The proliferation of recommender systems (RS) research has attracted researchers to explore new tools and techniques to address several issues in the domain. In this context, text mining has received considerable acceptance for solving cold start and data sparsity problems in recommender systems. Additionally, text mining has been exploited to design modern recommender systems through sentiment analysis of user reviews, social network posts, web data, etc. These sentiment identifications help create user profiles, identify preferences, understand the context in which a review is provided, and extract features to assess item quality, facilitating better recommendations. This paper presents a comprehensive and systematic review of text mining-based recommender systems (TMRS). We analyze how text mining techniques have been integrated into different RS paradigms and propose a novel taxonomy that classifies TMRS based on underlying text mining approaches and recommendation approaches. In addition, we examine commonly used evaluation metrics and discuss how they are applied to assess TMRS performance. Key research challenges and open issues are identified, along with promising future research directions. This review provides a structured overview of the state of the art in TMRS and serves as a useful reference for researchers and practitioners seeking to design, evaluate, and advance next-generation recommender systems.
PurposeThis study aims to investigate how business-to-business (B2B) sales professionals use social media in their roles, focusing on its impact on personal branding, customer relationship management and sales performance. It fills gaps in social selling literature by exploring new variables and relationships.Design/methodology/approachA quantitative method was used, with 276 valid responses from salespeople across sectors, collected through snowball and purposive sampling. The analysis examined how job-related social media usage (SMU) and personal branding SMU affect in-role performance, customer relationship performance and overall sales outcomes.FindingsFindings reveal that job-related SMU significantly boosts in-role performance, with online social capital enhancing this effect. While job-related SMU does not directly influence sales performance, it indirectly affects it via improved in-role performance. Personal branding SMU positively impacts customer relationship performance, further amplified by online social capital. Both in-role and customer relationship performances strongly predict sales success.Practical implicationsThis study uniquely integrates personal branding and job-related SMU in the B2B context, offering new insights into how social media influences sales. It also provides practical recommendations for sales organizations to enhance social media skills, emphasizing relationship-building and online social capital to drive better sales performance.Originality/valueTo the best of the authors' knowledge, this study is among the first to integrate SMU for personal branding with job-related tasks in the B2B sales context. By highlighting the mediating role of in-role performance and the amplifying effect of online social capital, the research introduces new perspectives on the relationship between social media and sales performance.
We investigate the cosmological evolution of the Universe within the framework of f(R,L-m) gravity by adopting a hybrid expansion law capable of describing both decelerated and accelerated phases of cosmic expansion. The analysis is performed in a spatially flat FLRW spacetime, and the resulting dynamics are examined through key cosmological parameters, including the deceleration parameter and the equation-of-state parameter. Observational constraints from Hubble parameter measurements, Type Ia supernova data from DESY5, and their combination with baryon acoustic oscillation observations are used to estimate the model parameters via Bayesian analysis. The results indicate a smooth transition from deceleration to late-time acceleration consistent with current observations. The present Universe evolves along a quintessence-like trajectory, satisfying the null and dominant energy conditions while violating the strong energy condition at late times. Overall, the proposed f(R,L-m) gravity model provides a viable alternative to the standard Lambda CDM scenario for explaining late-time cosmic acceleration.
Magnetic compounds with noncentrosymmetric chiral crystal structures and spin-frustrated lattices often exhibit complex magnetic ordering and coupled responses. In this report, we present a comprehensive study of the chiral and triangular lattice magnetic system Ni2ScSbO6, which exhibits an incommensurate noncollinear helical antiferromagnetic long-range ordering at a temperature of TN = 62 K, as revealed by bulk magnetization, specific heat, and neutron diffraction studies. The onset of this magnetic ordering is closely linked to a series of strongly coupled phenomena occurring at TN. A clear dielectric anomaly in the form of a sharp )-like peak is observed at TN, triggered by an isostructural distortion, which is mediated by the magnetostriction effect in this system, as evidenced by our synchrotron x-ray diffraction studies. Moreover, a clear anomalous phonon softening for various Raman modes is observed at TN, which can be attributed to substantial spin-phonon coupling combined with the influence of magnetostriction effects. All these strongly correlated phenomena, occurring concurrently with the emergence of the helical antiferromagnetic order, demonstrate an entangled behavior of various microscopic degrees of freedom in this system, thus highlighting Ni2ScSbO6 as a unique material.
