Recommendation Diversity Research Articles

Uncertainty-aware graph neural network for semi-supervised diversified recommendation

Uncertainty-aware graph neural network for semi-supervised diversified recommendation

Leveraging language representation for materials exploration and discovery

Data-driven approaches to materials exploration and discovery are building momentum due to emerging advances in machine learning. However, parsimonious representations of crystals for navigating the vast materials search space remain limited. To address this limitation, we introduce a materials discovery framework that utilizes natural language embeddings from language models as representations of compositional and structural features. The contextual knowledge encoded in these language representations conveys information about material properties and structures, enabling both similarity analysis to recall relevant candidates based on a query material and multi-task learning to share information across related properties. Applying this framework to thermoelectrics, we demonstrate diversified recommendations of prototype crystal structures and identify under-studied material spaces. Validation through first-principles calculations and experiments confirms the potential of the recommended materials as high-performance thermoelectrics. Language-based frameworks offer versatile and adaptable embedding structures for effective materials exploration and discovery, applicable across diverse material systems.

A European Society of Oncologic Imaging (ESOI) survey on the radiological assessment of response to oncologic treatments in clinical practice

ObjectivesTo present the results of a survey on the assessment of treatment response with imaging in oncologic patient, in routine clinical practice. The survey was promoted by the European Society of Oncologic Imaging to gather information for the development of reporting models and recommendations.MethodsThe survey was launched on the European Society of Oncologic Imaging website and was available for 3 weeks. It consisted of 5 sections, including 24 questions related to the following topics: demographic and professional information, methods for lesion measurement, how to deal with diminutive lesions, how to report baseline and follow-up examinations, which previous studies should be used for comparison, and role of RECIST 1.1 criteria in the daily clinical practice.ResultsA total of 286 responses were received. Most responders followed the RECIST 1.1 recommendations for the measurement of target lesions and lymph nodes and for the assessment of tumor response. To assess response, 48.6% used previous and/or best response study in addition to baseline, 25.2% included the evaluation of all main time points, and 35% used as the reference only the previous study. A considerable number of responders used RECIST 1.1 criteria in daily clinical practice (41.6%) or thought that they should be always applied (60.8%).ConclusionSince standardized criteria are mainly a prerogative of clinical trials, in daily routine, reporting strategies are left to radiologists and oncologists, which may issue local and diversified recommendations. The survey emphasizes the need for more generally applicable rules for response assessment in clinical practice.Critical relevance statementCompared to clinical trials which use specific criteria to evaluate response to oncological treatments, the free narrative report usually adopted in daily clinical practice may lack clarity and useful information, and therefore, more structured approaches are needed.Key points· Most radiologists consider standardized reporting strategies essential for an objective assessment of tumor response in clinical practice.· Radiologists increasingly rely on RECIST 1.1 in their daily clinical practice.· Treatment response evaluation should require a complete analysis of all imaging time points and not only of the last.Graphical

A novel self-supervised graph model based on counterfactual learning for diversified recommendation

Consumers’ needs present a trend of diversification, which causes the emergence of diversified recommendation systems. However, existing diversified recommendation research mostly focuses on objective function construction rather than on the root cause that limits diversity—namely, imbalanced data distribution. This study considers how to balance data distribution to improve recommendation diversity. We propose a novel self-supervised graph model based on counterfactual learning (SSG-CL) for diversified recommendation. SSG-CL first distinguishes the dominant and disadvantageous categories for each user based on long-tail theory. It then introduces counterfactual learning to construct an auxiliary view with relatively balanced distribution among the dominant and disadvantageous categories. Next, we conduct contrastive learning between the user–item interaction graph and the auxiliary view as the self-supervised auxiliary task that aims to improve recommendation diversity. Finally, SSG-CL leverages a multitask training strategy to jointly optimize the main accuracy-oriented recommendation task and the self-supervised auxiliary task. Finally, we conduct experimental studies on real-world datasets, and the results indicate good SSG-CL performance in terms of accuracy and diversity.

