Sparsity Problem Research Articles

Global heterogeneous graph enhanced category-aware attention network for session-based recommendation

Session-based recommendation (SBR) aims at predicting the next item based on an ongoing recorded session of user’s behaviors. Most of existing approaches mainly focus on capturing various item relations such as transitions, co-occurrences, via item identifiers (IDs). In such model, item attributes, such as category, are simply ignored. A few approaches try to explore items’ category information to design SBR model. However, due to the dynamically changing preferences of users, it is challenging to exactly pinpoint which category of items is currently of interest to the user. We argue that since a single session faces a serious problem of category information sparsity, the category context of other similar sessions can help to model user category interests more accurately. To model user intent more accurately, the intra-category information is vital beside the inter-category user interest modeling.In this paper, we propose a Heterogeneous Graph Enhanced Category-aware Attention Network (HGCAN) as a solution model to accomplish both two tasks of estimating user’s category intent and predicting items for recommendation. The architecture of HGCAN consists of a weighted heterogeneous graph attention network (WHGAT) on global graph for item and category embeddings learning and a category-aware attention network for session representations learning. Experiments on three public datasets validate our HGCAN outperforms these state-of-the-art models.11Source code and data are available at https://github.com/resistzzz/HGCAN.

The influence of the duration of the storage period of aeroponic minitubers on the growth and development of potato varieties in field conditions

When using minitubers as planting material in seed production, there is a problem of sparseness of seedlings, which is associated with different duration of the storage period of minitubers (since harvesting from an aeroponic installation to the appearance of the first single shoots). The purpose of the research is to establish the influence of the duration of the storage period of aeroponic minitubers on the growth, development and productivity of various potato varieties under field conditions. The research was carried out in 2020-2022 in the conditions of the Novosibirsk region, the soil is leached medium loamy chernozem. The objects of the research are aeroponic minitubers of early-ripening (Yuna, Terra, Legenda) and mid-ripening (Zlatka, Sokur) potato varieties for December, March and April harvesting periods. It was established that the storage period of minitubers of various varieties was 179-184 days when collected from an aeroponic installation in December, 91.7-118.3 days in March, 71.3-99.7 days in April. The use of minitubers as planting material for growing high-quality potato seeds is justified during the December and March harvesting periods. Potato plants from the December planting material had a germination rate of 98.5 %, germinated 20 days earlier, had a larger average leaf area, the initial yield on the first recording date was 2.6 times greater and the final yield was 1.4 times greater than the plants from the March planting material. Minitubers of the December period provided the highest yield (22.8 t/ha) and the yield of seed tubers (76.3 %), with March harvest time the corresponding values were lower by 6.8 t/ha and 15.9 %, with April harvest period – by 15.9 t/ha and 34,0 %. It is not practical to use minitubers of the April harvest time with a storage period of 91.7-118.3 days as planting material, since it is impossible to obtain yield for the most of the studied varieties. The Sokur (mid-ripening) and Terra (early-ripening) varieties showed the highest yields of 20.4 and 19.5 t/ha.

Contrastive Graph Prompt-tuning for Cross-domain Recommendation

Recommender systems commonly suffer from the long-standing data sparsity problem where insufficient user-item interaction data limits the systems’ ability to make accurate recommendations. This problem can be alleviated using cross-domain recommendation techniques. In particular, in a cross-domain setting, knowledge sharing between domains permits improved effectiveness on the target domain. While recent cross-domain recommendation techniques used a pre-training configuration, we argue that such techniques lead to a low fine-tuning efficiency, especially when using large neural models. In recent language models, prompts have been used for parameter-efficient and time-efficient tuning of the models on the downstream tasks—these prompts represent a tunable latent vector that permits to freeze the rest of the language model’s parameters. To address the cross-domain recommendation task in an efficient manner, we propose a novel Personalised Graph Prompt-based Recommendation (PGPRec) framework, which leverages the efficiency benefits from prompt-tuning. In such a framework, we develop personalised and item-wise graph prompts based on relevant items to those items the user has interacted with. In particular, we apply Contrastive Learning to generate the pre-trained embeddings, to allow an increased generalisability in the pre-training stage, and to ensure an effective prompt-tuning stage. To evaluate the effectiveness of our PGPRec framework in a cross-domain setting, we conduct an extensive evaluation with the top- k recommendation task and perform a cold-start analysis. The obtained empirical results on four Amazon Review datasets show that our proposed PGPRec framework can reduce up to 74% of the tuned parameters with a competitive performance and achieves an 11.41% improved performance compared to the strongest baseline in a cold-start scenario.

