveraging data from various auxiliary behaviors such as tip and collect. Among various multi-behavior recommendation methods, non-sampling methods have shown superiority over negative sampling methods. However, two observations are usually ignored in existing state-of-the-art non-sampling methods based on binary regression: (1) users have different preference strengths for different items, so they cannot be measured simply by binary implicit data; (2) the dependency across multiple behaviors varies for different users and items. To tackle the above issue, we propose a novel non-sampling learning framework named C riterion-guided H eterogeneous C ollaborative F iltering (CHCF). CHCF introduces both upper and lower thresholds to indicate selection criteria, which will guide user preference learning. Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior. We further theoretically demonstrate that the optimization of Collaborative Metric Learning can be approximately achieved by the CHCF learning framework in a non-sampling form effectively. Extensive experiments on three real-world datasets show the effectiveness of CHCF in heterogeneous scenarios.

A Multi-channel Next POI Recommendation Framework with Multi-granularity Check-in Signals

Current study on next point-of-interest (POI) recommendation mainly explores user sequential transitions with the fine-grained individual-user POI check-in trajectories only, which suffers from the severe check-in data sparsity issue. In fact, coarse-grained signals (i.e., region- and global-level check-ins) in such sparse check-ins would also benefit to augment user preference learning. Specifically, our data analysis unveils that user movement exhibits noticeable patterns w.r.t. the regions of visited POIs. Meanwhile, the global all-user check-ins can help reflect sequential regularities shared by the crowd. We are, therefore, inspired to propose the MCMG: a Multi-Channel next POI recommendation framework with Multi-Granularity signals categorized from two orthogonal perspectives, i.e., fine-coarse grained check-ins at either POI/region level or local/global level. The MCMG is equipped with three modules, namely, global user behavior encoder, local multi-channel (i.e., region, category, and POI channels) encoder, and region-aware weighting strategy. Such design enables MCMG to be capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the check-in patterns w.r.t. visited regions. Extensive experiments on four real-world datasets show that our MCMG significantly outperforms state-of-the-art next POI recommendation approaches.

Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation

Multi-behavior recommendation exploits multiple types of user-item interactions, such as view and cart , to learn user preferences and has demonstrated to be an effective solution to alleviate the data sparsity problem faced by the traditional models that often utilize only one type of interaction for recommendation. In real scenarios, users often take a sequence of actions to interact with an item, in order to get more information about the item and thus accurately evaluate whether an item fits their personal preferences. Those interaction behaviors often obey a certain order, and more importantly, different behaviors reveal different information or aspects of user preferences towards the target item. Most existing multi-behavior recommendation methods take the strategy to first extract information from different behaviors separately and then fuse them for final prediction. However, they have not exploited the connections between different behaviors to learn user preferences. Besides, they often introduce complex model structures and more parameters to model multiple behaviors, largely increasing the space and time complexity. In this work, we propose a lightweight multi-behavior recommendation model named Cascading Residual Graph Convolutional Network ( CRGCN for short) for multi-behavior recommendation, which can explicitly exploit the connections between different behaviors into the embedding learning process without introducing any additional parameters (with comparison to the single-behavior based recommendation model). In particular, we design a cascading residual graph convolutional network (GCN) structure, which enables our model to learn user preferences by continuously refining the embeddings across different types of behaviors. The multi-task learning method is adopted to jointly optimize our model based on different behaviors. Extensive experimental results on three real-world benchmark datasets show that CRGCN can substantially outperform the state-of-the-art methods, achieving 24.76%, 27.28%, and 25.10% relative gains on average in terms of HR@K (K = {10,20,50,80}) over the best baseline across the three datasets. Further studies also analyze the effects of leveraging multi-behaviors in different numbers and orders on the final performance.

Learning User Preferences for Complex Cobotic Tasks: Meta-Behaviors and Human Groups

In complex tasks (beyond a single targeted controller) requiring robots to collaborate with multiple human users, two challenges arise: complex tasks are often composed of multiple behaviors which can only be evaluated as a collective (a meta-behavior) and user preferences often differ between individuals, yet successful interactions are expected across groups. To address these challenges, we formulate a set-wise preference learning problem, and validate a cost function that captures human group preferences for complex collaborative robotic tasks (cobotics). We develop a sparse optimization formulation to introduce a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distinctiveness</i> metric that aggregates individuals with similar preference profiles. Analysis of anonymized unlabelled preferences provides further insight into group preferences. Identification of the mode average most-preferred meta-behavior and minimum covariance bound allows us to analyze <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">group cohesion</i> . A user study with 43 participants is used to validate group preference profiles.

