With a Little Help from My Friends: Use of Recommendations at School

In this exploratory paper, we study the usage of recommendations by and for children (ages 9 to 11) in an educational setting. From our preliminary analysis, it becomes apparent that recommender systems (RS) could provide extra support to and help children successfully complete inquiry tasks. Nonetheless, children have difficulty in recognizing the role of RS, in terms of aiding information discovery for classroom assignments. Findings from our study set a foundation that can inform future design and development of RS for children that support classroom-related work

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

Flow Moods: Recommending Music by Moods on Deezer

The music streaming service Deezer extensively relies on its Flow algorithm, which generates personalized radio-style playlists of songs, to help users discover musical content. Nonetheless, despite promising results over the past years, Flow used to ignore the moods of users when providing recommendations. In this paper, we present Flow Moods, an improved version of Flow that addresses this limitation. Flow Moods leverages collaborative filtering, audio content analysis, and mood annotations from professional music curators to generate personalized mood-specific playlists at scale. We detail the motivations, the development, and the deployment of this system on Deezer. Since its release in 2021, Flow Moods has been recommending music by moods to millions of users every day.Comment: 16th ACM Conference on Recommender Systems (RecSys 2022) - Industry pape

PrivateJobMatch: A Privacy-Oriented Deferred Multi-Match Recommender System for Stable Employment

Coordination failure reduces match quality among employers and candidates in the job market, resulting in a large number of unfilled positions and/or unstable, short-term employment. Centralized job search engines provide a platform that connects directly employers with job-seekers. However, they require users to disclose a significant amount of personal data, i.e., build a user profile, in order to provide meaningful recommendations. In this paper, we present PrivateJobMatch -- a privacy-oriented deferred multi-match recommender system -- which generates stable pairings while requiring users to provide only a partial ranking of their preferences. PrivateJobMatch explores a series of adaptations of the game-theoretic Gale-Shapley deferred-acceptance algorithm which combine the flexibility of decentralized markets with the intelligence of centralized matching. We identify the shortcomings of the original algorithm when applied to a job market and propose novel solutions that rely on machine learning techniques. Experimental results on real and synthetic data confirm the benefits of the proposed algorithms across several quality measures. Over the past year, we have implemented a PrivateJobMatch prototype and deployed it in an active job market economy. Using the gathered real-user preference data, we find that the match-recommendations are superior to a typical decentralized job market---while requiring only a partial ranking of the user preferences.Comment: 45 pages, 28 figures, RecSys 201