Information Retrieval and User-Centric Recommender System Evaluation

Traditional recommender system evaluation focuses on raising the accuracy, or lowering the rating prediction error of the recommendation algorithm. Recently, however, discrepancies between commonly used metrics (e.g. precision, recall, root-mean-square error) and the experienced quality from the users' have been brought to light. This project aims to address these discrepancies by attempting to develop novel means of recommender systems evaluation which encompasses qualities identified through traditional evaluation metrics and user-centric factors, e.g. diversity, serendipity, novelty, etc., as well as bringing further insights in the topic by analyzing and translating the problem of evaluation from an Information Retrieval perspective

Similarity and Diversity in Information Retrieval

Inter-document similarity is used for clustering, classification, and other purposes within information retrieval. In this thesis, we investigate several aspects of document similarity. In particular, we investigate the quality of several measures of inter-document similarity, providing a framework suitable for measuring and comparing the effectiveness of inter-document similarity measures. We also explore areas of research related to novelty and diversity in information retrieval. The goal of diversity and novelty is to be able to satisfy as many users as possible while simultaneously minimizing or eliminating duplicate and redundant information from search results. In order to evaluate the effectiveness of diversity-aware retrieval functions, user query logs and other information captured from user interactions with commercial search engines are mined and analyzed in order to uncover various informational aspects underlying queries, which are known as subtopics. We investigate the suitability of implicit associations between document content as an alternative to subtopic mining. We also explore subtopic mining from document anchor text and anchor links. In addition, we investigate the suitability of inter-document similarity as a measure for diversity-aware retrieval models, with the aim of using measured inter-document similarity as a replacement for diversity-aware evaluation models that rely on subtopic mining. Finally, we investigate the suitability and application of document similarity for requirements traceability. We present a fast algorithm that uncovers associations between various versions of frequently edited documents, even in the face of substantial changes

Application and evaluation of multi-dimensional diversity

Traditional information retrieval (IR) systems mostly focus on finding documents relevant to queries without considering other documents in the search results. This approach works quite well in general cases; however, this also means that the set of returned documents in a result list can be very similar to each other. This can be an undesired system property from a user's perspective. The creation of IR systems that support the search result diversification present many challenges, indeed current evaluation measures and methodologies are still unclear with regards to specific search domains and dimensions of diversity. In this paper, we highlight various issues in relation to image search diversification for the ImageClef 2009 collection and tasks. Furthermore, we discuss the problem of defining clusters/subtopics by mixing diversity dimensions regardless of which dimension is important in relation to information need or circumstances. We also introduce possible applications and evaluation metrics for diversity based retrieval

Creating a test collection to evaluate diversity in image retrieval

This paper describes the adaptation of an existing test collection for image retrieval to enable diversity in the results set to be measured. Previous research has shown that a more diverse set of results often satisfies the needs of more users better than standard document rankings. To enable diversity to be quantified, it is necessary to classify images relevant to a given theme to one or more sub-topics or clusters. We describe the challenges in building (as far as we are aware) the first test collection for evaluating diversity in image retrieval. This includes selecting appropriate topics, creating sub-topics, and quantifying the overall effectiveness of a retrieval system. A total of 39 topics were augmented for cluster-based relevance and we also provide an initial analysis of assessor agreement for grouping relevant images into sub-topics or clusters

Learning to Diversify Web Search Results with a Document Repulsion Model

Search diversification (also called diversity search), is an important approach to tackling the query ambiguity problem in information retrieval. It aims to diversify the search results that are originally ranked according to their probabilities of relevance to a given query, by re-ranking them to cover as many as possible different aspects (or subtopics) of the query. Most existing diversity search models heuristically balance the relevance ranking and the diversity ranking, yet lacking an efficient learning mechanism to reach an optimized parameter setting. To address this problem, we propose a learning-to-diversify approach which can directly optimize the search diversification performance (in term of any effectiveness metric). We first extend the ranking function of a widely used learning-to-rank framework, i.e., LambdaMART, so that the extended ranking function can correlate relevance and diversity indicators. Furthermore, we develop an effective learning algorithm, namely Document Repulsion Model (DRM), to train the ranking function based on a Document Repulsion Theory (DRT). DRT assumes that two result documents covering similar query aspects (i.e., subtopics) should be mutually repulsive, for the purpose of search diversification. Accordingly, the proposed DRM exerts a repulsion force between each pair of similar documents in the learning process, and includes the diversity effectiveness metric to be optimized as part of the loss function. Although there have been existing learning based diversity search methods, they often involve an iterative sequential selection process in the ranking process, which is computationally complex and time consuming for training, while our proposed learning strategy can largely reduce the time cost. Extensive experiments are conducted on the TREC diversity track data (2009, 2010 and 2011). The results demonstrate that our model significantly outperforms a number of baselines in terms of effectiveness and robustness. Further, an efficiency analysis shows that the proposed DRM has a lower computational complexity than the state of the art learning-to-diversify methods