Michael P. O’Mahony

University College Dublin | UCD · School of Computer Science

Modern universities present students with a wide array of course and module options, making it difficult for students to make informed decisions about the modules they take and how their choices can help them achieve their educational goals. This is exacerbated when students are uncertain about their goals or when limited information about module o...

Modern universities present students with a dizzying array of course and module options, making it difficult for students to make informed decisions about the modules they take and how their choices can help them achieve their educational goals. This is exacerbated when students are uncertain about their goals or when limited information about modu...

Since the last decade, data collection is becoming more pervasive, passive and easier to perform. This is resulting in the rise of data wherein a user performs some activities in a sequence, such as locations visited, physical activities performed, and modes of transport taken. In such cases, activities are often performed in a particular order, an...

The e-commerce boom has shifted the real estate market to an online medium whereby realtors and owners can post their property listings. A recent focus of the research community has been studying various aspects of these online property portals. We perform a textual analytics study for a popular Irish portal with Dublin being our case-study on acco...

Starting their academic career can be overwhelming for many young people. Students are often presented with a variety of options within their programmes of study and making appropriate and informed decisions can be a challenge. Compared to many other areas in our everyday life, recommender systems remain underused in the academic setting. In this p...

Recommender 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 large space of available courses and modules manually. Since it is crucial for students to make...

Traditionally, recommender systems have relied on user preference data (such as ratings) and product descriptions (such as meta-data) as primary sources of recommendation knowledge. More recently, new sources of recommendation knowledge in the form of social media information and other kinds of user-generated content have emerged as viable alternat...

Personalised recommendations feature prominently in many aspects of our lives, from the movies we watch, to the news we read, and even the people we date. However, one area that is still relatively underdeveloped is the educational sector where recommender systems have the potential to help students to make informed choices about their learning pat...

In this paper, we propose a new evaluation measure to assess the 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 structure of the hierarchy by measuring the cognitive challenge of determin...

In this paper we consider the problem of recommending sequences of activities to a user. The proposed approach leverages the order as well as the context associated with the user’s past activity patterns to make recommendations. This work extends the general activity recommendation framework proposed in [16] to iteratively recommend the next sequen...

This paper demonstrates a Web service called TweetCric to uncover cricket insights from Twitter with the aim of facilitating sports analysts and journalists. It essentially arranges crowdsourced Twitter data about a team in comprehensive visualizations by incorporating domain-specific approaches to sentiment analysis.

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...

In the world of recommender systems, so-called content-based methods are an important approach that rely on the availability of detailed product or item descriptions to drive the recommendation process. For example, recommendations can be generated for a target user by selecting unseen products that are similar to the products that the target user...

As the music consumption paradigm moves towards streaming services, users have access to increasingly large catalogs of music. In this scenario, music classification plays an 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 song mo...

User-generated reviews are now plentiful online and they have proven to be a valuable source of real user opinions and real user experiences. In this chapter we consider recent work that seeks to extract topics, opinions, and sentiment from review text that is unstructured and often noisy. We describe and evaluate a number of practical case-studies...

Modern web search engines have come to dominate how millions of people find the information that they are looking for online. While the sheer scale and success of the leading search engines is a testimony to the scientific and engineering progress that has been made over the last two decades, mainstream search is not without its challenges. Mainstr...

Past work on the evaluation of recommender systems indicates that collaborative filtering algorithms are accurate and suitable for the top-N recommendation task. Further, the importance of performance beyond accuracy has been recognised in the literature. Here, we present an evaluation framework based on a set of accuracy and beyond accuracy metric...

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 activiti...

The classification of television content helps users organise and navigate through the large list of channels and programs now available. In this paper, we address the problem of television content classification by exploiting text information extracted from program transcriptions. We present an analysis which adapts a model for sentiment that has...

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...

In this paper we build on recent work on case-based product recommendation focused on generating rich product descriptions for use in a recommendation context by mining user-generated reviews. This is in contrast to conventional case-based approaches which tend to rely on case descriptions that are based on available meta-data or catalog descriptio...

In this paper we build on recent work on case-based product recommendation focused on generating rich product descriptions for use in a recommendation context by mining user-generated reviews. This is in contrast to conventional case-based approaches which tend to rely on case descriptions that are based on available meta-data or catalog descriptio...

Today, people increasingly leverage their online social networks to discover meaningful and relevant information, products and services. Thus, the ability to identify reputable online contacts with whom to interact has become ever more important. In this work we describe a generic approach to modeling user and item reputation in social recommender...

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 show...

This 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 sentiment to suggest products that are similar but superior to a query product according to the...

Automatically identifying informative reviews is increasingly important given the rapid growth of user generated reviews on sites like Amazon and TripAdvisor. In this paper, we describe and evaluate techniques for identifying and recommending helpful product reviews using a combination of review features, including topical and sentiment information...

Case-based reasoning (CBR) attempts to reuse past expe-riences to solve new problems. CBR ideas are commonplace in recom-mendation systems, which rely on the similarity between product queries and a case base of product cases. But, the relationship between CBR and many of these recommenders can be tenuous: the idea that prod-uct cases made up of st...

