Now showing 1 - 10 of 53
  • Publication
    An Analysis Framework for Content-based Job Recommendation
    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.
      901
  • Publication
    Towards a reputation-based model of social web search
    (Association for Computing Machinery, 2010-02-07) ; ; ; ;
    While web search tasks are often inherently collaborative in nature, many search engines do not explicitly support collaboration during search. In this paper, we describe HeyStaks (www.heystaks.com), a system that provides a novel approach to collaborative web search. Designed to work with mainstream search engines such as Google, HeyStaks supports searchers by harnessing the experiences of others as the basis for result recommendations. Moreover, a key contribution of our work is to propose a reputation system for HeyStaks to model the value of individual searchers from a result recommendation perspective. In particular, we propose an algorithm to calculate reputation directly from user search activity and we provide encouraging results for our approach based on a preliminary analysis of user activity and reputation scores across a sample of HeyStaks users.
      1459Scopus© Citations 21
  • Publication
    Evaluation of Hierarchical Clustering via Markov Decision Processes for Efficient Navigation and Search
    In this paper, we propose a new evaluation measure to assessthe quality of a hierarchy in supporting search queries to content collections.The evaluation measure models the scenario of a searcher seeking a particular target item in the hierarchy. It takes into account the structureof the hierarchy by measuring the cognitive challenge of determiningthe correct path in the hierarchy as well as the reduction in search timeaorded by hierarchy. The goal is to propose a general-purpose measurethat can be applied in dierent application contexts, allowing dierenthierarchical arrangements of content to be quantitatively assessed
      551Scopus© Citations 2
  • Publication
    Mining the Real-Time Web: A Novel Approach to Product Recommendation
    Real-time web (RTW) services such as Twitter allow users to express their opinions and interests, often expressed in the form of short text messages providing abbreviated and highly personalized commentary in real-time. Although this RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research, it can contain useful consumer reviews on products, services and brands. This paper describes how Twitter-like short-form messages can be leveraged as a source of indexing and retrieval information for product recommendation. In particular, we describe how users and products can be represented from the terms used in their associated reviews. An evaluation performed on four different product datasets from the Blippr service shows the potential of this type of recommendation knowledge, and the experiments show that our proposed approach outperforms a more traditional collaborative-filtering based approach.
      6237Scopus© Citations 80
  • Publication
    A classification-based review recommender
    Many online stores encourage their users to submit product or service reviews in order to guide future purchasing decisions. These reviews are often listed alongside product recommendations but, to date, limited attention has been paid as to how best to present these reviews to the end-user. In this paper, we describe a supervised classification approach that is designed to identify and recommend the most helpful product reviews. Using the TripAdvisor service as a case study, we compare the performance of several classification techniques using a range of features derived from hotel reviews.We then describe how these classifiers can be used as the basis for a practical recommender that automatically suggests the most-helpful contrasting reviews to end-users. We present an empirical evaluation which shows that our approach achieves a statistically significant improvement over alternative review ranking schemes.
    Scopus© Citations 83  1234
  • Publication
    Towards Activity Recommendation from Lifelogs
    With 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.
    Scopus© Citations 9  332
  • Publication
    A Case Study of Collaboration and Reputation in Social Web Search.
    Although collaborative searching is not supported by mainstream search engines, recent research has high- lighted the inherently collaborative nature of many web search tasks. In this paper, we describe HeyStaks (www.heystaks.com), a collaborative web search framework that is designed to complement mainstream search engines. At search time, HeyStaks learns from the search activities of other users and leverages this information to generate recommendations based on results that others have found relevant for similar searches. The key contribution of this paper is to extend the HeyStaks social search model by considering the search expertise, or reputation, of HeyStaks users and using this information to enhance the result recommendation process. In particular, we propose a reputation model for HeyStaks users that utilises the implicit collaboration events that take place between users as recommendations are made and selected. We describe a live-user trial of HeyStaks that demonstrates the relevance of its core recommendations and the ability of the reputation model to further improve recommendation quality. Our findings indicate that incorporating reputation into the recommendation process further improves the relevance of HeyStaks recommendations by up to 40%.
      2180Scopus© Citations 33
  • Publication
    Effective product recommendation using the real-time web
    The 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 highly personalized commentary in real-time. Today, Twitter is undoubtedly the king of the RTW. It boasts 190 million users and generates in the region of 65m tweets per day. This RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research but it is useful to consider its applicability to recommendation scenarios. In this paper we consider harnessing the real-time opinions of users, expressed through the Twitter-like short textual reviews available on the Blippr service (www.blippr.com). In particular we describe how users and products can be represented from the terms used in their associated reviews and describe experiments to highlight the recommendation potential of this RTW data-source and approach.
      4552Scopus© Citations 18
  • Publication
    Social and collaborative web search : an evaluation study
    In this paper we describe the results of a live-user study to demonstrate the benefits of using the social search utility HeyStaks, a novel approach to Web search that combines ideas from personalization and social networking to provide a more collaborative search experience.
    Scopus© Citations 9  632
  • Publication
    An Exploration of Mood Classification in the Million Songs Dataset
    (Music Technology Research Group, Department of Computer Science, Maynooth University, 2015-08-01) ;
    As the music consumption paradigm moves towards streamingservices, users have access to increasingly large catalogsof music. In this scenario, music classification playsan important role in music discovery. It enables, for example, search by genres or automatic playlist creation based on mood. In this work we study the classification of songmood, using features extracted from lyrics alone, basedon a vector space model representation. Previous work inthis area reached contradictory conclusions based on experimentscarried out using different datasets and evaluationmethodologies. In contrast, we use a large freelyavailabledataset to compare the performance of differentterm-weighting approaches from a classification perspective.The experiments we present show that lyrics can successfullybe used to classify music mood, achieving accuraciesof up to 70% in some cases. Moreover, contraryto other work, we show that the performance of the differentterm weighting approaches evaluated is not statisticallydifferent using the dataset considered. Finally, we discuss the limitations of the dataset used in this work, and the need for a new benchmark dataset to progress work in this area.
      833