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        <title>Frontiers in Big Data | Recommender Systems section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/big-data/sections/recommender-systems</link>
        <description>RSS Feed for Recommender Systems section in the Frontiers in Big Data journal | New and Recent Articles</description>
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        <pubDate>2026-04-11T23:33:56.251+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1632766</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1632766</link>
        <title><![CDATA[Multistakeholder fairness in tourism: what can algorithms learn from tourism management?]]></title>
        <pubdate>2025-09-18T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Peter Müllner</author><author>Anna Schreuer</author><author>Simone Kopeinik</author><author>Bernhard Wieser</author><author>Dominik Kowald</author>
        <description><![CDATA[Algorithmic decision-support systems, i.e., recommender systems, are popular digital tools that help tourists decide which places and attractions to explore. However, algorithms often unintentionally direct tourist streams in a way that negatively affects the environment, local communities, or other stakeholders. This issue can be partly attributed to the computer science community's limited understanding of the complex relationships and trade-offs among stakeholders in the real world. In this work, we draw on the practical findings and methods from tourism management to inform research on multistakeholder fairness in algorithmic decision-support. Leveraging a semi-systematic literature review, we synthesize literature from tourism management as well as literature from computer science. Our findings suggest that tourism management actively tries to identify the specific needs of stakeholders and utilizes qualitative, inclusive and participatory methods to study fairness from a normative and holistic research perspective. In contrast, computer science lacks sufficient understanding of the stakeholder needs and primarily considers fairness through descriptive factors, such as measureable discrimination, while heavily relying on few mathematically formalized fairness criteria that fail to capture the multidimensional nature of fairness in tourism. With the results of this work, we aim to illustrate the shortcomings of purely algorithmic research and stress the potential and particular need for future interdisciplinary collaboration. We believe such a collaboration is a fundamental and necessary step to enhance algorithmic decision-support systems toward understanding and supporting true multistakeholder fairness in tourism.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1564521</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1564521</link>
        <title><![CDATA[Toward more realistic career path prediction: evaluation and methods]]></title>
        <pubdate>2025-08-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Elena Senger</author><author>Yuri Campbell</author><author>Rob van der Goot</author><author>Barbara Plank</author>
        <description><![CDATA[Predicting career trajectories is a complex yet impactful task, offering significant benefits for personalized career counseling, recruitment optimization, and workforce planning. However, effective career path prediction (CPP) modeling faces challenges including highly variable career trajectories, free-text resume data, and limited publicly available benchmark datasets. In this study, we present a comprehensive comparative evaluation of CPP models—linear projection, multilayer perceptron (MLP), LSTM, and large language models (LLMs)—across multiple input settings and two recently introduced public datasets. Our contributions are threefold: (1) we propose novel model variants, including an MLP extension and a standardized LLM approach, (2) we systematically evaluate model performance across input types (titles only vs. title+description, standardized vs. free-text), and (3) we investigate the role of synthetic data and fine-tuning strategies in addressing data scarcity and improving model generalization. Additionally, we provide a detailed qualitative analysis of prediction behaviors across industries, career lengths, and transitions. Our findings establish new baselines, reveal the trade-offs of different modeling strategies, and offer practical insights for deploying CPP systems in real-world settings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1611389</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1611389</link>
        <title><![CDATA[LLM-as-a-Judge: automated evaluation of search query parsing using large language models]]></title>
        <pubdate>2025-07-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mehmet Selman Baysan</author><author>Serkan Uysal</author><author>İrem İşlek</author><author>Çağla Çığ Karaman</author><author>Tunga Güngör</author>