Watchog: A Light-weight Contrastive Learning based Framework for Column Annotation

Relational Web tables provide valuable resources for numerous downstream applications, making table understanding, especially column annotation that identifies semantic types and relations of columns, a hot topic in the field of data management. Despite recent efforts to improve different tasks in table understanding by using the power of large pre-trained language models, existing methods heavily rely on large-scale and high-quality labeled instances, while they still suffer from the data sparsity problem due to the imbalanced data distribution among different classes. In this paper, we propose the Watchog framework, which employs contrastive learning techniques to learn robust representations for tables by leveraging a large-scale unlabeled table corpus with minimal overhead. Our approach enables the learned table representations to enhance fine tuning with much fewer additional labeled instances than in prior studies for downstream column annotation tasks. Besides, we further proposed optimization techniques for semi-supervised settings. Experimental results on popular benchmarking datasets illustrate the superiority of our proposed techniques in two column annotation tasks under different settings. In particular, our Watchog framework effectively alleviates the class imbalance issue caused by a long-tailed label distribution. In the semi-supervised setting, Watchog outperforms the best-known method by up to 26% and 41% in Micro and Macro F1 scores, respectively, on the task of semantic type detection.

Imputation Missing Value to Overcome Sparsity Problems in The Recommendation System

A recommendation system is a system that provides suggestions or recommendations to a product or service for its users. One of the problems encountered in the recommendation system is sparsity, namely the lack of available data for analysis, resulting in poor performance of the recommendation system because it cannot provide the proper recommendations. On this basis, this study proposes the mean method and the stochastic hot-deck method to calculate missing values to improve the quality of the recommendations. The experimental results show that the hot-deck imputation method gives better results than the mean imputation method with smaller RMSE and MAE values, namely 2,706 and 2,691.

What influences users to provide explicit feedback? A case of food delivery recommenders

Although various forms of explicit feedback such as ratings and reviews are important for recommenders, they are notoriously difficult to collect. However, beyond attributing these difficulties to user effort, we know surprisingly little about user motivations. Here, we provide a behavioral account of explicit feedback’s sparsity problem by modeling a range of constructs on the rating and review intentions of US food delivery platform users, using data collected from a structured survey (n = 796). Our model, combining the Technology Acceptance Model and Theory of Planned Behavior, revealed that standard industry practices for feedback collection appear misaligned with key psychological influences of behavioral intentions. Most notably, rating and review intentions were most influenced by subjective norms. This means that while most systems directly request feedback in user-to-provider relationships, eliciting them through social ties that manifest in user-to-user relationships is likely more effective. Secondly, our hypothesized dimensions of feedback’s perceived usefulness recorded insubstantial effect sizes on feedback intentions. These findings offered clues for practitioners to improve the connection between providing behaviors and recommendation benefits through contextualized messaging. In addition, perceived pressure and users’ high stated ability to provide feedback recorded insignificant effects, suggesting that frequent feedback requests may be ineffective. Lastly, privacy concerns recorded insignificant effects, hinting that the personalization-privacy paradox might not apply to preference information such as ratings and reviews. Our results provide a novel understanding of explicit feedback intentions to improve feedback collection in food delivery and beyond.