Combining Graph Contrastive Embedding and Multi-head Cross-Attention Transfer for Cross-Domain Recommendation

Cross-domain recommendation (CDR) has become an important research direction in the field of recommender systems due to the increasing demand for personalized recommendations across different domains. However, CDR faces multiple challenges, including data sparsity, popularity bias, and long-tail problems. To address these challenges, we propose a novel framework that combines graph contrastive embedding and multi-head cross-attention transfer for cross-domain recommendation, called GCE-MCAT. Specifically, in the pre-training process, we generate more uniform user and item embeddings through contrastive learning, effectively solving the problem of inconsistent data embedding space distribution and recommendation popularity bias. Moreover, we propose a multi-head cross-attention transfer mechanism that allows the model to extract user common and specific domain features from multiple perspectives and perform cross-domain bidirectional knowledge transfer. Finally, we propose a cross-domain feature fusion mechanism that dynamically assigns weights to common user features and specific domain features. This enables the model to more effectively learn common user interests. We evaluate the proposed framework on three real-world CDR datasets and show that GCE-MCAT consistently and significantly improves recommendation performance compared to state-of-the-art methods. In particular, the proposed framework has demonstrated remarkable effectiveness in addressing long-tail distribution and enhancing recommendation novelty, providing users with more diversified recommendations and reducing popularity bias.

Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study

We present the results of a 12-week longitudinal user study wherein the participants, 110 subjects from Southern Europe, received on a daily basis Electronic Music (EM) diversified recommendations. By analyzing their explicit and implicit feedback, we show that exposure to specific levels of music recommendation diversity may be responsible for long-term impacts on listeners’ attitudes. In particular, we highlight the function of diversity in increasing the openness in listening to EM, a music genre not particularly known or liked by the participants previous to their participation in the study. Moreover, we demonstrate that recommendations may help listeners in removing positive and negative attachments towards EM, deconstructing pre-existing implicit associations but also stereotypes associated with this music. In addition, our results show the significant influence that recommendation diversity has in generating curiosity in listeners.

How and when ranking-based recommendations induce higher willingness-to-pay

ABSTRACT Web-based businesses create various product recommendation labels to facilitate online decision-making. Across three studies, this research demonstrates that relative to non-ranking-based recommendation labels, ranking-based recommendation labels increase e-shoppers’ quality assessment belief and elicit a higher willingness to pay. Consistency among consensus attributes and perceived product differences moderate this effect. According to the findings, online retailers should use a multi-layer, diversified recommendation scheme and ranking-based recommendations for high profit-margin products to maximize their future revenues.

Individual Diversity Preference Aware Neural Collaborative Filtering

The diversified recommendation of recommender systems enriches user experiences by diversifying recommendation lists. However, the conventional post-processing strategy, which re-ranking the recommendation lists by diversity measurement, could cause a trade-off between accuracy and diversity. An improvement of one performance usually sacrifices the other one. Thus, we propose an in-processing method named Individual Diversity Preference Aware Neural Collaborative Filtering that takes accuracy and diversity together as optimal direction during the recommendation process. It consists of two filtering processes: collaborative filtering and diversity filtering. The items with high accuracy and high diversity to users could be on the recommendation lists. Besides, to meet different diverse needs of users, we model the diversity preference of an individual user by the items he/she has interacted with. As the front preparation of our method, we pre-train the item embeddings by applying a graph embedding technique on the item-linking knowledge graph. Since the pre-training is independent of recommendation, the pre-trained item embeddings could be used for objective diversity comparison among different recommendation models. To validate our proposed method, we conduct extensive experiments on Movielens datasets. Experimental results suggest that our approach has achieved high accuracy and high diversity of recommendations.