        <description><![CDATA[IntroductionThe adoption of Large Language Models (LLMs) in search systems necessitates new evaluation methodologies beyond traditional rule-based or manual approaches.MethodsWe propose a general framework for evaluating structured outputs using LLMs, focusing on search query parsing within an online classified platform. Our approach leverages LLMs' contextual reasoning capabilities through three evaluation methodologies: Pointwise, Pairwise, and Pass/Fail assessments. Additionally, we introduce a Contextual Evaluation Prompt Routing strategy to improve reliability and reduce hallucinations.ResultsExperiments conducted on both small- and large-scale datasets demonstrate that LLM-based evaluation achieves approximately 90% agreement with human judgments.DiscussionThese results validate LLM-driven evaluation as a scalable, interpretable, and effective alternative to traditional evaluation methods, providing robust query parsing for real-world search systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2025.1573072</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2025.1573072</link>
        <title><![CDATA[Editorial: Natural language processing for recommender systems]]></title>
        <pubdate>2025-03-25T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Alfred Krzywicki</author><author>Michael Bain</author><author>Wayne Wobcke</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1505284</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1505284</link>
        <title><![CDATA[On explaining recommendations with Large Language Models: a review]]></title>
        <pubdate>2025-01-27T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Alan Said</author>
        <description><![CDATA[The rise of Large Language Models (LLMs), such as LLaMA and ChatGPT, has opened new opportunities for enhancing recommender systems through improved explainability. This paper provides a systematic literature review focused on leveraging LLMs to generate explanations for recommendations—a critical aspect for fostering transparency and user trust. We conducted a comprehensive search within the ACM Guide to Computing Literature, covering publications from the launch of ChatGPT (November 2022) to the present (November 2024). Our search yielded 232 articles, but after applying inclusion criteria, only six were identified as directly addressing the use of LLMs in explaining recommendations. This scarcity highlights that, despite the rise of LLMs, their application in explainable recommender systems is still in an early stage. We analyze these select studies to understand current methodologies, identify challenges, and suggest directions for future research. Our findings underscore the potential of LLMs improving explanations of recommender systems and encourage the development of more transparent and user-centric recommendation explanation solutions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1374980</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1374980</link>
        <title><![CDATA[The development and application of a novel E-commerce recommendation system used in electric power B2B sector]]></title>
        <pubdate>2024-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wenjun Meng</author><author>Lili Chen</author><author>Zhaomin Dong</author>
        <description><![CDATA[The advent of the digital era has transformed E-commerce platforms into critical tools for industry, yet traditional recommendation systems often fall short in the specialized context of the electric power industry. These systems typically struggle with the industry's unique challenges, such as infrequent and high-stakes transactions, prolonged decision-making processes, and sparse data. This research has developed a novel recommendation engine tailored to these specific conditions, such as to handle the low frequency and long cycle nature of Business-to-Business (B2B) transactions. This approach includes algorithmic enhancements to better process and interpret the limited data available, and data pre-processing techniques designed to enrich the sparse datasets characteristic of this industry. This research also introduces a methodological innovation that integrates multi-dimensional data, combining user E-commerce activities, product specifics, and essential non-tendering information. The proposed engine employs advanced machine learning techniques to provide more accurate and relevant recommendations. The results demonstrate a marked improvement over traditional models, offering a more robust and effective tool for facilitating B2B transactions in the electric power industry. This research not only addresses the sector's unique challenges but also provides a blueprint for adapting recommendation systems to other industries with similar B2B characteristics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1399739</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1399739</link>
        <title><![CDATA[A time-robust group recommender for featured comments on news platforms]]></title>
        <pubdate>2024-05-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Cedric Waterschoot</author><author>Antal van den Bosch</author>
        <description><![CDATA[IntroductionRecently, content moderators on news platforms face the challenging task to select high-quality comments to feature on the webpage, a manual and time-consuming task exacerbated by platform growth. This paper introduces a group recommender system based on classifiers to aid moderators in this selection process.MethodsUtilizing data from a Dutch news platform, we demonstrate that integrating comment data with user history and contextual relevance yields high ranking scores. To evaluate our models, we created realistic evaluation scenarios based on unseen online discussions from both 2020 and 2023, replicating changing news cycles and platform growth.ResultsWe demonstrate that our best-performing models maintain their ranking performance even when article topics change, achieving an optimum mean NDCG@5 of 0.89.DiscussionThe expert evaluation by platform-employed moderators underscores the subjectivity inherent in moderation practices, emphasizing the value of recommending comments over classification. Our research contributes to the advancement of (semi-)automated content moderation and the understanding of deliberation quality assessment in online discourse.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1295009</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1295009</link>
