Predict User Preferences Research Articles

Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network

Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user’s higher-order social friends and community neighbors on the user is obtained according to the user’s higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI’s higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model.

A heterogeneous graph neural network model for list recommendation

List recommendation, an approach for recommending a set of items closely related to user preferences, plays a significant role in improving the user experience and has received growing attention in recent years. However, most existing models ignore list-level attributes and the heterogeneity of the interactions between the users and lists when predicting user preferences. In this paper, we propose a heterogeneous graph neural network list recommendation (HGLR) model to bridge these gaps. First, a representation learning module based on pre-training techniques is developed to learn the embeddings of the lists, by encoding textual information and aggregating the features of the constituent items. We then transform the user–list interaction into a bipartite graph and incorporate the list attributes into a heterogeneous information network (HIN). Propagation-based embedding learning is subsequently performed to generate user and list representation based on the message-passing mechanism of the graph neural network. Finally, an attentive fusion layer is designed to obtain the global representations for the recommendation. We also develop a new large-scale dataset containing multiple types of side information to evaluate the list recommendation model. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art list recommendation methods and achieves performance gains over 8.96% and 14.59% in Recall and NDCG, respectively, against the strongest baselines.

MOVIE RECOMMENDER SYSTEM USING MACHINE LEARNING

The recommendation system of today has transformed the way we search for items of our interest through the use of an information filtering approach that predicts user preferences. A movie recommendation system named RECOM has been proposed in this paper. Based on the content-based filtering approach, it utilizes the information in the dataset, undergoes analysis, and recommends the most suitable movies for the user. The recommended movie list is ordered according to the IMDB ratings calculated by a standard formula. The system also enables users to search for movies based on their favourite actors/actresses. Developed in Python and Machine Learning, the recommender system generates recommendations through the utilization of various forms of knowledge and data about movies, such as vote count, vote average, mean, quantile, etc. Overall, the effectiveness and efficacy of movie searches have significantly increased thanks to the introduction of content-based filtering and machine learning techniques in movie recommendation systems, making it simpler for users to locate films they are likely to enjoy.

Joint optimization of recommendation and caching based on user preference prediction

AbstractThe development of the Internet of things brings exponential growth of wireless traffic, which puts great pressure on the backhaul link. The proactive caching of some contents in the edge device of mobile network can effectively reduce the repeated transmission of the same contents and relieve the burden of the backhaul link. Moreover, the introduction of recommendation mechanisms can reshape user's request and improve cache hit ratio. However, the optimization of recommendation and caching decisions is highly dependent on the users’ preference information for files. Here, a joint optimization algorithm of recommendation and caching based on users’ preference prediction with multiple base stations cooperative caching is proposed. To improve the caching efficiency, the Deep Crossing model is adopted to predict users’ preferences. Under the constraints of cache capacity, recommendation quantity and bandwidth, an optimization problem to minimize the total transmission delay of the system is formulated. Then, the NP‐hardness of the proposed optimization problem is proved and it is decoupled it into three sub‐problems, namely recommendation, user access and caching optimization sub‐problems. Simulation results show that the proposed algorithm can effectively reduce the total transmission delay of the system.

KTPGN: Novel event-based group recommendation method considering implicit social trust and knowledge propagation

Groups where like-minded people gather to share interests, comments, or participate in activities have recently gained popularity in well-known social platforms, such as Meetup and Douban. Group recommendations are essential in social media to help users identify their preferred groups. Our concern is event-based group recommendations, based on which users join a group primarily to participate in offline events hosted by group members. Traditional methods for group recommendation do not work well in addressing this issue, because they ignore the social relationships among users or multiple attributes of groups. The knowledge-enhanced trust propagation graph neural network (KTPGN) presented in this study exploits social trust, multiple attributes of groups, and user-group interaction history in a unified framework to predict user preferences for groups. First, an implicit social trust graph is constructed by considering two aspects of credibility, because explicit trust relationships are not always available in most real recommender systems. Furthermore, a graph neural network (GNN)-based model is proposed to learn group embeddings by iteratively aggregating multiple linked attributes and user embeddings through recursive propagation of social trust and historical interest. Experimental results on several datasets from Meetup demonstrate that KTPGN significantly outperforms state-of-the-art recommendation methods.