User-Preference-Learning-Based Proactive Edge Caching for D2D-Assisted Wireless Networks

This work investigates proactive edge caching for device-to-device (D2D) assisted wireless networks, where user equipment (UE) can be selected as caching nodes to assist content delivery to reduce the content transmission latency. In doing so, there are two challenges: 1) How to precisely get the user’s preference to cache the proper contents at UEs; 2) How to replace the contents cached at UEs when there are new popular contents emerging. To address these, we develop a user preference learning-based proactive edge caching (UPL-PEC) strategy. In the strategy, we first propose a novel context and social-aware user preference learning method to precisely predict user’s dynamic preferences by jointly exploiting the context correlation among different contents, the influence of social relationships and the time-sequential patterns of user’s content requests. Specifically, the bidirectional long short term memory networks are adopted to capture the time-sequential patterns of the user’s content requests. And, the graph convolutional networks are developed to capture the high-order similarity representation among different contents from the constructed content graph. To learn the social influence representation, an attention mechanism is designed to generate the social influence weights to users with different social relationship. Based on the learned user preference, a proactive edge caching architecture is proposed to integrate the offline caching content placement and the online caching content replacement policy to continuously cache the popular contents at UEs. Simulation results show that the proposed UPL-PEC strategy outperforms the existing similar caching strategies at about 3.13%-4.62% in terms of the average content transmission latency.

Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation

Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transition pattern from the previous item to the next one, ignoring the time interval between these two items. However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of preference drift. In fact, we conducted an empirical study to validate this observation, and found that a sequence with uniformly distributed time interval (denoted as uniform sequence) is more beneficial for performance improvement than that with greatly varying time interval. Therefore, we propose to augment sequence data from the perspective of time interval, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-Reorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of variance of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths. Finally, we implement these improvements on a state-of-the-art model CoSeRec and validate our approach on four real datasets. The experimental results show that our approach reaches significantly better performance than the other 9 competing methods. Our implementation is available: https://github.com/KingGugu/TiCoSeRec.

A GRU-CNN Algorithm Leveraging on User Reviews

Personalized recommendation systems learn user preference characteristics by analyzing behavioral data such as ratings and comments generated by users in the Internet, and provide precise recommendations for individual users accordingly. However, in real life, users often conduct group activities like group buying and traveling together. How to recommend for groups has become a heated research topic in recent years. Most existing group recommendation algorithms are recommended for given divided groups by collectively combining the preferences of members in the group. However, in most cases, users’ group properties are fickle. As the results of group detection are decisive to the performance of group recommendation, group detection is particularly important to the group recommendation algorithm. After analyzing problems of existing group recommendation algorithms, this paper proposes the density peak clustering group detection algorithm based on GRU-CNN and the group recommendation algorithm based on the mechanism. With respect to group detection, most of the existing group detection algorithms suffer from certain deficiencies: First, depending solely on the users’ static preference features while ignoring the variation of users’ interest over time when finding the group structure in the network; second, group division based on users’ topic features extracted from reviews is difficult to support further digging of the in-depth features in reviews. To address the above-mentioned problems, this paper proposes a density peak clustering group detection algorithm based on CNN-GRU. It would first extract representative keywords in the reviews with LDA topic model, and then model time series information based on GRU attaining users’ dynamic topic features. Coupling with deeper characteristics cored out by CNN, density peak clustering algorithm completes its group detection finally. Experiments on real dataset indicate that the features mined by the fusion depth neural network model effectively capture users’ dynamic preferences, and yield better results of group detection than that of existing algorithms.