        <title><![CDATA[Multi-modal recommender system for predicting project manager performance within a competency-based framework]]></title>
        <pubdate>2024-05-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Imene Jemal</author><author>Wilfried Armand Naoussi Sijou</author><author>Belkacem Chikhaoui</author>
        <description><![CDATA[The evaluation of performance using competencies within a structured framework holds significant importance across various professional domains, particularly in roles like project manager. Typically, this assessment process, overseen by senior evaluators, involves scoring competencies based on data gathered from interviews, completed forms, and evaluation programs. However, this task is tedious and time-consuming, and requires the expertise of qualified professionals. Moreover, it is compounded by the inconsistent scoring biases introduced by different evaluators. In this paper, we propose a novel approach to automatically predict competency scores, thereby facilitating the assessment of project managers' performance. Initially, we performed data fusion to compile a comprehensive dataset from various sources and modalities, including demographic data, profile-related data, and historical competency assessments. Subsequently, NLP techniques were used to pre-process text data. Finally, recommender systems were explored to predict competency scores. We compared four different recommender system approaches: content-based filtering, demographic filtering, collaborative filtering, and hybrid filtering. Using assessment data collected from 38 project managers, encompassing scores across 67 different competencies, we evaluated the performance of each approach. Notably, the content-based approach yielded promising results, achieving a precision rate of 81.03%. Furthermore, we addressed the challenge of cold-starting, which in our context involves predicting scores for either a new project manager lacking competency data or a newly introduced competency without historical records. Our analysis revealed that demographic filtering achieved an average precision of 54.05% when dealing with new project managers. In contrast, content-based filtering exhibited remarkable performance, achieving a precision of 85.79% in predicting scores for new competencies. These findings underscore the potential of recommender systems in competency assessment, thereby facilitating more effective performance evaluation process.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1384460</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1384460</link>
        <title><![CDATA[Editorial: Reviews in recommender systems: 2022]]></title>
        <pubdate>2024-04-02T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Dominik Kowald</author><author>Deqing Yang</author><author>Emanuel Lacic</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2024.1304439</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2024.1304439</link>
        <title><![CDATA[Knowledge-based recommender systems: overview and research directions]]></title>
        <pubdate>2024-02-26T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Mathias Uta</author><author>Alexander Felfernig</author><author>Viet-Man Le</author><author>Thi Ngoc Trang Tran</author><author>Damian Garber</author><author>Sebastian Lubos</author><author>Tamim Burgstaller</author>
        <description><![CDATA[Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1251072</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1251072</link>
        <title><![CDATA[Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks]]></title>
        <pubdate>2023-12-19T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Tomislav Duricic</author><author>Dominik Kowald</author><author>Emanuel Lacic</author><author>Elisabeth Lex</author>
        <description><![CDATA[By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), have demonstrated great results in terms of recommendation accuracy. However, accuracy may not always be the most important criterion for evaluating recommender systems' performance, since beyond-accuracy aspects such as recommendation diversity, serendipity, and fairness can strongly influence user engagement and satisfaction. This review paper focuses on addressing these dimensions in GNN-based recommender systems, going beyond the conventional accuracy-centric perspective. We begin by reviewing recent developments in approaches that improve not only the accuracy-diversity trade-off but also promote serendipity, and fairness in GNN-based recommender systems. We discuss different stages of model development including data preprocessing, graph construction, embedding initialization, propagation layers, embedding fusion, score computation, and training methodologies. Furthermore, we present a look into the practical difficulties encountered in assuring diversity, serendipity, and fairness, while retaining high accuracy. Finally, we discuss potential future research directions for developing more robust GNN-based recommender systems that go beyond the unidimensional perspective of focusing solely on accuracy. This review aims to provide researchers and practitioners with an in-depth understanding of the multifaceted issues that arise when designing GNN-based recommender systems, setting our work apart by offering a comprehensive exploration of beyond-accuracy dimensions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1284511</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1284511</link>