Improving Accuracy and Diversity in Matching of Recommendation With Diversified Preference Network

Real-world recommendation systems need to deal with millions of item candidates. Therefore, most practical large-scale recommendation systems usually contain two modules. The matching module aims to efficiently retrieve hundreds of high-quality items from large corpora, while the ranking module aims to generate specific ranks for these items. Recommendation diversity is an essential factor that strongly impacts user experience. There are lots of efforts that have explored recommendation diversity in ranking, while the matching module should take more responsibility for diversity. In this article, we propose a novel Heterogeneous graph neural network framework for diversified recommendation (GraphDR) in matching to improve both recommendation accuracy and diversity. Specifically, GraphDR builds a huge heterogeneous preference network to record different types of user preferences, and conducts a field-level heterogeneous graph attention network for node aggregation. We conduct a neighbor-similarity based loss with a multi-channel matching to improve both accuracy and diversity. In experiments, we conduct extensive online and offline evaluations on a real-world recommendation system with various accuracy and diversity metrics and achieve significant improvements. GraphDR has been deployed on a well-known recommendation system named WeChat Top Stories, which affects millions of users. The source code will be released in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lqfarmer/GraphDR</uri> .

Application Analysis of Multi-Intelligence Optimization Decision-Making Method in College Students’ Ideological and Political Education System

Today’s society is a society with diversified information. The rapid change of information also affects the ideological and political education in universities. Therefore, the fixed thinking of ideological and political education in universities tells students, which is an educational way in line with the development of the times. We should follow up with the rapidly changing times at any time, change with the times, and update the thinking and systematic way of the ideological and political education system at any time. In the ideological and political education of college students, diversified teaching methods and multiobjective recommendation systems are implemented and then combined with traditional ideological and political teaching methods. The two ways complement each other and promote each other so as to achieve higher learning efficiency and better learning effect. The optimization algorithm of multiobjective recommendation should be used to further improve the ideological and political education system. By analyzing the optimization results and performance comparison of various algorithms, we find the most suitable algorithm model for optimizing the ideological and political education system. The multiobjective-recommended ideological and political education system for college students needs to fully improve the teaching tasks of teachers and students in two stages. A reasonable and scientific system recommendation mechanism should take into account students’ own learning preferences, subject types, ideological and political teacher information, curriculum information, curriculum evaluation, curriculum relevance, and other multiobjective data. This paper achieves the highest performance and the lowest time cost of the ideological and political education system through multiobjective evolutionary optimization method.

Facial Nerve Anomalies as an Obscure Co-occurrence With External Ear Malformations: A Case Report

This study reports an infant with a rare triad of congenital facial palsy, bilateral aural atresia, and middle ear malformations. We highlight the audiological test battery in detail that led to identifying this obscure co-occurrence in a 6-month-old infant. The challenges associated with identifying such rare conditions, especially in infants, can be overcome only by incorporating a meticulous and vigilant approach. The infant was subjected to a series of subjective and objective audiological evaluations, through which rare asymmetric facial grimaces were vigilantly observed. This observation warranted radiologic investigation, which confirmed the suspicion that the anomaly may not be restricted to auditory structures alone. As facial nerve anomalies were confirmed, diversified recommendations, including speech, language, and swallow evaluation, were made apart from the auditory management. Hence in cases of external ear anomalies, although rare, it is essential to rule out facial nerve abnormalities as they can be a concealed problem.

Deep Reinforcement Learning for Stock Recommendation

Recommending stocks is very important for investment companies and investors. However, without enough analysts, no stock selection strategy can capture the dynamics of all S&P 500 stocks. Nevertheless, most existing recommending strategies are based on predictive models to buy and hold stocks with high return potential. But these strategies fail to recommend stocks from different industrial sectors to reduce risks. In this article, we propose a novel solution that recommends a stock portfolio with reinforcement learning from the S&P 500 index. Our basic idea is to construct a stock relation graph (RG) which provide rich relations among stocks and industrial sectors, to generate diversified recommendation result. To this end, we design a new method to explore high-quality stocks from the constructed relation graph with reinforcement learning. Specifically, the reinforcement learning agent jumps from each industrial sector to select stock based on the feedback signals from the market. Finally, we apply portfolio allocation methods (i.e., mean-variance and minimum-variance) to test the validity of the recommendation. The empirical results show that the performance of portfolio allocation based on the selected stocks is better than the long-term strategy on the S&P 500 Index in terms of cumulative returns.