In this paper we describe a novel approach to case-based product recommendation. It is novel because it does not leverage the usual static, feature-based, purely similarity-driven approaches of traditional case-based recommenders. Instead we harness experiential cases, which are automatically mined from user generated reviews, and we use these as t...

Supplementing product information with user-generated content such as ratings and reviews can help to convert browsers into buyers. As a result this type of content is now front and centre for many major e-commerce sites such as Amazon. We believe that this type of content can provide a rich source of valuable information that is useful for a varie...

Real-time information streams such as Twitter have become a common way for users to discover new information. For most users this means curating a set of other users to follow. However, at the moment the following granularity of Twitter is restricted to the level of individual users. Our research has highlighted that many following relationships ar...

Information streams allow social network users to receive and interact with the latest messages from friends and followers. But as our social graphs grow and mature it becomes increasingly difficult to deal with the information overload that these realtime streams introduce. Some social networks, like Facebook, use proprietary interestingness metri...

In this paper we describe a generic approach to modeling user reputation in online social platforms based on an underlying model of collaboration. This distinguishes our approach from more conventional reputation models which are often based around ad-hoc activity metrics. We evaluate our model with respect to a conventional reputation model used b...

User generated reviews are now a familiar and valuable part of most e-commerce sites since high quality reviews are known to influence purchasing de-cisions. In this paper we describe work on the Reviewer's Assistant (RA), which is a recommendation system that is designed to help users to write better reviews. It does this by suggesting relevant to...

There is no doubting the incredible impact of Twitter on how we communicate, access and share information online. Currently users can follow other users or hashtags in order to benefit from a stream of data from people they trust or on topics that matter to them. However at the moment the following granularity of Twitter means that users cannot lim...

Today, online reviews for products and services have become an important class of user-generated content and they play a valuable role for countless online businesses by helping to convert casual browsers into informed and satisfied buyers. As users gravitate towards sites that offer insightful and objec-tive reviews, the ability to source helpful...

User 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 recommendation system that is designed to help users to write better quality reviews. It does this by suggest...

Today, online reviews for products and services have become an important class of user-generated content and they play a valuable role for countless online businesses by helping to convert casual browsers into informed and satisfied buyers. In many respects, the content of user reviews is every bit as important as the catalog content that describes...

In this paper we describe an initial attempt to build multi-faceted user models from raw Twitter data. The key contribution is to describe a technique for categorising users and their social ties according to a collection of curated topical categories and in this way resolve much of the preference noise that is inherent within user conversations. W...

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 sys...

User opinions and reviews are an important part of the modern web and all major e-commerce sites typically provide their users with the ability to provide and access customer reviews across their product catalog. The importance of reviews has driven the need to improve the review quality by providing interactive support for the reviewer and we will...

User opinions and reviews are an important part of the modern web and all major e-commerce sites typically provide their users with the ability to provide and access customer reviews across their product catalog. Indeed this has become a vital part of the service provided by sites like Amazon and TripAdvisor, so much so that many of us will routine...

In the past recommender systems have relied heavily on the availability of ratings data as the raw material for recommendation. Moreover, popular collaborative filtering approaches generate recommendations by drawing on the interests of users who share similar ratings patterns. This is set to change because of the unbundling of social networks (via...

Although collaborative searching is not supported by mainstream search engines, recent research has highlighted the inherently collaborative nature of many Web search tasks. In this article, we describe HeyStaks, a collaborative Web search framework that is designed to complement mainstream search engines. At search time, HeyStaks learns from the s...

To date web search has been a solitary experience for the end-user, despite the fact that recent studies highlight the potential for collaboration that is inherent in many search tasks and scenarios. As a result, researchers have begun to explore the potential for a more collaborative approach to web search, one in which the search actions of other...

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.

The Social Web provides new and exciting sources of in- formation that may be used by recommender systems as a complementary source of recommendation knowledge. For example, User-Generated Content, such as reviews, tags, comments, tweets etc. can provide a useful source of item information and user preference data, if a clear signal can be extracte...

Collaborative recommender systems are vulnerable to malicious users who seek to bias their output, causing them to recommend (or not recommend) particular items. This problem has been an active research topic since 2002. Researchers have found that the most widely-studied memory-based algorithms have significant vulnerabilities to attacks that can...

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 personalized commentary in real-time. Twitter is undoubtedly the king of the RTW. It boasts 100+ million users and generates in the region of 50m tweets per day. This...

User-generated content provides online consumers with a wealth of information. Given the ever-increasing quantity of available content and the lack of quality control applied to this content, there is a clear need to enhance the user experience when it comes to effectively leveraging this vast information source. In this paper, we address these iss...

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...

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 p...

User-generated reviews are a common and valuable source of product information, yet little attention has been paid as to how best to present them to end-users. In this paper, we describe a classification-based recommender system that is designed to recommend the most helpful reviews for a given product. We present a large-scale evaluation of our ap...

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 ap...

In this paper, we consider a classification-based approach to the recommendation of user-generated product reviews. In particular, we develop review ranking techniques that allow the most helpful reviews for a particular product to be recommended, thereby facilitating users to readily asses the quality of the product in question. We apply a supervi...

