This thesis describes work on using content to improve recommendation systems. Personalised recommendations help potential buyers filter information and identify products that they might be interested in. Current recommender systems are based mainly on collaborative filtering (CF) methods, which suffer from two main problems: (1) the ramp-up problem, where items that do not have a sufficient amount of meta-data associated with them cannot be recommended; and (2) lack of transparency due to the fact that recommendations produced by the system are not clearly explained. In this thesis we tackle both of these problems. We outline a framework for generating more accurate recommendations that are based solely on available textual content or in combination with rating information. In particular, we show how content in the form of social tags can help improve recommendations in the book and movie domains. We address the ramp-up problem and show how in cases where they do not exist, social tags can be automatically predicted from available textual content, such as the full texts of books. We evaluate our methods using two sets of data that differ in product type and size. Finally we show how once products are selected to be recommended, social tags can be used to explain the recommendations. We conduct a web-based study to evaluate different styles of explanations and demonstrate how tag-based explanations outperform a common CF-based explanation and how a textual review-like explanation yields the best results in helping users predict how much they will like the recommended items.

The rapid growth of the World Wide Web in the last decade makes it the largest publicly accessible data source in the world, which has become one of the most significant and influential information revolution of modern times. The influence of the Web has impacted almost every aspect of humans' life, activities and fields, causing paradigm shifts and transformational changes in business, governance, and education. Moreover, the rapid evolution of Web 2.0 and the Social Web in the past few years, such as social blogs and friendship networking sites, has dramatically transformed the Web from a raw environment for information consumption to a dynamic and rich platform for information production and sharing worldwide. However, this growth and transformation of the Web has resulted in an uncontrollable explosion and abundance of the textual contents, creating a serious challenge for any user to find and retrieve the relevant information that he truly seeks to find on the Web. The process of finding a relevant Web page in a website easily and efficiently has become very difficult to achieve. This has created many challenges for researchers to develop new mining techniques in order to improve the user experience on the Web, as well as for organizations to understand the true informational interests and needs of their customers in order to improve their targeted services accordingly by providing the products, services and information that truly match the requirements of every online customer. With these challenges in mind, Web mining aims to extract hidden patterns and discover useful knowledge from Web page contents, Web hyperlinks, and Web usage logs. Based on the primary kinds of Web data used in the mining process, Web mining tasks can be categorized into three main types: Web content mining, which extracts knowledge from Web page contents using text mining techniques, Web structure mining, which extracts patterns from the hyperlinks that represent the structure of the website, and Web usage mining, which mines user's Web navigational patterns from Web server logs that record the Web page access made by every user, representing the interactional activities between the users and the Web pages in a website. The main goal of this thesis is to contribute toward addressing the challenges that have been resulted from the information explosion and overload on the Web, by proposing and developing novel Web mining-based approaches. Toward achieving this goal, the thesis presents, analyzes, and evaluates three major contributions. First, the development of an integrated Web structure and usage mining approach that recommends a collection of hyperlinks for the surfers of a website to be placed at the homepage of that website. Second, the development of an integrated Web content and usage mining approach to improve the understanding of the user's Web behavior and discover the user group interests in a website. Third, the development of a supervised classification model based on recent Social Web concepts, such as Tag Clouds, in order to improve the retrieval of relevant articles and posts from Web social blogs.

