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- short-paperOctober 2024
Recommending Personalised Targeted Training Adjustments for Marathon Runners
RecSys '24: Proceedings of the 18th ACM Conference on Recommender SystemsPages 1051–1056https://doi.org/10.1145/3640457.3688192Preparing for the marathon involves many weeks of dedicated training. Achieving the right balance between building strength and endurance and the need for rest and recovery is a must, if a runner is to arrive at the start-line injury-free and ready to ...
- short-paperSeptember 2020
Fit to Run: Personalised Recommendations for Marathon Training
RecSys '20: Proceedings of the 14th ACM Conference on Recommender SystemsPages 480–485https://doi.org/10.1145/3383313.3412228Training for the marathon is a complex problem. In order to run an optimal time, runners must find the right workload for their current abilities and identify the correct balance between the hard work and rest throughout their training programmes. We ...
- short-paperSeptember 2020
Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners
RecSys '20: Proceedings of the 14th ACM Conference on Recommender SystemsPages 539–544https://doi.org/10.1145/3383313.3412220Millions of people participate in marathon events every year, typically devoting at least 12-16 weeks to building their endurance and fitness so that they can safely complete these gruelling 42.2km races. Most runners follow a training plan that is ...
- short-paperSeptember 2020
Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation
- Francisco J. Peña,
- Diarmuid O'Reilly-Morgan,
- Elias Z. Tragos,
- Neil Hurley,
- Erika Duriakova,
- Barry Smyth,
- Aonghus Lawlor
RecSys '20: Proceedings of the 14th ACM Conference on Recommender SystemsPages 438–443https://doi.org/10.1145/3383313.3412207Nowadays we commonly have multiple sources of data associated with items. Users may provide numerical ratings, or implicit interactions, but may also provide textual reviews. Although many algorithms have been proposed to jointly learn a model over both ...
- short-paperSeptember 2019
PDMFRec: a decentralised matrix factorisation with tunable user-centric privacy
- Erika Duriakova,
- Elias Z. Tragos,
- Barry Smyth,
- Neil Hurley,
- Francisco J. Peña,
- Panagiotis Symeonidis,
- James Geraci,
- Aonghus Lawlor
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 457–461https://doi.org/10.1145/3298689.3347035Conventional approaches to matrix factorisation (MF) typically rely on a centralised collection of user data for building a MF model. This approach introduces an increased risk when it comes to user privacy. In this short paper we propose an alternative,...
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- short-paperSeptember 2019Best Short Paper
Pace my race: recommendations for marathon running
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 246–250https://doi.org/10.1145/3298689.3346991We propose marathon running as a novel domain for recommender systems and machine learning. Using high-resolution marathon performance data from multiple marathon races (n = 7931), we build in-race recommendations for runners. We show that we can ...
- short-paperSeptember 2019
PyRecGym: a reinforcement learning gym for recommender systems
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsPages 491–495https://doi.org/10.1145/3298689.3346981Recommender systems (RS) share many features and objectives with reinforcement learning (RL) systems. The former aim to maximise user satisfaction by recommending the right items to the right users at the right time, the latter maximise future rewards ...
- demonstrationSeptember 2018
Module advisor: a hybrid recommender system for elective module exploration
RecSys '18: Proceedings of the 12th ACM Conference on Recommender SystemsPages 498–499https://doi.org/10.1145/3240323.3241613Recommender systems are omni-present in our every day lives, guiding us through the vast amount of information available. However, in the academic world, personalised recommendations are less prominent, leaving students to navigate through the typically ...
- research-articleSeptember 2018
Why I like it: multi-task learning for recommendation and explanation
RecSys '18: Proceedings of the 12th ACM Conference on Recommender SystemsPages 4–12https://doi.org/10.1145/3240323.3240365We describe a novel, multi-task recommendation model, which jointly learns to perform rating prediction and recommendation explanation by combining matrix factorization, for rating prediction, and adversarial sequence to sequence learning for ...