Multi-interest Diversification for End-to-end Sequential Recommendation

Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often neglect the fact that users display multiple interests over extended periods of time, which could be used to improve the diversity of lists of recommended items. Existing work related to diversified recommendation typically assumes that users’ preferences are static and depend on post-processing the candidate list of recommended items. However, those conditions are not suitable when applied to sequential recommendations. We tackle sequential recommendation as a list generation process and propose a unified approach to take accuracy as well as diversity into consideration, called multi-interest, diversified, sequential recommendation . Particularly, an implicit interest mining module is first used to mine users’ multiple interests, which are reflected in users’ sequential behavior. Then an interest-aware, diversity promoting decoder is designed to produce recommendations that cover those interests. For training, we introduce an interest-aware, diversity promoting loss function that can supervise the model to learn to recommend accurate as well as diversified items. We conduct comprehensive experiments on four public datasets and the results show that our proposal outperforms state-of-the-art methods regarding diversity while producing comparable or better accuracy for sequential recommendation.

Service Recommendation with High Accuracy and Diversity

In recent years, the number of web services grows explosively. With a large amount of information resources, it is difficult for users to quickly find the services they need. Thus, the design of an effective web service recommendation method has become the key factor to satisfy the requirements of users. However, traditional recommendation methods often tend to pay more attention to the accuracy of the results but ignore the diversity, which may lead to redundancy and overfitting, thus reducing the satisfaction of users. Considering these drawbacks, a novel method called DivMTID is proposed to improve the effectiveness by achieving accurate and diversified recommendations. First, we utilize users’ historical scores of web services to explore the users’ preferences. And we use the TF-IDF algorithm to calculate the weight vector of each web service. Second, we utilize cosine similarity to calculate the similarity between candidate web services and historical web services and we also forecast the ranking scores of candidate web services. At last, a diversification method is used to generate the top- K recommended list for users. And through a case study, we show that DivMTID is an effective, accurate, and diversified web service recommendation method.

Diversified Personalized Recommendation Optimization Based on Mobile Data

With the advent of the Internet of Things, especially the Internet of Vehicles, abundant environmental and mobile data can be generated continuously. A personalized recommender system is one of the important methods for solving the problem of big data overload. However, to make use of these mobile data from vehicles, traditional recommender services are confronted by severe challenges. Therefore, we study the diversified recommendation problem based on a real-world dataset, represented as a tensor with three dimensions of user, location and activity. As the tensor is rather sparse, we employ tensor decomposition to predict missing values. Additionally, we directly regard recommendation precision as an objective. In addition to precision, we also consider the recommendation novelty and coverage, providing a more comprehensive view of the recommender system. Thus, visitors can discover attractive spots that are less visited in a personalized manner, relieving traffic pressure at famous scenic spots and balancing overall transportation. By integrating all these objectives, we construct a many-objective recommendation model. To optimize this model, we propose a distributed parallel evolutionary algorithm employing the nondominated ranking and crowding distance. Compared with the state-of-the-art algorithms, the proposed algorithm performs well and is very efficient.

Diversified and Scalable Service Recommendation With Accuracy Guarantee

As one of the most successful recommendation techniques, neighborhood-based collaborative filtering (CF), which recommends appropriate items to a target user by identifying similar users or similar items, has been widely applied to various recommender systems. Although many neighbor-based CF methods have been put forward, there are still some open issues that have remained unsolved. First, the ever-increasing volume of user–item rating data decreases the recommendation efficiency significantly as a recommender system needs to analyze all the rating data when searching for similar neighbors or similar items. In this situation, users’ requirements on quick response may not be met. Second, in neighbor-based CF methods, more attention is paid to the recommendation accuracy while other key indicators of recommendation performances are often ignored, i.e., recommendation diversity (RD), which probably produces similar or redundant items in the recommended list and decreases users’ satisfaction. Considering these issues, a diversified and scalable recommendation method (called DR_LT) based on locality-sensitive hashing and cover tree is proposed in this article, where the item topic information is used to optimize the final recommended list. We show the effectiveness of our proposed method through a set of experiments on MovieLens data set that clearly shows the feasibility of our proposal in terms of item recommendation accuracy, diversity, and scalability.