The rapid growth of the World Wide Web in the last decade makes it the largest publicly accessible data source in the world, which has become one of the most significant and influential information revolution of modern times. The influence of the Web has impacted almost every aspect of humans' life, activities and fields, causing paradigm shifts and transformational changes in business, governance, and education. Moreover, the rapid evolution of Web 2.0 and the Social Web in the past few years, such as social blogs and friendship networking sites, has dramatically transformed the Web from a raw environment for information consumption to a dynamic and rich platform for information production and sharing worldwide. However, this growth and transformation of the Web has resulted in an uncontrollable explosion and abundance of the textual contents, creating a serious challenge for any user to find and retrieve the relevant information that he truly seeks to find on the Web. The process of finding a relevant Web page in a website easily and efficiently has become very difficult to achieve. This has created many challenges for researchers to develop new mining techniques in order to improve the user experience on the Web, as well as for organizations to understand the true informational interests and needs of their customers in order to improve their targeted services accordingly by providing the products, services and information that truly match the requirements of every online customer. With these challenges in mind, Web mining aims to extract hidden patterns and discover useful knowledge from Web page contents, Web hyperlinks, and Web usage logs. Based on the primary kinds of Web data used in the mining process, Web mining tasks can be categorized into three main types: Web content mining, which extracts knowledge from Web page contents using text mining techniques, Web structure mining, which extracts patterns from the hyperlinks that represent the structure of the website, and Web usage mining, which mines user's Web navigational patterns from Web server logs that record the Web page access made by every user, representing the interactional activities between the users and the Web pages in a website. The main goal of this thesis is to contribute toward addressing the challenges that have been resulted from the information explosion and overload on the Web, by proposing and developing novel Web mining-based approaches. Toward achieving this goal, the thesis presents, analyzes, and evaluates three major contributions. First, the development of an integrated Web structure and usage mining approach that recommends a collection of hyperlinks for the surfers of a website to be placed at the homepage of that website. Second, the development of an integrated Web content and usage mining approach to improve the understanding of the user's Web behavior and discover the user group interests in a website. Third, the development of a supervised classification model based on recent Social Web concepts, such as Tag Clouds, in order to improve the retrieval of relevant articles and posts from Web social blogs.

Drawing on the notion of faultlines – a hypothetical dividing line that splits a group into two or more subgroups based on the alignment of one or more individual attributes – this thesis proposes a new approach to the measurement and assessment of board diversity to understand how high(er) performing boards can be built i.e., the multi-dimensional diversity index (MDI). The proposed MDI captures the joint effect of differences in director attributes at four diversity levels for 26,743 directors, namely: (i) surface (or baseline); (ii) identity; (iii) demographic; and (iv) meso-level. The current study uses three-stage least squares (3SLS) with a panel of 3,357 FTSE All-Share index non-financial companies from 2005 to 2018. To this end, a key implication of this study – and by extension, the proposed MDI – is that it challenges the conventional notion that boards are improved ‘enough’ by focusing on the micro-dimension and increasing stand-alone diversity attributes, such as gender. Collectively, this study’s results suggest that a well-diversified board incentivises managers to disclose more information on social and environmental activities in contrast to firms with an extreme faultline score. The results show that highly effective boards with a moderate faultline score at meso-level diversity (e.g., identity, information, and non-demographic attributes) lead to better accounting profitability, corporate value, and market-based performance. Remarkably, the present study finds that nationality diversity per se positively impacts corporate performance; in contrast, the dominance of male directors hinders firm performance significantly.

No / Manuscript Type: Empirical Research Question/Issue: This paper examines the influence of corporate governance on risk disclosure practices in the UK and Italy and also studies the impact of those practices on market liquidity. Research Findings/Insights: We find that governance factors principally influence the decisions of UK (Italian) firms over whether to exhibit risk information voluntarily (mandatorily) in their annual report narratives. When we distinguish between firms with strong and weak governance (in terms of board efficiency) in each country, we find that the factors that affect mandatory and voluntary risk disclosure appear to be driven more by strongly governed firms in both countries. Furthermore, strongly governed firms in the UK tend to provide more meaningful risk information to their investors than weakly governed firms. In Italy, however, we find that strongly rather than weakly governed firms exhibiting risk information voluntarily rather than mandatorily improves market liquidity significantly. Theoretical/Academic Implications: This paper emphasizes the importance of distinguishing between mandatory and voluntary risk disclosure when studying the impact of corporate governance. Our findings differ across strongly and weakly governed firms, in terms of both the factors that influence risk disclosure practices and the exact informativeness of those practices. Practitioner/Policy Implications: The results support the current regulatory trend in risk reporting within the UK that emphasizes the importance of directors and encourages rather than mandates risk disclosure. However, the results generally signal a need for further improvements in the Italian context. Our evidence also supports the value of the confidence in the UK governance system, compared to that in Italy, which motivates British firms to provide highly informative risk information more often than Italian firms.

The full text will be available at the end of the embargo period: 31st December 2026.