Heterogeneous information fusion based graph collaborative filtering recommendation

Nowadays, with the application of 5G, graph-based recommendation algorithms have become a research hotspot. Graph neural networks encode the graph structure information in the node representation through an iterative neighbor aggregation method, which can effectively alleviate the problem of data sparsity. In addition, more and more information graph can be used in collaborative filtering recommendation, such as user social information graph, user or item attributed information graph, etc. In this paper, we propose a novel heterogeneous information fusion based graph collaborative filtering method, which models graph data from different heterogeneous graph, and combines them together to enhance presentation learning. Through information propagation and aggregation, our model can learn the latent embeddings effectively and enhance the performance of recommendation. Experimental results on different datasets validate the outperformance of the proposed framework.

A many objective commercial recommendation algorithm via Game-Based core node extraction

The development of the recommendation system (RS) has been focused on improving the accuracy of algorithms. As the number of new users grows, RS suffers from the cold start problem, data sparsity and long-tail problem. In this paper, we proposed a many objective commercial recommendation algorithm that extracts the core node through the pay-off function of game theory and makes it guide the evolutionary computation. Firstly, the core aim of commercial recommendations is to maximize total profit. Profits can vary wildly with the same accuracy. Thus, the profit value as one of the goals is considered in attention to accuracy, coverage, and novelty. Besides, the current user state value is updated according to user features and rating scores matrix with the attention mechanism at different time stamps. The nodes are divided into different communities, forming a dynamic community, and recommendations are made within each community. Meanwhile, to reduce the computational complexity and runtime, the pay-off function of game theory is used to extract the core user in each community. Each contribution value and user interaction as criteria to guide the current community of users. Experiments on real datasets show that selecting core nodes can improve the performance of business RS. Compared with other existing algorithms in many experiments, our algorithm performs better.

Enhanced Temporal Knowledge Graph Completion via Learning High-Order Connectivity and Attribute Information

Temporal knowledge graph completion (TKGC) refers to the prediction and filling in of missing facts on time series, which is essential for many downstream applications. However, many existing TKGC methods suffer from two limitations: (1) they only consider direct relations between entities and fail to express high-order structural dependencies between entities; and (2) they only leverage relation quadruples of temporal knowledge graphs, ignoring attribute information that contains rich semantic information. This makes them vulnerable to sparsity and incompleteness problems. In response, we propose HCAE, a temporal knowledge graph completion model that includes high-order connectivity and attribute information. This consists mainly of a recursive embedding propagation layer and a multi-head attention aggregation layer. The former leverages a recursive mechanism to update entity embeddings and can learn high-order connectivity information between entities in linear complexity time. The latter leverages an attention mechanism to understand the importance of different attributes for entity representation automatically. Combining high-order connectivity and attribute information can lead to more diverse entity representations and help enhance the model’s ability to infer unknown entities. Comparative experiments on three real-world datasets show that the model’s inference accuracy significantly outperforms other benchmark methods, especially regarding knowledge graphs with many unknown entities or relations.

Learning Hierarchical Spatial Tasks with Visiting Relations for Next POI Recommendation

Sparsity is an established problem for the next Point-of-Interest (POI) recommendation task, where it hinders effective learning of user preferences from the User-POI matrix. However, learning multiple hierarchically related spatial tasks, and visiting relations between users and POIs, can help to alleviate this sparsity problem. In this article, we propose our Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) approach, which alleviates the sparsity problem by learning different User-Region matrices of lower sparsities in a multi-task setting. We then perform a Hierarchical Beam Search (HBS) on the different region and POI distributions to hierarchically reduce the search space with increasing spatial granularity and predict the next POI. Our HBS provides efficiency gains by reducing the search space, resulting in speedups of 5 to 7 times over an exhaustive approach. In addition, we propose a selectivity layer to predict if the next POI has been visited before by the user to balance between personalization and exploration. Further, we propose a novel Joint Triplet Loss Learning (JTLL) module to learn visited and unvisited relations between users and POIs for the recommendation task. Experimental results on two real-world Location-Based Social Network (LBSN) datasets show that our proposed approach significantly outperforms baseline and the state-of-the-art methods.

