ABSTRACT In this paper, we propose a context‐aware fragrance recommendation model, which moves the task beyond static similar scent matching towards situational recommendation, where different perfumes are appropriate for different social environments. In our formulation, a perfume collection is modelled as a dynamic olfactory wardrobe, where each perfume is tailored to specific environmental and social demands. We formalize each perfume through a unified representation which combines contextual suitability and affective signals extracted from user reviews, together with olfactory catalogue data. The proposed model integrates deterministic top‐k ranking and stochastic candidate selection to balance relevance and exploration. We assess the performance of the model with complementary evaluation measures covering identity retrieval, ranking quality of success hits, semantic utility and catalogue exploration. The results indicate that while even simple popularity baselines remain highly competitive in raw retrieval precision, our fused context‐affective model provides stronger contextual utility than single‐feature strategies. Furthermore, our stochastic ranking variant substantially improves catalogue coverage with only a limited loss in retrieval quality.

Compared to other recommender systems domains, perfume recommendation proves to be highly personalized and more challenging due to the very subjective factors and complex mixture of involved senses.Individual perfume preferences are influenced by subtle elements such as emotional associations, personal memories, and unique biochemistry, making it difficult for users to clearly express their olfactory preferences.This paper provides an insight of significant challenges in perfume recommendations planned to be addressed in the context of my ongoing PhD project.By exploring these areas, I aim to make a meaningful contribution to the ongoing development of perfume recommender systems.

In this paper, we propose a method for obtaining personalized book recommendations using content-based filtering approach and three sets of book features to define the book metadata, in order to highlight their impact in the recommendation process. The recommender system is experimentally validated using four books datasets of different sizes, collected from Goodreads website - a popular book social network, using our customized web scraper. Lastly, we propose three evaluation metrics: Coverage, Average Recommendations Similarity and Relevance, and discuss our results.

In this contribution, we present a method for obtaining literature books recommendations using collaborative filtering recommender system technique and emotions extracted from multi-source online reviews.We experimentally validated the proposed system using a book dataset and associated reviews that we collected from Goodreads and Amazon websites using our customized web scrapers.We show the benefits of using multisource reviews by proposing a series of recommender system evaluation measures, which include single-source and multisource recommendations similarity, recommendation algorithm usecases coverage and generated recommendations relevance.

In this contribution, we propose a method for providing User-Based Collaborative Filtering Recommendations using the emotions present in social media reviews. We use a deep learning model which identifies the review dominant emotion from the 6 primary emotions proposed by W.G. Parrott. For experiments, we use a dataset containing book reviews and movie reviews that we collected from Goodreads and IMDB websites using our customized web scrapers. Moreover, we represent the book and associated movie as an unique item in the dataset. We validated our recommender system using a set of system-unseen reviews that simulate a set of users seeking for recommendations. The top k recommendations received for each simulated user are then analyzed by our proposed performance measures.

In this paper we propose two book recommendation methods based on emotions extracted from user reviews, using content-based filtering and collaborative filtering.The methods were experimentally evaluated on our own dataset that we collected from Goodreads -a popular website with large database of books and readers reviews.We created an experimental setup where the recommendation algorithms for carrying out the evaluation using two proposed evaluation metrics: coverage and average recommendations similarity.