Capturing Preferences of New Users in Generative Tasks with Minimal Interactions Collaborative Filtering Using Siamese Networks and Soft Clustering

Prediction of user preferences is a challenge, in particular when the objective is to learn them without requiring the user to provide a profile or a significant number of interactions. Many collaborative filtering algorithms exist but all of them require the availability of huge datasets of user information and expensive computations. In this paper, a novel architecture is introduced which aims to predict a new user’s interests in the context of previous users’ interactions with minimal feedback interactions. Here, a Siamese Network is used to generate an embedding space for data from existing users. This information is then used in a Gaussian Mixture Model to generate multiple soft clusters. Based on the embedding space, system responses to the user are generated using a Conditional Generative Adversarial Network which uses a vector drawn from the Gaussian Mixture in embedding space from the Siamese Network as the conditional input. The predictive model then interacts with the new user and based on their feedback adjusts the Gaussian Mixture to find the distribution with the highest probability of generating the user’s preferred data. The approach is applied in the context of an image generation task where the goal is to learn to generate images that match the preferences of the user using only a minimal number of direct user interactions. Testing in this domain has shown promising results that exemplify the ability of the approach to capture the user’s preferences while presenting only a minimal number of image examples.

SGT: Session-based Recommendation with GRU and Transformer

Session-based recommendation aims to predict user preferences based on anonymous behavior sequences. Recent research on session-based recommendation systems has mainly focused on utilizing attention mechanisms on sequential patterns, which has achieved significant results. However, most existing studies only consider individual items in a session and do not extract information from continuous items, which can easily lead to the loss of information on item transition relationships. Therefore, this paper proposes a session-based recommendation algorithm (SGT) based on Gated Recurrent Unit (GRU) and Transformer, which captures user interests by learning continuous items in the current session and utilizes all item transitions on sessions in a more refined way. By combining short-term sessions and long-term behavior, user dynamic preferences are captured. Extensive experiments were conducted on three session-based recommendation datasets, and compared to the baseline methods, both the recall rate Recall@20 and the mean reciprocal rank MRR@20 of the SGT algorithm were improved, demonstrating the effectiveness of the SGT method.

Symbolic knowledge extraction for explainable nutritional recommenders

Background and objective:This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designing these kinds of systems, there including: (i) adherence to experts’ prescriptions, (ii) adherence to users’ tastes and preferences, (iii) explainability of the whole recommendation process. Accordingly, in this paper we propose a novel approach to the engineering of nutritional RS, combining machine learning and symbolic knowledge extraction to profile users—hence harmonising the aforementioned requirements.MethodsOur contribution focuses on the data processing workflow. Stemming from neural networks (NN) trained to predict user preferences, we use CART Breiman et al.(1984) to extract symbolic rules in Prolog Körner et al.(2022) form, and we combine them with expert prescriptions brought in similar form. We can then query the resulting symbolic knowledge base via logic solvers, to draw explainable recommendations.ResultsExperiments are performed involving a publicly available dataset of 45,723 recipes, plus 12 synthetic datasets about as many imaginary users, and 6 experts’ prescriptions. Fully-connected 4-layered NN are trained on those datasets, reaching ∼86% test-set accuracy, on average. Extracted rules, in turn, have ∼80% fidelity w.r.t. those NN. The resulting recommendation system has a test-set precision of ∼74%. The symbolic approach makes it possible to devise how the system draws recommendations.ConclusionsThanks to our approach, intelligent agents may learn users’ preferences from data, convert them into symbolic form, and extend them with experts’ goal-directed prescriptions. The resulting recommendations are then simultaneously acceptable for the end user and adequate under a nutritional perspective, while the whole process of recommendation generation is made explainable.