Asymmetrical Attention Networks Fused Autoencoder for Debiased Recommendation

Popularity bias is a massive challenge for autoencoder-based models, which decreases the level of personalization and hurts the fairness of recommendations. User reviews reflect their preferences and help mitigate bias or unfairness in the recommendation. However, most existing works typically incorporate user (item) reviews into a long document and then use the same module to process the document in parallel. Actually, the set of user reviews is completely different from the set of item reviews. User reviews are heterogeneous in that they reflect a variety of items purchased by users, while item reviews are only related to the item itself and are thus typically homogeneous. In this article, a novel asymmetric attention network fused with autoencoders is proposed, which jointly learns representations from the user and item reviews and implicit feedback to perform recommendations. Specifically, we design an asymmetric attentive module to capture rich representations from user and item reviews, respectively, which solves data sparsity and explainable problems. Furthermore, to further address popularity bias, we apply a noise-contrastive estimation objective to learn high-quality “de-popularity” embedding via the decoder structure. A series of extensive experiments are conducted on four benchmark datasets to show that leveraging user review information can eliminate popularity bias and improve performance compared to various state-of-the-art recommendation techniques.

CLHHN: Category-aware Lossless Heterogeneous Hypergraph Neural Network for Session-based Recommendation

In recent years, session-based recommendation (SBR), which seeks to predict the target user’s next click based on anonymous interaction sequences, has drawn increasing interest for its practicality. The key to completing the SBR task is modeling user intent accurately. Due to the popularity of graph neural networks (GNNs), most state-of-the-art (SOTA) SBR approaches attempt to model user intent from the transitions among items in a session with GNNs. Despite their accomplishments, there are still two limitations. First, most existing SBR approaches utilize limited information from short user–item interaction sequences and suffer from the data sparsity problem of session data. Second, most GNN-based SBR approaches describe pairwise relations between items while neglecting complex and high-order data relations. Although some recent studies based on hypergraph neural networks have been proposed to model complex and high-order relations, they usually output unsatisfactory results due to insufficient relation modeling and information loss. To this end, we propose a category-aware lossless heterogeneous hypergraph neural network (CLHHN) in this article to recommend possible items to the target users by leveraging the category of items. More specifically, we convert each category-aware session sequence with repeated user clicks into a lossless heterogeneous hypergraph consisting of item and category nodes as well as three types of hyperedges, each of which can capture specific relations to reflect various user intents. Then, we design an attention-based lossless hypergraph convolutional network to generate sessionwise and multi-granularity intent-aware item representations. Experiments on three real-world datasets indicate that CLHHN can outperform the SOTA models in making a better tradeoff between prediction performance and training efficiency. An ablation study also demonstrates the necessity of CLHHN’s key components.

Multi-aspect Graph Contrastive Learning for Review-enhanced Recommendation

Review-based recommender systems explore semantic aspects of users’ preferences by incorporating user-generated reviews into rating-based models. Recent works have demonstrated the potential of review information to improve the recommendation capacity. However, most existing studies rely on optimizing review-based representation learning part, thus failing to explicitly capture the fine-grained semantic aspects, and also ignoring the intrinsic correlation between ratings and reviews. To address these problems, we propose a multi-aspect graph contrastive learning framework, named MAGCL, with three distinctive designs: (i) a multi-aspect representation learning module, which projects semantic relations to different subspaces by decoupling review information, and then obtains high-order decoupled representations in each aspect via graph encoder. (ii) the contrastive learning module performs graph contrastive learning to capture the correlation between rating and review patterns, which utilize unlabeled data to generate self-supervised signals and, in turn, relieve the data sparsity problem of supervision signals. (iii) the multi-task learning module conducts joint training to learn high-order structure-aware yet self-discriminative node representations by combining recommendation task and self-supervised task, which helps alleviate the over-smoothing problem. Extensive experiments are conducted on four real-world review datasets and the results show the superiority of the proposed framework MAGCL compared with several state of the arts. We also provide further analysis on multi-aspect representations and graph contrastive learning to verify the advantage of proposed framework.