Web search is the main way for millions of users to access information every day, but we continue to struggle when it comes to finding the right information at the right time. In this paper we build on recent work to describe and evaluate a new application of case-based Web search, one that focuses on how experience reuse can support collaboration...

Collaborative filtering (CF) techniques have proved to be a powerful and popular component of modern recommender systems. Common approaches such as user-based and item-based methods generate predictions from the past ratings of users by combining two separate ratings components: a base estimate, generally based on the average rating of the target u...

Web search is the dominant form of information access and everyday millions of searches are handled by mainstream search engines, but users still struggle to find what they are looking for, and there is much room for improvement. In this paper we describe a novel and practical approach to Web search that combines ideas from personalization and soci...

Trust, reputation and recommendation are key components of successful ecommerce systems. However, ecommerce systems are also vulnerable in this respect because there are opportunities for sellers to gain advantage through manipulation of reputation and recommendation. One such vulnerability is the use of fraudulent user profiles to boost (or damage...

While search engines are the primary means by which infor- mation is located online, significant issues remain when trying to satisfy the needs of searchers, especially in the face of the type of vague queries that dominate Web search. In this paper, we tackle this problem by ap- plying a recommender system approach to Web search which allows users...

In this paper we report on our work to date concerning the development of a course recommender system for University College Dublin's on-line enrollment application. We outline the factors that influence student choices and propose solutions to address some of the key considerations that are identified. We empirically evaluate our approach using hi...

Collaborative web search utilises past search histories in a community of like-minded users to improve the quality of search results. Search results that have been selected by community members for past queries are promoted in response to similar queries that occur in the future. The I-SPY system is one example of such a collaborative approach to s...

A work highlights the lack of robustness collaborative recommender systems exhibit against attack. This vulnerability can lead to significantly biased recommendations for target items. Here, we examine such attacks from a cost perspective, focusing on how attack size - that is, the number of ratings inserted - affects attack success. We introduce a...

In this paper, we propose a framework that enables the detection of noise in recommender system databases. We consider two classes of noise: natural and malicious noise. The issue of natural noise arises from imperfect user behaviour (e.g. erroneous/careless preference selection) and the various rating collection processes that are employed. Malici...

In the research to date, the performance of recom- mender systems has been extensively evaluated across various dimensions. Increasingly, the issue of robust- ness against malicious attack is receiving attention from the research community. In previous work, we have shown that knowledge of certain domain statistics is sufficient to allow successful...

Collaborative recommendation has emerged as an effective technique for personalized information access. However, there has been relatively little theoretical analysis of the conditions under which the technique is effective. To explore this issue, we analyse the robustness of collaborative recommendation: the ability to make recommendations despite...

Personalisation features are key to the success of many web applications and collaborative recommender systems have been widely implemented. These systems assist users in finding relevant information or products from the vast quantities that are frequently available. In previous work, we have demonstrated that such systems are vulnerable to attack...

In this paper we propose novel neighbourhood formation and similarity weight transformation schemes for automated collaborative filtering systems. We demonstrate the benefits of our schemes from the point-of-view of the efficiency and robustness provided, while achieving the accuracy and coverage of a benchmark k-Nearest Neighbour (k-NN) model.

Collaborative recommendation has emerged as an effective technique for personalised information access. However, there has been relatively little theoretical analysis of the conditions under which the technique is effective. To explore this issue, we analyse the robustness of collaborative recommendation: the ability to make recommendations despite...

In this paper, we introduce novel neighbourhood forma- tion and similarity weight transformation schemes for automated col- laborative ltering systems. We dene prole utility, which models the usefulness of user proles for collaborative ltering as a function of the items they contain. We demonstrate that our approach leads to more efcient and scalab...

Recommender systems are now widely used to overcome the information overload problem. Security is an ever-present concern, and since many of these systems operate in an on-line environment, it is necessary to understand and remove any security issues that may exist. In earlier work, we have demonstrated that collaborative recommender systems are in...

Collaborative filtering has nowb ecome a popular choice for reducing information overload. While many researchers have proposed and compared the performance of various collaborative filtering algorithms, one important performance measure has been omitted from the research to date. Robustness measures the power of an algorithm to make good predictio...

The growth and popularity of Internet applications has reinforced the need for effective information filtering techniques. The collaborative filtering approach is now a popular choice and has been implemented in many on-line systems. While many researchers have proposed and compared the performance of various collaborative filtering algorithms, one...

Consumers frequently rely on user-generated product reviews to guide purchasing decisions. Given the ever-increasing volume of such reviews and variations in re-view quality, consumers require assistance to effectively leverage this vast information source. In this paper, we examine to what extent the readability of reviews is a predictor of review...

Often today's recommender systems look to past user activ-ity in order to influence future recommendations. In the case of social web search, employing collaborative recommenda-tion techniques allows for personalization of search results. If recommendations arise from past user activity, the exper-tise of those users driving the recommendation proc...

There is growing recognition of the key role that social and informal learning play in Higher Education. There is also increasing interest in technologies that enable, capture and channel this type of learning to students at their point of need and personalised to their ability. The objective of this project was to leverage research technologies fr...