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Muhammad, Khalil
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Muhammad, Khalil
Official Name
Muhammad, Khalil
Research Output
Now showing 1 - 8 of 8
- PublicationOn the Use of Opinionated Explanations to Rank and Justify Recommendations(Association for the Advancement of Artificial Intelligence, 2016-05-18)
; ; Explanations are an important part of modern recommendersystems. They help users to make better decisions, improvethe conversion rate of browsers into buyers, and lead togreater user satisfaction in the long-run. In this paper, we extendrecent work on generating explanations by mining userreviews. We show how this leads to a novel explanation formatthat can be tailored for the needs of the individual user.Moreover, we demonstrate how the explanations themselvescan be used to rank recommendations so that items which canbe associated with a more compelling explanation are rankedahead of items that have a less compelling explanation. Weevaluate our approach using a large-scale, real-world TripAdvisordataset.200 - PublicationGenerating Personalised and Opinionated Review Summaries(2015)
; ; ; This paper describes a novel approach for summarising user-generated reviews for the purpose of explaining recommendations. Wedemonstrate our approach using TripAdvisor reviews.200 - PublicationGreat Explanations: Opinionated Explanations for RecommendationExplaining recommendations helps users to make better, more satisfying decisions. We describe a novel approach to explanation for recommender systems, one that drives the recommendation process, while at the same time providing the user with useful insights into the reason why items have been chosen and the trade-os they may need to consider when making their choice. We describe this approach in the context ofa case-based recommender system that harnesses opinions mined from user-generated reviews, and evaluate it on TripAdvisor Hotel data.
628Scopus© Citations 36 - PublicationExplanation-based Ranking in Opinionated Recommender SystemsExplanations can help people to make better choices, but their use in recommender systems has so far been limited to the annotation of recommendations after they have been ranked and suggested to the user. In this paper we argue that explanations can also be used to rank recommendations. We describe a technique that uses the strength of an item’s explanation as a ranking signal – preferring items with compelling explanations – and demonstrate its efficacy on a real-world dataset.
268 - PublicationMining Product Experiences from User Generated Reviews: A Recommender Systems Perspective(2014-09)
; ; ; We have employed algorithms described in to mine opinions from TripAdvisor hotel reviews; we have experimented with different parameters to learn which provided more meaningful extractions. Secondly, we have considered opinion summarization and search similar to. We have implemented a retrieval strategy that accepts natural language queries based on opinions from reviews. Additionally, we have proposed various methods of summarizing opinions based on statistical metrics. Currently, we are experimenting with feature quality metrics. Our aim is to establish a relevance score that describes the usefulness of extracted opinions. We are also running recommendation experiments using different versions the extracted opinions.211 - PublicationA Multi-Domain Analysis of Explanation-Based Recommendation using User-Generated ReviewsThis paper extends recent work on the use of explanations in recommender systems. In particular, we show how explanations can be used to rank as well as justify recommendations, then we compare the results to more conventional recommendation approaches, in three large-scale application domains.
205 - PublicationFedFast: Going Beyond Average for Faster Training of Federated Recommender Systems(ACM, 2020-08-27)
; ; ; ; ; ; ; Federated learning (FL) is quickly becoming the de facto standard for the distributed training of deep recommendation models, us-ing on-device user data and reducing server costs. In a typical FLprocess, a central server tasks end-users to train a shared recommen-dation model using their local data. The local models are trained over several rounds on the users’ devices and the server combinesthem into a global model, which is sent to the devices for the pur-pose of providing recommendations. Standard FL approaches userandomly selected users for training at each round, and simply average their local models to compute the global model. The resulting federated recommendation models require significant client effortto train and many communication rounds before they converge to asatisfactory accuracy. Users are left with poor quality recommendations until the late stages of training. We present a novel technique, FedFast, to accelerate distributed learning which achieves goodaccuracy for all users very early in the training process. We achievethis by sampling from a diverse set of participating clients in each training round and applying an active aggregation method that propagates the updated model to the other clients. Consequently, with FedFast the users benefit from far lower communication costsand more accurate models that can be consumed anytime during the training process even at the very early stages. We demonstrate the efficacy of our approach across a variety of benchmark datasetsand in comparison to state-of-the-art recommendation techniques609Scopus© Citations 145 - PublicationA Live-User Study of Opinionated Explanations for Recommender SystemsThis paper describes an approach for generating rich and compellingexplanations in recommender systems, based on opinionsmined from user-generated reviews. The explanationshighlight the features of a recommended item that matter mostto the user and also relate them to other recommendation alternativesand the users past activities to provide a context.
655Scopus© Citations 39