        <title><![CDATA[Recommender systems for sustainability: overview and research issues]]></title>
        <pubdate>2023-10-30T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Alexander Felfernig</author><author>Manfred Wundara</author><author>Thi Ngoc Trang Tran</author><author>Seda Polat-Erdeniz</author><author>Sebastian Lubos</author><author>Merfat El Mansi</author><author>Damian Garber</author><author>Viet-Man Le</author>
        <description><![CDATA[Sustainability development goals (SDGs) are regarded as a universal call to action with the overall objectives of planet protection, ending of poverty, and ensuring peace and prosperity for all people. In order to achieve these objectives, different AI technologies play a major role. Specifically, recommender systems can provide support for organizations and individuals to achieve the defined goals. Recommender systems integrate AI technologies such as machine learning, explainable AI (XAI), case-based reasoning, and constraint solving in order to find and explain user-relevant alternatives from a potentially large set of options. In this article, we summarize the state of the art in applying recommender systems to support the achievement of sustainability development goals. In this context, we discuss open issues for future research.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1281614</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1281614</link>
        <title><![CDATA[An overview of video recommender systems: state-of-the-art and research issues]]></title>
        <pubdate>2023-10-30T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Sebastian Lubos</author><author>Alexander Felfernig</author><author>Markus Tautschnig</author>
        <description><![CDATA[Video platforms have become indispensable components within a diverse range of applications, serving various purposes in entertainment, e-learning, corporate training, online documentation, and news provision. As the volume and complexity of video content continue to grow, the need for personalized access features becomes an inevitable requirement to ensure efficient content consumption. To address this need, recommender systems have emerged as helpful tools providing personalized video access. By leveraging past user-specific video consumption data and the preferences of similar users, these systems excel in recommending videos that are highly relevant to individual users. This article presents a comprehensive overview of the current state of video recommender systems (VRS), exploring the algorithms used, their applications, and related aspects. In addition to an in-depth analysis of existing approaches, this review also addresses unresolved research challenges within this domain. These unexplored areas offer exciting opportunities for advancements and innovations, aiming to enhance the accuracy and effectiveness of personalized video recommendations. Overall, this article serves as a valuable resource for researchers, practitioners, and stakeholders in the video domain. It offers insights into cutting-edge algorithms, successful applications, and areas that merit further exploration to advance the field of video recommendation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1249997</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1249997</link>
        <title><![CDATA[Differential privacy in collaborative filtering recommender systems: a review]]></title>
        <pubdate>2023-10-12T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Peter Müllner</author><author>Elisabeth Lex</author><author>Markus Schedl</author><author>Dominik Kowald</author>
        <description><![CDATA[State-of-the-art recommender systems produce high-quality recommendations to support users in finding relevant content. However, through the utilization of users' data for generating recommendations, recommender systems threaten users' privacy. To alleviate this threat, often, differential privacy is used to protect users' data via adding random noise. This, however, leads to a substantial drop in recommendation quality. Therefore, several approaches aim to improve this trade-off between accuracy and user privacy. In this work, we first overview threats to user privacy in recommender systems, followed by a brief introduction to the differential privacy framework that can protect users' privacy. Subsequently, we review recommendation approaches that apply differential privacy, and we highlight research that improves the trade-off between recommendation quality and user privacy. Finally, we discuss open issues, e.g., considering the relation between privacy and fairness, and the users' different needs for privacy. With this review, we hope to provide other researchers an overview of the ways in which differential privacy has been applied to state-of-the-art collaborative filtering recommender systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1245198</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1245198</link>
