Item representation holds significant importance in recommendation systems, which encompasses domains such as news, retail, and videos. Retrieval and ranking models utilise item representation to capture the user-item relationship based on user ...

Food recommendation systems play a crucial role in promoting personalized recommendations designed to help users find food and recipes that align with their preferences. However, many existing food recommendation systems have overlooked the important ...

Recent years have seen a rapid increase in the application of computational approaches to different HR tasks, such as algorithmic hiring, skill extraction, and monitoring of employee satisfaction. Much of the recent work on estimating the fit between a ...

LensKit is one of the first and most popular Recommender System libraries. While LensKit offers a wide variety of features, it does not include any optimization strategies or guidelines on how to select and tune LensKit algorithms. LensKit developers ...

With the clinical environment becoming more data-reliant, healthcare professionals now have unparalleled access to comprehensive clinical information from numerous sources. Then, one of the main issues is how to avoid overloading practitioners with ...

We propose a robust recommender systems model which performs matrix completion and a ratings-wise uncertainty estimation jointly. Whilst the prediction module is purely based on an implicit low-rank assumption imposed via nuclear norm regularization, ...

The complexity associated to group recommendations needs strategies to mitigate several problems, such as the group's heterogeinity and conflicting preferences, the emotional contagion phenomenon, the cold-start problem, and the group members’ needs and ...

Outdoor sport climbing in Northern Italy attracts climbers from around the world. While this country has many rock formations, it offers enormous possibilities for adventurous people to explore the mountains. Unfortunately, this great potential causes a ...

Negative item sampling in offline top-n recommendation evaluation has become increasingly wide-spread, but remains controversial. While several studies have warned against using sampled evaluation metrics on the basis of being a poor approximation of ...

As personalization has great potential to improve mobile health apps, analyzing the effect of different recommender algorithms in the health domain is still in its infancy. As such, this paper investigates whether more accurate recommendations from a ...

Although beyond-accuracy metrics have gained attention in the last decade, the accuracy of recommendations is still considered the gold standard to evaluate Recommender Systems (RSs). This approach prioritizes the accuracy of recommendations, neglecting ...

A recommender system operates in a dynamic environment where new items emerge and new users join the system, resulting in ever-growing user-item interactions over time. Existing works either assume a model trained offline on a static dataset (requiring ...

The description of an item plays a pivotal role in providing concise and informative summaries to captivate potential viewers and is essential for recommendation systems. Traditionally, such descriptions were obtained through manual web scraping ...

The debut of ChatGPT has recently attracted significant attention from the natural language processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT shows significant improvement in a range of downstream NLP tasks, but the ...

Recently sequential recommendations and next-item prediction task has become increasingly popular in the field of recommender systems. Currently, two state-of-the-art baselines are Transformer-based models SASRec and BERT4Rec. Over the past few years, ...

Sequential Recommender Systems (SRSs) are a popular type of recommender system that leverages user history to predict the next item of interest. However, the presence of noise in user interactions, stemming from account sharing, inconsistent preferences,...

Multi-task optimization is an emerging research field in recommender systems that focuses on improving the recommendation performance of multiple tasks. Various methods have been proposed in the past to address task weight balancing, gradient conflict ...

We consider the problem of sequential recommendation, where the current recommendation is made based on past interactions. This recommendation task requires efficient processing of the sequential data and aims to provide recommendations that maximize ...

Improvements in the recommender systems (RS) domain are not possible without a thorough way to evaluate and compare newly proposed approaches. User studies represent a viable alternative to online and offline evaluation schemes, but despite their ...

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 ...