News recommendations based on collaborative topic modeling and collaborative filtering with generative adversarial networks

PurposeOnline news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.Design/methodology/approachThe collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.FindingsThis study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.Originality/valueAs the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

Communication Efficient Federated Personalized Recommendation

Recommendation systems that can correctly predict user preferences in the information age have become an important factor for business success. However, recommendation systems require users' personal information, and centralized collection and processing of user data may lead to serious privacy risks. Good progress has been made in recent years using federated learning techniques for privacy-preserving recommendations, but several key challenges remain to be addressed: most federated recommender systems ignore communication process optimization, inequities in aggregation of federated models, and lack of personalization to users. In this paper, we propose a communication efficient and fair personalized federated recommendation approach (CFFR) to address these challenges. CFFR uses adaptive client group selection to personalize models while accelerating the training process. A fair-aware model aggregation algorithm is proposed that adaptively captures the performance and data imbalance among different clients to address the unfairness problem. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed method.

Addressing Confounding Feature Issue for Causal Recommendation

In recommender systems, some features directly affect whether an interaction would happen, making the happened interactions not necessarily indicate user preference. For instance, short videos are objectively easier to finish even though the user may not like the video. We term such feature as confounding feature , and video length is a confounding feature in video recommendation. If we fit a model on such interaction data, just as done by most data-driven recommender systems, the model will be biased to recommend short videos more, and deviate from user actual requirement. This work formulates and addresses the problem from the causal perspective. Assuming there are some factors affecting both the confounding feature and other item features, e.g., the video creator, we find the confounding feature opens a backdoor path behind user-item matching and introduces spurious correlation. To remove the effect of backdoor path, we propose a framework named Deconfounding Causal Recommendation (DCR) , which performs intervened inference with do-calculus . Nevertheless, evaluating do-calculus requires to sum over the prediction on all possible values of confounding feature, significantly increasing the time cost. To address the efficiency challenge, we further propose a mixture-of-experts (MoE) model architecture, modeling each value of confounding feature with a separate expert module. Through this way, we retain the model expressiveness with few additional costs. We demonstrate DCR on the backbone model of neural factorization machine (NFM) , showing that DCR leads to more accurate prediction of user preference with small inference time cost. We release our code at: https://github.com/zyang1580/DCR .

Deconfounded recommendation via causal intervention

Traditional recommenders suffer from hidden confounding factors, leading to the spurious correlations between user/item profiles and user preference prediction, i.e., the confounding bias issue. Most works resort to only one confounding bias, which greatly block their applications on recommendations with mixture confounder, i.e., more than one bias. It is therefore of practical demand to empower the recommender with the capability of debiasing different biases from data. Moreover, the positive effect of bias is neglected in most previous works. We argue that confounding bias is actually beneficial for capturing users’ preferences in some recommendation scenarios. In this paper, we propose a novel deconfounded causal learning method called GCRec (Graph Causal Recommendetion) to debias two confounders: social network confounder and item group confounder. We employ Graph Neural Networks (GNNs) to aggregate user-user connections for social networks and user-item interactions for item groups in order to learn high-order representations that can efficiently debias these two confounders from a causal view. In the inference stage, we use symmetric Kullback–Leibler divergence to measure the user preference drift. If the divergence is large, we perform the causal intervention to alleviate the bias amplification caused by confounders on user preferences. Otherwise, we incorporate the user preferences that can potentially deliver a positive effect on favoring recommendation performance. Extensive experiments are conducted on two benchmark datasets to verify that GCRec outperforms state-of-the-art methods and achieves robust recommendations.

Navigating the AI Frontier: Advancements Redefining the World Wide Web's Future – A Review

