9 research outputs found
Formalizing Multimedia Recommendation through Multimodal Deep Learning
Recommender systems (RSs) offer personalized navigation experiences on online
platforms, but recommendation remains a challenging task, particularly in
specific scenarios and domains. Multimodality can help tap into richer
information sources and construct more refined user/item profiles for
recommendations. However, existing literature lacks a shared and universal
schema for modeling and solving the recommendation problem through the lens of
multimodality. This work aims to formalize a general multimodal schema for
multimedia recommendation. It provides a comprehensive literature review of
multimodal approaches for multimedia recommendation from the last eight years,
outlines the theoretical foundations of a multimodal pipeline, and demonstrates
its rationale by applying it to selected state-of-the-art approaches. The work
also conducts a benchmarking analysis of recent algorithms for multimedia
recommendation within Elliot, a rigorous framework for evaluating recommender
systems. The main aim is to provide guidelines for designing and implementing
the next generation of multimodal approaches in multimedia recommendation
How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation
Recommender Systems have shown to be an efective way to alleviate the over-choice problem and provide
accurate and tailored recommendations. However, the impressive number of proposed recommendation
algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental
evaluation particularly challenging. ELLIOT is a comprehensive recommendation framework that aims
to run and reproduce an entire experimental pipeline by processing a simple confguration fle. The
framework loads, flters, and splits the data considering a vast set of strategies. Then, it optimizes
hyperparameters for several recommendation algorithms, selects the best models, compares them with
the baselines, computes metrics spanning from accuracy to beyond-accuracy, bias, and fairness, and
conducts statistical analysis. The aim is to provide researchers a tool to ease all the experimental
evaluation phases (and make them reproducible), from data reading to results collection. ELLIOT is
freely available on GitHub at https://github.com/sisinflab/ellio