A Survey on Recommendation Methods Based on Social Relationships

With the rapid development of online social networks recently, more and more online users have participated in social network activities and rich social relationships are formed accordingly. These social relationships provide a rich data source and research basis for in-depth study on recommender systems (RSs), while also promoting the development of RSs based on social networks. To solve the problems of cold start and sparsity in RSs, many recommendation algorithms are constantly being proposed. Motivated by the availability of rich social connections in today’s RSs, a large number of recommendation techniques based on social relationships have been proposed recently, achieving good recommendation results, and have become the mainstream research direction in the field of RSs, attracting more and more researchers to engage in this research. In this study, we mainly review and summarize the social relationship-based recommendation methods and techniques in RSs, and study some recent deep social relationship recommendation methods and techniques based on deep learning (DL), including the latest social matrix factorization (MF)-based recommendation methods and graph neural network (GNN)-based recommendation methods. Finally, we discuss the potential impact that may improve the RS and future direction. In this article, we aim to introduce the recent recommendation techniques integrating social relationships to solve data sparsity and cold start, and provide a new perspective for improving the performance of RSs, thereby providing useful resources in the state-of-the-art research results for future researchers.

A deep learning knowledge graph neural network for recommender systems

Knowledge graphs are becoming the new state-of-the-art for recommender systems. This paper is based on knowledge graphs to alleviate the problem of data sparsity. Various methods have been recently deployed to solve this problem which largely attempts to study user-item representation and then recommend items to users based on these representations. Although these methods are effective, they lack explainability for recommendations and do not mine side information. In this paper, we propose the use of knowledge graphs which includes additional information about users and items in addition to the use of a user/item interaction matrix. The vital element of our model is neighbourhood aggregation for collaborative filtering. Every user and item are associated with an ID embedding, which is circulated on the interaction graph for users, items, and their attributes. We obtain the final embeddings by combining the embeddings learned at various hidden layers with a biased sum. Our model is easier to train and achieves better performance compared to graph neural network-based collaborative filtering (GCF) and other state-of-the-art recommender methods. We provide evidence for our argument by analytically comparing the knowledge graph convolution network (KGCN) with GCF and eight other state-of-the-art methods, using similar experimental settings and the same datasets.

Multi-granularity cross-modal representation learning for named entity recognition on social media

With social media posts tending to be multimodal, Multimodal Named Entity Recognition (MNER) for the text with its accompanying image is attracting more and more attention since it plays an important role for various applications such as intention understanding and user recommendation. However, there are two drawbacks in existing approaches: (1) Meanings of the text and its accompanying image do not match always, so the text information still plays a major role. However, social media posts are usually shorter and more informal compared with other normal contents, which easily causes incomplete semantic description and the data sparsity problem. (2) Although the visual representations are already used, existing methods ignore either fine-grained semantic correspondence between objects in images and words in text or the objective fact that there are misleading objects or no objects in some images . In this work, we solve the above two problems by introducing the multi-granularity cross-modal representation learning . To resolve the first problem, we enhance the representation by semantic augmentation for each word in text. As for the second issue, we perform the cross-modal semantic interaction between text and vision at the different vision granularity to get the most effective multimodal guidance representation for every word . The experiments show that our results on TWITTER-2015 (74.57%) and TWITTER-2017 (86.09%) outperform the current performances. The code, data and the best performing models are available at : https://github.com/LiuPeiP-CS/IIE4MNER.

Electricity Theft Detection Based on Contrastive Learning and Non-Intrusive Load Monitoring

Electricity theft has caused enormous damage to grids safety and economy globally, bringing plentiful attention to electricity theft detection. However, the inherent problems of data imbalance, data sparsity, and data shift due to the residents will or the seasons remain challenges. Besides, there is a lack of appliance-level information provided by non-intrusive load monitoring to improve the data graininess in electricity theft detection. To address these problems, an electricity theft detection algorithm based on contrastive learning and non-intrusive load monitoring is proposed. Firstly, a semi-supervised learning architecture composed of Gramian angular field encoding for sequence visualization and contrastive learning architecture featuring few-shot learning is established for load monitoring and initial detection considering the inherent operating characteristics of appliances. Furthermore, after the filtration of typical regular-switched appliances by Kendalls coefficient of concordance, in-depth detection of abnormal operation routines of appliances is conducted for suspicious residents, aiming to improve the fault-tolerant ability and resist the emergence of unknown electricity theft methods. Finally, the electricity theft probability is computed to confirm fraudulent users. Both the public and practical datasets are utilized to verify the effectiveness of the proposed study, and the results show an overall better performance compared to other state-of-the-art algorithms.