        <title><![CDATA[Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives]]></title>
        <pubdate>2023-10-06T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Deepak Kumar</author><author>Tessa Grosz</author><author>Navid Rekabsaz</author><author>Elisabeth Greif</author><author>Markus Schedl</author>
        <description><![CDATA[Recommender systems (RSs) have become an integral part of the hiring process, be it via job advertisement ranking systems (job recommenders) for the potential employee or candidate ranking systems (candidate recommenders) for the employer. As seen in other domains, RSs are prone to harmful biases, unfair algorithmic behavior, and even discrimination in a legal sense. Some cases, such as salary equity in regards to gender (gender pay gap), stereotypical job perceptions along gendered lines, or biases toward other subgroups sharing specific characteristics in candidate recommenders, can have profound ethical and legal implications. In this survey, we discuss the current state of fairness research considering the fairness definitions (e.g., demographic parity and equal opportunity) used in recruitment-related RSs (RRSs). We investigate from a technical perspective the approaches to improve fairness, like synthetic data generation, adversarial training, protected subgroup distributional constraints, and post-hoc re-ranking. Thereafter, from a legal perspective, we contrast the fairness definitions and the effects of the aforementioned approaches with existing EU and US law requirements for employment and occupation, and second, we ascertain whether and to what extent EU and US law permits such approaches to improve fairness. We finally discuss the advances that RSs have made in terms of fairness in the recruitment domain, compare them with those made in other domains, and outline existing open challenges.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1239705</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1239705</link>
        <title><![CDATA[Multi-list interfaces for recommender systems: survey and future directions]]></title>
        <pubdate>2023-08-10T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Benedikt Loepp</author>
        <description><![CDATA[For a long time, recommender systems presented their results in the form of simple item lists. In recent years, however, multi-list interfaces have become the de-facto standard in industry, presenting users with numerous collections of recommendations, one below the other, each containing items with common characteristics. Netflix's interface, for instance, shows movies from certain genres, new releases, and lists of curated content. Spotify recommends new songs and albums, podcasts on specific topics, and what similar users are listening to. Despite their popularity, research on these so-called “carousels” is still limited. Few authors have investigated how to simulate the user behavior and how to optimize the recommendation process accordingly. The number of studies involving users is even smaller, with sometimes conflicting results. Consequently, little is known about how to design carousel-based interfaces for achieving the best user experience. This mini review aims to organize the existing knowledge and outlines directions that may improve the multi-list presentation of recommendations in the future.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1168692</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.1168692</link>
        <title><![CDATA[A review on individual and multistakeholder fairness in tourism recommender systems]]></title>
        <pubdate>2023-05-10T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Ashmi Banerjee</author><author>Paromita Banik</author><author>Wolfgang Wörndl</author>
        <description><![CDATA[The growing use of Recommender Systems (RS) across various industries, including e-commerce, social media, news, travel, and tourism, has prompted researchers to examine these systems for any biases or fairness concerns. Fairness in RS is a multi-faceted concept ensuring fair outcomes for all stakeholders involved in the recommendation process, and its definition can vary based on the context and domain. This paper highlights the importance of evaluating RS from multiple stakeholders' perspectives, specifically focusing on Tourism Recommender Systems (TRS). Stakeholders in TRS are categorized based on their main fairness criteria, and the paper reviews state-of-the-art research on TRS fairness from various viewpoints. It also outlines the challenges, potential solutions, and research gaps in developing fair TRS. The paper concludes that designing fair TRS is a multi-dimensional process that requires consideration not only of the other stakeholders but also of the environmental impact and effects of overtourism and undertourism.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.974072</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2023.974072</link>
        <title><![CDATA[Personalized diversification of complementary recommendations with user preference in online grocery]]></title>
