A methodology for contextual recommendation using artificial neural networks

“A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy”.Recommender systems are an advanced form of software applications, more specifically decision-support systems, that efficiently assist the users in finding items of their interest. Recommender systems have been applied to many domains from music to e-commerce, movies to software services delivery and tourism to news by exploiting available information to predict and provide recommendations to end user. The suggestions generated by recommender systems tend to narrow down the list of items which a user may overlook due to the huge variety of similar items or users’ lack of experience in the particular domain of interest. While the performance of traditional recommender systems, which rely on relatively simpler information such as content and users’ filters, is widely accepted, their predictive capability perfomrs poorly when local context of the user and situated actions have significant role in the final decision. Therefore, acceptance and incorporation of context of the user as a significant feature and development of recommender systems utilising the premise becomes an active area of research requiring further investigation of the underlying algorithms and methodology. This thesis focuses on categorisation of contextual and non-contextual features within the domain of context-aware recommender system and their respective evaluation. Further, application of the Multilayer Perceptron Model (MLP) for generating predictions and ratings from the contextual and non-contextual features for contextual recommendations is presented with support from relevant literature and empirical evaluation. An evaluation of specifically employing artificial neural networks (ANNs) in the proposed methodology is also presented. The work emphasizes on both algorithms and methodology with three points of consideration:\ud contextual features and ratings of particular items/movies are exploited in several representations to improve the accuracy of recommendation process using artificial neural networks (ANNs), context features are combined with user-features to further improve the accuracy of a context-aware recommender system and lastly, a combination of the item/movie features are investigated within the recommendation process. The proposed approach is evaluated on the LDOS-CoMoDa dataset and the results are compared with state-of-the-art approaches from relevant published literature

Exploring Strategies to Prevent Harm from Web Search

Web search, the process of seeking and finding information online, is an ubiquitous activity engrained in the lives of many individuals and much of broader society. This activity, which has brought many benefits to individuals and society, has also opened the door to many harms, such as echo chambers, loss of privacy and exposure to misinformation. Members of the information retrieval (IR) community now recognize the dangers of the search technologies commonplace in our daily lives. The upshot of this recognition are growing efforts to address these dangers by the IR community. These efforts focus heavily on system oriented solutions, but give limited focus on behavioural and cognitive biases and behaviours of the search and even less attention to interventions designed to address these biases and behaviours. As such, a theoretical framework is proposed, with behavioural and cognitive strategies as a core component of interactive Web search environments designed to minimize harm. Using the framework as the foundation, this thesis presents a number of offline and online studies to evaluate nudging, a popular intervention strategy rooted in the field of behavioural economics, and boosting, a successful intervention strategy from the cognitive sciences, as strategies to reduce risk of harm in Web search. Overall the studies produce findings in line with the theories underlying the behavioural and cognitive strategies considered. The key takeaway from these studies being that both boosting and nudging should be considered as viable approaches for harm prevention in Web search environments, in addition to pure system and algorithmic solutions. Additional contributions of this thesis include methods of study design for the comparison of multiple paradigms that promote improved decision making, along with a set of evaluation metrics to measure the success of the IR system and user performance as they relate to the harms being prevented. Future research is needed to confirm the effectiveness of these strategies for other types of harms