CP-nets-based user preference learning in automated negotiation through completion and correction

CP-nets-based user preference learning in automated negotiation through completion and correction

Topic-aware Intention Network for Explainable Recommendation with Knowledge Enhancement

Recently, recommender systems based on knowledge graphs (KGs) have become a popular research direction. Graph neural network (GNN) is the key technology of KG-based recommendation systems. However, existing GNNs have a significant flaw: They cannot explicitly model users’ intent in recommendations. Intent plays an essential role in users’ behaviors. For example, users may first generate an intent to purchase a certain group of items and then select a specific item from the group based on their preferences. Therefore, explicitly modeling intent has a positive significance for improving recommendation performance and providing explanations for recommendations. In this article, we propose a new model called Topic-aware Intention Network (TIN) for explainable recommendations with KGs. TIN models user representations from both preference and intent views. Specifically, we design a relational attention graph neural network to selectively aggregate information in KG to learn user preferences, and we propose a knowledge-enhanced topic model to learn user intent, which is viewed as topics hidden in user behavior sequences. Finally, we obtain the user representation by fusing user preference and intent through an attention network. The experimental results show that our proposed model outperforms the state-of-the-art methods and can generate reasonable explanations for the recommendation results.

Multi-cloud service provision based on decision tree and two-layer Restricted Monte Carlo Tree Search

Compared with traditional service provision methods, cloud computing has many advantages, such as on-demand services and flexible pricing systems, attracting widespread attention from both academia and industry. However, the abundance of cloud services makes it difficult for users to choose suitable ones. Furthermore, the cloud is usually a dynamically changing execution environment, requiring users to constantly switch service carriers for the best experience. Therefore, to improve the efficiency of service interactions in a multi-cloud environment and cope with the high concurrency demands, this paper proposes a multi-agent and distributed broker-based cloud service provisioning model. Equipped with a decision tree-based user preference learning module, brokers in the proposed model can continuously adjust their resource introduction strategy through training on historical data. In the process of cloud service interaction, brokers select the best cost-effective service for users within a limited time through a two-layer Restricted Monte Carlo Tree Search (RMTCS) algorithm. This paper develops a multi-cloud service interconnection system based on the Libcloud framework. Shielding the different interfaces of different cloud service providers in cloud virtual machine and storage services provides the most suitable services for users and realizes the unified cloud federation service supply. The experiment results prove that the proposed model achieves better performance over the traditional methods and can better meet the service requirements of users.

Clustering of conversational bandits with posterior sampling for user preference learning and elicitation

Clustering of conversational bandits with posterior sampling for user preference learning and elicitation

A Next POI Recommendation Based on Graph Convolutional Network by Adaptive Time Patterns

Users’ activities in location-based social networks (LBSNs) can be naturally transformed into graph structural data, and more advanced graph representation learning techniques can be adopted for analyzing user preferences, which benefits a variety of real-world applications. This paper focuses on the next point-of-interest (POI) recommendation task in LBSNs. We argue that existing graph-based POI recommendation methods only consider user preferences from several individual contextual factors, ignoring the influence of interactions between different contextual information. This practice leads to the suboptimal learning of user preferences. To address this problem, we propose a novel method called hierarchical attention-based graph convolutional network (HAGCN) for the next POI recommendation, a technique which leverages graph convolutional networks to extract the representations of POIs from predefined graphs via different time patterns and develops a hierarchical attention mechanism to adaptively learn user preferences from the interactions between different contextual data. Moreover, HAGCN uses a dynamic preference estimation to precisely learn user preferences. We conduct extensive experiments on real-world datasets to evaluate the performance of HAGCN against representative baseline models in the field of next POI recommendation. The experimental results demonstrate the superiority of our proposed method on the next POI recommendation task.

CAML: Contextual augmented meta-learning for cold-start recommendation

The performance of recommendation engines is challenged by the cold-start issues. Classical recommendation techniques have limited capability to address this problem because the underlying machine learning models are data-thirsty. Conversely, the recent success of meta-learning attracted a lot of attention due to few-shot learning capabilities which can be employed to accommodate new tasks even when there are few data samples for training. Most of the meta-learning recommendation models adopt model-agnostic meta-learning to initialize parameters that may lead to stuck into local optima instead of global optima for some users. To leverage the learning process, we propose a Contextually Augmented Meta-Learning recommender system (CAML). The proposed method augments the contextual features into a meta-learning model which considerably improves the tasks adaptation capability. We constructed a Data Augmentation Unit (DAU) that used a hybrid similarity to augment data samples of similar neighbors. The augmented samples are then forwarded to a Meta-Learner (MetaL) to learn user preferences and generate relevant recommendations. Additionally, we highlighted that the proposed method is generalized and can be adapted for various meta-learning-based recommendation models. We validate our proposal on three benchmark datasets for both ranking and rating prediction perspectives. The experimental outcomes prove the significance of our proposal over the state-of-the-art recommendation methods.

