A probabilistic model to resolve diversity-accuracy challenge of recommendation systems

Recommendation systems have wide-spread applications in both academia and industry. Traditionally, performance of recommendation systems has been measured by their precision. By introducing novelty and diversity as key qualities in recommender systems, recently increasing attention has been focused on this topic. Precision and novelty of recommendation are not in the same direction, and practical systems should make a trade-off between these two quantities. Thus, it is an important feature of a recommender system to make it possible to adjust diversity and accuracy of the recommendations by tuning the model. In this paper, we introduce a probabilistic structure to resolve the diversity-accuracy dilemma in recommender systems. We propose a hybrid model with adjustable level of diversity and precision such that one can perform this by tuning a single parameter. The proposed recommendation model consists of two models: one for maximization of the accuracy and the other one for specification of the recommendation list to tastes of users. Our experiments on two real datasets show the functionality of the model in resolving accuracy-diversity dilemma and outperformance of the model over other classic models. The proposed method could be extensively applied to real commercial systems due to its low computational complexity and significant performance.Comment: 19 pages, 5 figure

Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities

Digital journalism has faced a dramatic change and media companies are challenged to use data science algo-rithms to be more competitive in a Big Data era. While this is a relatively new area of study in the media landscape, the use of machine learning and artificial intelligence has increased substantially over the last few years. In particular, the adoption of data science models for personalization and recommendation has attracted the attention of several media publishers. Following this trend, this paper presents a research literature analysis on the role of Data Science (DS) in Digital Journalism (DJ). Specifically, the aim is to present a critical literature review, synthetizing the main application areas of DS in DJ, highlighting research gaps, challenges, and op-portunities for future studies. Through a systematic literature review integrating bibliometric search, text min-ing, and qualitative discussion, the relevant literature was identified and extensively analyzed. The review reveals an increasing use of DS methods in DJ, with almost 47% of the research being published in the last three years. An hierarchical clustering highlighted six main research domains focused on text mining, event extraction, online comment analysis, recommendation systems, automated journalism, and exploratory data analysis along with some machine learning approaches. Future research directions comprise developing models to improve personalization and engagement features, exploring recommendation algorithms, testing new automated jour-nalism solutions, and improving paywall mechanisms.Acknowledgements This work was supported by the FCT-Funda?a ? o para a Ciência e Tecnologia, under the Projects: UIDB/04466/2020, UIDP/04466/2020, and UIDB/00319/2020

PEN recsys: a Personalized News Recommender Systems Framework (DEMO)

We present the Personalized News (PEN) recommender systems framework, (Note that this paper is an extended abstract of Garcin2013b), currently in use by a newspaper website to evaluate various algorithms for news recommendations. We briefly describe its system architecture and related components. We show how a researcher can easily evaluate different algorithms thanks to a web-based interface

Leveraging Large Language Models for Sequential Recommendation

Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches. In this work, we explore how large language models (LLMs), which are nowadays introducing disruptive effects in many AI-based applications, can be used to build or improve sequential recommendation approaches. Specifically, we devise and evaluate three approaches to leverage the power of LLMs in different ways. Our results from experiments on two datasets show that initializing the state-of-the-art sequential recommendation model BERT4Rec with embeddings obtained from an LLM improves NDCG by 15-20% compared to the vanilla BERT4Rec model. Furthermore, we find that a simple approach that leverages LLM embeddings for producing recommendations, can provide competitive performance by highlighting semantically related items. We publicly share the code and data of our experiments to ensure reproducibility.Comment: 9 page