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Jerbi, Houssem
Preferred name
Jerbi, Houssem
Official Name
Jerbi, Houssem
Research Output
Now showing 1 - 5 of 5
- PublicationPersonalised Recommendations for Modes of Transport: A Sequence-based Approach(2016-08-14)
; ; In this paper we consider the problem of recommending modes of transport to users in an urban setting. In particular, we build on our past work in which a general framework for activity recommendation is proposed. To model the personal preferences and habits of users, the framework uses a sequence-based approach to capture the order as well as the context associated with user activity patterns. Here, we extend this work by introducing a machine learning approach to learn and take into account the natural variations in the regularity and repetition of individual user behaviour that occur. We demonstrate the versatility of our recommendation framework by applying it to the transport domain, and an evaluation using a real-world (mode of transport) dataset demonstrates the efficacy of the approach.321 - PublicationAn Analysis Framework for Content-based Job Recommendation(2014-09-29)
; ; In this paper, we focus on the task of job recommendation. In particular, we consider several personalised content-based and case-based approaches to recommendation. We investigate a number of feature-based item representations, along with a variety of feature weighting schemes. A comparative evaluation of the various approaches is performed using a realworld, open source dataset.755 - PublicationTowards the Recommendation of Personalised Activity Sequences in the Tourism DomainIn this paper we consider the problem of recommending sequencesof activities to a user. The proposed approach leverages the order aswell as the context associated with the users past activity patternsto make recommendations. This work extends the general activityrecommendation framework proposed in [16] to iteratively recommendthe next sequence of activities to perform. We demonstratethe efficacy of our recommendation framework by applying it to thetourism domain and evaluations are performed using a real-world(checkin) dataset
187 - PublicationEvaluating the Relative Performance of Neighbourhood-Based Recommender Systems(2014-06-20)
; ; Neighbourhood-based recommender systems are a class of collaborative filtering algorithms, which rely on finding like-minded users to generate recommendations, automating what is usually known as word-of-mouth. These systems attempt to solve the information overload problem by presenting the user with relevant items. However, there is evidence showing that these algorithms may contribute to the filter bubble problem, making it harder for the user to find interesting items which are non-popular. In this paper we propose a novel evaluation of the performance and biases of the two most common neighbourhood-based approaches: user k-nearest neighbour collaborative filtering (UKNN ), and item k-nearest neighbour collaborative filtering (IKNN). We propose an evaluation which considers the size of the neighbourhood, finding that optimising for accuracy in UKNN algorithms leads to a poor performance in terms of diversity, a higher bias towards popularity, and less unique recommendations, when compared to the IKNN approach.112 - PublicationTowards Activity Recommendation from LifelogsWith the increasing availability of passive, wearable sensor devices, digital lifelogs can now be captured for individuals. Lifelogs contain a digital trace of a person’s life, and are characterised by large quantities of rich contextual data. In this paper, we propose a content based recommender system to leverage such lifelogs to suggest activities to users. We model lifelogs as timelines of chronological sequences of activity objects, and describe a recommendation framework in which a two-level distance metric is proposed to measure the similarity between current and past timelines. An initial evaluation of our activity recommender performed using a real-world lifelog dataset demonstrates the utility of our approach.
280Scopus© Citations 8