The World Wide Web (WWW) stands on the cusp of a transformative phase driven by the integration of Artificial Intelligence (AI) technologies. This report delves into AI's profound impact on the future landscape of the WWW, highlighting both opportunities and challenges. AI is set to reshape user experiences by delivering personalized content, refining search functionalities, and introducing conversational interfaces. Advanced algorithms enable websites to tailor recommendations, predict user preferences, and facilitate seamless interactions through AI-driven Chatbots and virtual assistants. Furthermore, AI's influence extends to content creation and moderation, enhancing the web's ability to generate high-quality content while combating harmful material. To content management, AI-powered cybersecurity measures promise to bolster digital defenses against evolving threats like malware and phishing attacks. AI also enhances accessibility by enabling features such as language translation, screen readers, and voice recognition, ensuring equitable access to web resources. Innovations in augmented reality (AR) and virtual reality (VR) further enrich digital experiences across sectors like e-commerce and education. Amidst these opportunities, significant challenges loom. Ethical concerns surrounding data privacy, algorithmic bias, and transparency require careful consideration to build trust in AI-driven web environments. Regulatory frameworks must evolve to protect user rights while fostering innovation and competition. The AI-powered future of the WWW holds immense promise for transforming digital experiences and addressing societal challenges. By navigating ethical, legal, and technical complexities, stakeholders can harness AI's potential to create a more inclusive, secure, and enriching web ecosystem.

Multi-Feature Behavior Relationship for Multi-Behavior Recommendation

Multi-behavior recommendation aims to model the interaction information of multiple behaviors to enhance the target behavior’s recommendation performance. Despite progress in recent research, it is challenging to represent users’ preferences using the multi-feature behavior information of user interactions. In this paper, we propose a Multi-Feature Behavior Relationship for Multi-Behavior Recommendation (MFBR) framework, which models the multi-behavior recommendation problem from both sequence structure and graph structure perspectives for user preference prediction of target behaviors. Specifically, the MFBR model is designed with a sequence encoder and a graph encoder to construct behavioral representations of different aspects of the user; the correlations between behaviors are modeled by a behavioral relationship encoding layer, and the importance of different behaviors is finally learned in order to construct the final representation of user preferences. Experimental validation conducted on two real-world recommendation datasets shows that our MFBR consistently outperforms state-of-the-art methods.

Modeling and predicting user preferences with multiple item attributes for sequential recommendations

Sequential recommendations have become a focus of attention across the deep learning community owing to their fitness to the actual application scenario. Although recently we have witnessed a surge of work on sequential recommender systems, they are still insufficient in exploring and exploiting item-attribute relations to enhance prediction accuracy. In this work, we propose a novel technological framework, MIA-SR, for sequential recommendation (SR) by modeling and predicting user preferences with multiple item attributes (MIA). When modeling the dynamic behavior of a user, not only the item sequence but also the attribute sequence is used to generate the fused representation of users. Further, we propose using a graph convolution network on the item-attribute bipartite graph to enhance the representations of items and attribute entities. Moreover, MIA-SR is naturally empowered with a multi-tasking strategy to exploit inductive bias among different preference signals and enhance item recommendation. Extensive experiments on public benchmark datasets have verified the merits of MIA-SR. The source code and data are available at: https://github.com/619496775/MIA-SR.

Region-aware neural graph collaborative filtering for personalized recommendation

ABSTRACT Personalized recommender systems have been widely deployed in various scenarios to enhance user experience in response to the challenge of information explosion. Especially, personalized recommendation models based on graph structure have advanced greatly in predicting user preferences. However, geographical region entities that reflect the geographical context of the items is not being utilized in previous works, leaving room for the improvement of personalized recommendation. This study proposes a region-aware neural graph collaborative filtering (RA-NGCF) model, which introduces the geographical regions for improving the prediction of user preference. The approach first characterizes the relationships between items and users with a user-item-region graph. And, a neural network model for the region-aware graph is derived to capture the higher-order interaction among users, items, and regions. Finally, the model fuses region and item vectors to infer user preferences. Experiments on real-world dataset results show that introducing region entities improves the accuracy of personalized recommendations. This study provides a new approach for optimizing personalized recommendation as well as a methodological reference for facilitating geographical regions for optimizing spatial applications.

One Hidden Semantic Model Based on Intergroup Effects for E-Commerce.