Learning the User’s Deeper Preferences for Multi-modal Recommendation Systems

Recommendation system plays an important role in the rapid development of micro-video sharing platform. Micro-video has rich modal features, such as visual, audio, and text. It is of great significance to carry out personalized recommendation by integrating multi-modal features. However, most of the current multi-modal recommendation systems can only enrich the feature representation on the item side, while it leads to poor learning of user preferences. To solve this problem, we propose a novel module named Learning the User’s Deeper Preferences (LUDP) , which constructs the item-item modal similarity graph and user preference graph in each modality to explore the learning of item and user representation. Specifically, we construct item-item similar modalities graph using multi-modal features, the item ID embedding is propagated and aggregated on the graph to learn the latent structural information of items; The user preference graph is constructed through the historical interaction between the user and item, on which the multi-modal features are aggregated as the user’s preference for the modal. Finally, combining the two parts as auxiliary information enhances the user and item representation learned from the collaborative signals to learn deeper user preferences. Through a large number of experiments on two public datasets (TikTok, Movielens), our model is proved to be superior to the most advanced multi-modal recommendation methods.

User Cold-Start Recommendation via Inductive Heterogeneous Graph Neural Network

Recently, user cold-start recommendations have attracted a lot of attention from industry and academia. In user cold-start recommendation systems, the user attribute information is often used by existing approaches to learn user preferences due to the unavailability of user action data. However, most existing recommendation methods often ignore the sparsity of user attributes in cold-start recommendation systems. To tackle this limitation, this article proposes a novel Inductive Heterogeneous Graph Neural Network (IHGNN) model, which utilizes the relational information in user cold-start recommendation systems to alleviate the sparsity of user attributes. Our model converts new users, items, and associated multimodal information into a Modality-aware Heterogeneous Graph (M-HG) that preserves the rich and heterogeneous relationship information among them. Specifically, to utilize rich and heterogeneous relational information in an M-HG for enriching the sparse attribute information of new users, we design a strategy based on random walk operations to collect associated neighbors of new users by multiple times sampling operation. Then, a well-designed multiple hierarchical attention aggregation model consisting of the intra- and inter-type attention aggregating module is proposed, focusing on useful connected neighbors and neglecting meaningless and noisy connected neighbors to generate high-quality representations for user cold-start recommendations. Experimental results on three real datasets demonstrate that the IHGNN outperforms the state-of-the-art baselines.

Preference-aware Graph Attention Networks for Cross-Domain Recommendations with Collaborative Knowledge Graph

Knowledge graphs (KGs) can provide users with semantic information and relations among numerous entities and nodes, which can greatly facilitate the performance of recommender systems. However, existing KG-based approaches still suffer from severe data sparsity and may not be effective in capturing the preference features of similar entities across domains. Therefore, in this article, we propose a P reference-aware G raph A ttention network model with C ollaborative K nowledge G raph ( PGACKG ) for cross-domain recommendations. Preference-aware entity embeddings with some collaborative signals are first obtained by exploiting the graph-embedding model, which can transform entities and items in the collaborative knowledge graph into semantic preference spaces. To better learn user preference features, we devise a preference-aware graph attention network framework that aggregates the preference features of similar entities within domains and across domains. In this framework, multi-hop reasoning is employed to assist in the generation of preference features within domains, and the node random walk based on frequency visits is proposed to gather similar preferences across domains for target entities. Then, the final preference features of entities are fused, while a novel C ross-domain B ayesian P ersonalized R anking ( CBPR ) is proposed to improve cross-domain recommendation accuracy. Extensive empirical experiments on four real-world datasets demonstrate that our proposed approach consistently outperforms state-of-the-art baselines. Furthermore, our PGACKG achieves strong performance in different ablation scenarios, and the interaction sparsity experiments also demonstrate that our proposed approach can significantly alleviate the data sparsity issue.