Attributed network embedding based on self-attention mechanism for recommendation method

Network embedding is a technique used to learn a low-dimensional vector representation for each node in a network. This method has been proven effective in network mining tasks, especially in the area of recommendation systems. The real-world scenarios often contain rich attribute information that can be leveraged to enhance the performance of representation learning methods. Therefore, this article proposes an attribute network embedding recommendation method based on self-attention mechanism (AESR) that caters to the recommendation needs of users with little or no explicit feedback data. The proposed AESR method first models the attribute combination representation of items and then uses a self-attention mechanism to compactly embed the combination representation. By representing users as different anchor vectors, the method can efficiently learn their preferences and reconstruct them with few learning samples. This achieves accurate and fast recommendations and avoids data sparsity problems. Experimental results show that AESR can provide personalized recommendations even for users with little explicit feedback information. Moreover, the attribute extraction of documents can effectively improve recommendation accuracy on different datasets. Overall, the proposed AESR method provides a promising approach to recommendation systems that can leverage attribute information for better performance.

Heterogeneous Multidomain Recommender System Through Adversarial Learning.

To solve the user data sparsity problem, which is the main issue in generating user preference prediction, cross-domain recommender systems transfer knowledge from one source domain with dense data to assist recommendation tasks in the target domain with sparse data. However, data are usually sparsely scattered in multiple possible source domains, and in each domain (source/target) the data may be heterogeneous, thus it is difficult for existing cross-domain recommender systems to find one source domain with dense data from multiple domains. In this way, they fail to deal with data sparsity problems in the target domain and cannot provide an accurate recommendation. In this article, we propose a novel multidomain recommender system (called HMRec) to deal with two challenging issues: 1) how to exploit valuable information from multiple source domains when no single source domain is sufficient and 2) how to ensure positive transfer from heterogeneous data in source domains with different feature spaces. In HMRec, domain-shared and domain-specific features are extracted to enable the knowledge transfer between multiple heterogeneous source and target domains. To ensure positive transfer, the domain-shared subspaces from multiple domains are maximally matched by a multiclass domain discriminator in an adversarial learning process. The recommendation in the target domain is completed by a matrix factorization module with aligned latent features from both the user and the item side. Extensive experiments on four cross-domain recommendation tasks with real-world datasets demonstrate that HMRec can effectively transfer knowledge from multiple heterogeneous domains collaboratively to increase the rating prediction accuracy in the target domain and significantly outperforms six state-of-the-art non-transfer or cross-domain baselines.

Deep adaptive collaborative graph neural network for social recommendation

Most graph convolutional network (GCN)-based social recommendation frameworks fuse social links with user-item interactions to enrich user representations, which alleviate the cold-start problem and data sparsity problem. However, GCN-based recommender systems still suffer from two limitations. First, Excessive reliance on social graphs to extract user interests for rating predictions is unreliable due to social inconsistency. Second, GCN-based models suffer from over-smoothing problems, node embeddings become more similar when going deeper to enable larger receptive fields. To address the two aforementioned problems simultaneously, we propose a Deep Adaptive Collaborative Graph Neural Network for Social Recommendation (DUI-SoRec). First, the graph generation module decomposes the user-item interaction to generate two subgraphs: an u2u graph and an i2i graph. Secondly, the graph learning module utilizes a deep adaptive graph neural network to learn user and item embeddings on the two subgraphs and the existing social graph, while solving the over-smoothing problem. Finally, we designed a refined fusion module to aggregate the social graph and u2u graph to address the social inconsistency. We conducted extensive experiments on four real-world datasets and the results demonstrate the model’s effectiveness.