        <pubdate>2023-03-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Luyi Ma</author><author>Nimesh Sinha</author><author>Jason H. D. Cho</author><author>Sushant Kumar</author><author>Kannan Achan</author>
        <description><![CDATA[Complementary recommendations play an important role in surfacing the relevant items to the customers. In the cross-selling scenario, some customers might present more exploratory shopping behaviors and prefer more diverse complements, while other customers show less exploratory (or more conventional) shopping behaviors and want to have a deep dive of less diverse types of complements. The existence of two distinct shopping behaviors reflects users' different shopping intents and requires complementary recommendations to be adaptable based on the user's shopping intent. Although many studies focus on improving the recommendations through post-processing techniques, such as user-item-level personalized ranking and diversification of recommendations, they fail to address such a requirement. First, many user-item-level personalization methods cannot explicitly model the preference of users in two types of shopping behaviors and their intent on the corresponding complementary recommendations. Second, most of the diversification methods increase the heterogeneity of the recommendations. However, users' intent on conventional complementary shopping requires more homogeneity of the recommendations, which is not explicitly modeled. The present study tries attempts to solve these problems by the personalized diversification strategies for complementary recommendations. To address the requirement of modeling heterogenized and homogenized complementary recommendations, we propose two diversification strategies, heterogenization and homogenization, to re-rank complementary recommendations based on the determinantal point process (DPP). We use transaction history to estimate users' intent on more exploratory or more conventional complementary shopping. With the estimated user intent scores and two diversification strategies, we propose an algorithm to personalize the diversification strategies dynamically. We demonstrate the effectiveness of our re-ranking algorithm on the publicly available Instacart dataset.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2023.1157899</guid>
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        <title><![CDATA[A survey on multi-objective recommender systems]]></title>
        <pubdate>2023-03-22T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Dietmar Jannach</author><author>Himan Abdollahpouri</author>
        <description><![CDATA[Recommender systems can be characterized as software solutions that provide users with convenient access to relevant content. Traditionally, recommender systems research predominantly focuses on developing machine learning algorithms that aim to predict which content is relevant for individual users. In real-world applications, however, optimizing the accuracy of such relevance predictions as a single objective in many cases is not sufficient. Instead, multiple and often competing objectives, e.g., long-term vs. short-term goals, have to be considered, leading to a need for more research in multi-objective recommender systems. We can differentiate between several types of such competing goals, including (i) competing recommendation quality objectives at the individual and aggregate level, (ii) competing objectives of different involved stakeholders, (iii) long-term vs. short-term objectives, (iv) objectives at the user interface level, and (v) engineering related objectives. In this paper, we review these types of multi-objective recommendation settings and outline open challenges in this area.1]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2022.966982</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2022.966982</link>
        <title><![CDATA[Multi-objective cluster based bidding algorithm for E-commerce search engine marketing system]]></title>
        <pubdate>2022-09-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Cheng Jie</author><author>Zigeng Wang</author><author>Da Xu</author><author>Wei Shen</author>
        <description><![CDATA[Search engine marketing (SEM) is an important channel for the success of e-commerce. With the increasing scale of catalog items, designing an efficient modern industrial-level bidding system usually requires overcoming the following hurdles: 1. the relevant bidding features are of high sparsity, preventing an accurate prediction of the performances of many ads. 2. the large volume of bidding requests induces a significant computation burden to offline and online serving. In this article, we introduce an end-to-end structure of a multi-objective bidding system for search engine marketing for Walmart e-commerce, which successfully handles tens of millions of bids each day. The system deals with multiple business demands by constructing an optimization model targeting a mixture of metrics. Moreover, the system extracts the vector representations of ads via the Transformer model. It leverages their geometric relation to building collaborative bidding predictions via clustering to address performance features' sparsity issues. We provide theoretical and numerical analyzes to discuss how we find the proposed system as a production-efficient solution.]]></description>
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