E-commerce systems often collect data that clearly express user preferences without considering the remaining negative cases, which gives rise to the hidden semantic problem. In this paper, we improve the original hidden semantic model and propose an intergroup effect model that incorporates users' historical browsing behavior, user type, and browsing content; by adopting the weighting and add weighting factors, we can predict users' preferences for different products more accurately and match the candidate products with users' current behaviors, so as to give more reasonable and effective product recommendation results; by adding the group effect model of user group and product group, we can achieve more accurate prediction of user preferences and make the recommendation more reasonable and effective. The research shows that the hidden semantic method based on intergroup effects information is better than other basic methods at a certain identified evaluation stage. In practice, users' purchasing preferences change with time, and using a hidden semantic method based on intergroup effects recommendation can effectively improve the recommendation quality of e-commerce recommendation systems.

Unifying attentive sparse autoencoder with neural collaborative filtering for recommendation

The autoencoder network has been proven to be one of the powerful techniques for recommender systems. Currently, the ways of utilizing autoencoder in recommender systems can be divided into two categories: modeling user-item interaction rely solely on autoencoder and integrating autoencoder with other models. Most existing methods based on autoencoder assume that all features of model’s input are equally the same contributing to the final prediction, which can be regarded as attention weight vectors; however, this hypothesis is not reliable, especially when exploring users’ interaction frequency with different items. Moreover, combining autoencoder with traditional methods, the usual strategy is to leverage a linear kernel of the inner product of user and item vectors to predict user preferences, which will lead to insufficient expression power and hurt the performance of recommendation when facing data sparsity and cold start problems. To tackle the above two problems, we propose a novel hybrid deep learning model for top-n recommendation, called attentive stacked sparse autoencoder (A-SAERec), which can capture attention weights vector of a user for items, and then combined with the neural matrix factorization to improve the performance of recommender model. Extensive experiments on four real-world datasets show that our A-SAERec algorithm has significant improvements over state-of-the-art algorithms.

Identification of Travel Styles by Learning from Consumer-generated Images in Online Travel Communities

“A picture is worth a thousand words”: Never has this adage been more meaningful than it is today. Online social media is driving the growth of unstructured image data. Unstructured data must be structured to be informative and thereby contribute to user understanding and revenue generation. Hitherto, companies have only been able to accomplish this through tedious manual work. This paper demonstrates how image data can be analyzed automatically using a combination of image analysis methods and fuzzy cluster algorithms to predict user preferences, which companies can then use to make targeted offers. Several methods, including support vector machines (SVMs) and convolutional neural networks (CNNs), are benchmarked across various cases of image data taken from an online travel community. Depending on the images’ diversity either a SVM or a CNN provides the best basis for preference prediction.

Sustainable Recognition Methods of Modeling Design Features of Light and Micro Vehicle-Mounted UAV: Based on Support Vector Regression and Kano Model

In the era of Industry 4.0, intelligent algorithms provide an effective way to make design methods more sustainable through mining people’s demands continuously, especially in the field of evaluating and predicting the user preferences of phasic or interim design schemes. Vehicle-mounted unmanned aerial vehicles (UAVs) are of significance in mobility experience and traffic surveys. However, as a new type of product, UAVs lack general rules in modeling design and the process of development decision making presents some fuzzy characteristics, which make the evolution and iteration of modeling design more complex. Based on the theories of Kansei Engineering, this study utilized support vector regression (SVR) to establish a correlation model between design factors and preference degree. Because the perceptual evaluation knowledge is fuzzy and uncertain, the paper applied cross-validation and grid search methods to find the optimal parameters. The parameters of the SVR model were adjusted to meet the need for stable learning and for endurance of the noise from subjective experience data to improve the prediction effect and generalization ability. In addition, by means of the Kano model, the customers’ cognition of demand types was quantified to obtain the prioritization of UAV modeling design elements, as well as to compare with the preference scores to validate the feasibility of this research. It was found that the SVR model proposed in the study could effectively predict user preference (R2 = 0.763, RMSE = 0.057). For the UAVs with a higher preference score, the modeling characteristics were consistent with the attractive, one-dimensional or must-be quality elements in the results of the Kano model, which verified the reliability of the study. The conclusion is expected to provide a sustainable design method for vehicle-mounted UAVs commonly used in citizen travel and outdoor activities.