- short-paperAugust 2017
A Novel Recommender System for Helping Marathoners to Achieve a New Personal-Best
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender SystemsPages 116–120https://doi.org/10.1145/3109859.3109874We describe a novel application for recommender systems -- helping marathon runners to run a new personal-best race-time -- by predicting a challenging, but achievable target-time, and by recommending a tailored race-plan to achieve this time. A ...
- short-paperSeptember 2015
The Recommendation Game: Using a Game-with-a-Purpose to Generate Recommendation Data
RecSys '15: Proceedings of the 9th ACM Conference on Recommender SystemsPages 305–308https://doi.org/10.1145/2792838.2799675This paper describes a casual Facebook game to capture recommendation data as a side-effect of gameplay. We show how this data can be used to make successful recommendations as part of a live-user trial.
- research-articleOctober 2013
The curated web: a recommendation challenge
RecSys '13: Proceedings of the 7th ACM conference on Recommender systemsPages 101–104https://doi.org/10.1145/2507157.2507216In this paper we consider the application of content-based recommendation techniques to web curation services which allow users to curate and share topical collections of content (e.g. images, news, web pages etc.). Curation services like Pinterest are ...
- posterOctober 2013
Sentimental product recommendation
RecSys '13: Proceedings of the 7th ACM conference on Recommender systemsPages 411–414https://doi.org/10.1145/2507157.2507199This paper describes a novel approach to product recommendation that is based on opinionated product descriptions that are automatically mined from user-generated product reviews. We present a recommendation ranking strategy that combines similarity and ...
- demonstrationSeptember 2012
Yokie: explorations in curated real-time search & discovery using twitter
RecSys '12: Proceedings of the sixth ACM conference on Recommender systemsPages 307–308https://doi.org/10.1145/2365952.2366026Our research involves developing technology and techniques that apply the vast sea of real-time web data to interesting problems and topics. In this demo, we will present the on- going development of a novel real-time search and discovery service named ...
- demonstrationSeptember 2012
The demonstration of the reviewer's assistant
RecSys '12: Proceedings of the sixth ACM conference on Recommender systemsPages 297–298https://doi.org/10.1145/2365952.2366021User generated reviews are now a familiar and valuable part of most e-commerce sites since high quality reviews are known to influence purchasing decisions. In this demonstration we describe work on the Reviewer's Assistant (RA), which is a ...
- short-paperSeptember 2012
HeyStaks: a real-world deployment of social search
RecSys '12: Proceedings of the sixth ACM conference on Recommender systemsPages 289–292https://doi.org/10.1145/2365952.2366017The purpose of this paper is to provide a deployment update for the HeyStaks social search system which uses recommendation techniques to add collaboration to mainstream search engines such as Google, Bing, and Yahoo. We describe our the results of ...
- posterOctober 2011
Power to the people: exploring neighbourhood formations in social recommender system
RecSys '11: Proceedings of the fifth ACM conference on Recommender systemsPages 337–340https://doi.org/10.1145/2043932.2043997The explosive growth of online social networks in recent times has presented a powerful source of information to be utilised in personalised recommendations. Unsurprisingly there has already been a large body of work completed in the recommender system ...
- posterSeptember 2010
On the real-time web as a source of recommendation knowledge
RecSys '10: Proceedings of the fourth ACM conference on Recommender systemsPages 305–308https://doi.org/10.1145/1864708.1864773The so-called real-time web (RTW) is a web of opinions, comments, and personal viewpoints, often expressed in the form of short, 140-character text messages providing abbreviated and personalized commentary in real-time. Twitter is undoubtedly the king ...
- research-articleSeptember 2010
Recommending twitter users to follow using content and collaborative filtering approaches
RecSys '10: Proceedings of the fourth ACM conference on Recommender systemsPages 199–206https://doi.org/10.1145/1864708.1864746Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender ...
- short-paperOctober 2009
Using twitter to recommend real-time topical news
RecSys '09: Proceedings of the third ACM conference on Recommender systemsPages 385–388https://doi.org/10.1145/1639714.1639794Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. In this paper, we describe a novel approach to news recommendation that harnesses real-time micro-blogging activity, from ...