A multiple criteria approach integrating social ties to support purchase decision

To leverage information sharing in online social networks, online retailers (e-tailers) have launched social networking functions on their platforms. The objective of e-tailers’ social networks is to empower consumers to connect and share product information. However, the e-tailers have not used these social networks to provide product recommendations to customers. Our goal is to aid e-retailers in personalizing recommendations for customers using their friends’ (referrers’) preferences. Our approach involves: (a) mapping the referrers’ online behaviors into a set of pairwise comparisons, and use additive value functions to model their preferences, (b) defining the degree of contextual trust of a customer towards a referrer to differentiate the roles of referrers, (c) proposing a clustering algorithm to capture referrers’ heterogeneous preferences, and (d) aggregating preference information for referrers within the same subgroup to obtain diversified recommendations. On a broader note, this study illustrates how online information (i.e., preference expressions of referrers, social ties between a consumer and referrers) from an e-tailer’s social network can be mined and incorporated into a decision-aiding approach to generate tailor-made recommendations for customers. Finally, we illustrate the proposed approach to with a numerical case study.

A Hybrid E-Learning Recommendation Approach Based on Learners’ Influence Propagation

In e-learning recommender systems, interpersonal information between learners is very scarce, which makes it difficult to apply collaborative filtering (CF) techniques to achieve recommendations. In this study, we propose a hybrid filtering recommendation approach (SI - IFL) combining learner influence model (LIM), self-organization based (SOB) recommendation strategy, and sequential pattern mining (SPM) together for recommending learning objects (LOs) to learners. The method works as follows: (i) LIM is applied to acquire the interpersonal information by computing the influence that a learner exerts on others. LIM consists of learner similarity, knowledge credibility, and learner aggregation, meanwhile, LIM is independent of ratings. Furthermore, to address the uncertainty and fuzzy natures of learners, intuitionistic fuzzy logic (IFL) is applied to optimize the LIM. (ii) A SOB recommendation strategy is applied to recommend the optimal learner cliques for active learners by simulating the influence propagation among learners. Influence propagation means that a learner can move towards active learners, and such behaviors can stimulate the moving behaviors of his/her neighbors. This SOB recommendation approach achieves a stable structure based on distributed and bottom-up behaviors of individuals. (iii) SPM is applied to decide the final learning objects (LOs) and navigational paths based on the recommended learner cliques. The experimental results demonstrate that SI - IFL can provide personalized and diversified recommendations, and it shows promising efficiency and adaptability in e-learning scenarios.

Diversified Interactive Recommendation with Implicit Feedback

Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attention. Previous methods mainly focus on optimizing recommendation accuracy. However, they usually ignore the diversity of the recommendation results, thus usually results in unsatisfying user experiences. In this paper, we propose a novel diversified recommendation model, named Diversified Contextual Combinatorial Bandit (DC2B), for interactive recommendation with users' implicit feedback. Specifically, DC2B employs determinantal point process in the recommendation procedure to promote diversity of the recommendation results. To learn the model parameters, a Thompson sampling-type algorithm based on variational Bayesian inference is proposed. In addition, theoretical regret analysis is also provided to guarantee the performance of DC2B. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed method in balancing the recommendation accuracy and diversity.

Session-aware news recommendations using random walks on time-evolving heterogeneous information networks

Traditional news media Web sites usually provide generic recommendations that are not personalized to the preferences of their users. Typically, news recommendation algorithms mainly rely on the long-term preferences of users and do not adjust their model to the continuous stream of short-lived incoming stories to capture short-term intentions revealed by users’ sessions. In this paper, we therefore study the problem of session-aware recommendations by running random walks on dynamic heterogeneous graphs. Concretely, we construct a heterogeneous information network consisting of users, news articles, news categories, locations and sessions. By using different (1) sliding time window sizes, (2) sub-graphs for model learning, (3) sequential article weighting strategies and (4) more diversified random walks, we perform recommendations in a second step. Our algorithm proposal is evaluated on three real-life data sets, and we demonstrate that our method outperforms state-of-the-art methods by delivering more accurate and diversified recommendations.