Database technology and the management of multimedia data in Mirror

Multimedia digital libraries require an open distributed architecture instead of a monolithic database system. In the Mirror project, we use the Monet extensible database kernel to manage different representations of multimedia objects. To maintain independence between content, meta-data, and the creation of meta-data, we allow distribution of data and operations using CORBA. This open architecture introduces new problems for data access. From an end user’s perspective, the problem is how to search the available representations to fulfill an actual information need; the conceptual gap between human perceptual processes and the meta-data is too large. From a system’s perspective, several representations of the data may semantically overlap or be irrelevant. We address these problems with an iterative query process and active user participation through relevance feedback. A retrieval model based on inference networks assists the user with query formulation. The integration of this model into the database design has two advantages. First, the user can query both the logical and the content structure of multimedia objects. Second, the use of different data models in the logical and the physical database design provides data independence and allows algebraic query optimization. We illustrate query processing with a music retrieval application

Random performance differences between online recommender system algorithms

In the evaluation of recommender systems, the quality of recommendations made by a newly proposed algorithm is compared to the state-of-the-art, using a given quality measure and dataset. Validity of the evaluation depends on the assumption that the evaluation does not exhibit artefacts resulting from the process of collecting the dataset. The main difference between online and offline evaluation is that in the online setting, the user’s response to a recommendation is only observed once. We used the NewsREEL challenge to gain a deeper understanding of the implications of this difference for making comparisons between different recommender systems. The experiments aim to quantify the expected degree of variation in performance that cannot be attributed to differences between systems. We classify and discuss the non-algorithmic causes of performance differences observed

Multimedia retrieval using multiple examples

The paper presents a variant of our generative probabilistic multimedia retrieval model that is suitable for information needs expressed as multiple examples. Results have been evaluated on the TRECVID 2003 collection

Prior Information and the Determination of Event Spaces in Probabilistic Information Retrieval Models

A mismatch between different event spaces has been used to argue against rank equivalence of classic probabilistic models of information retrieval and language models. We question the effectiveness of this strategy and we argue that a convincing solution should be sought in a correct procedure to design adequate priors for probabilistic reasoning. Acknowledging our solution of the event space issue invites to rethink the relation between probabilistic models, statistics and logic in the context of IR

Increasing Cheat Robustness of Crowdsourcing Tasks

Crowdsourcing successfully strives to become a widely used means of collecting large-scale scientific corpora. Many research fields, including Information Retrieval, rely on this novel way of data acquisition. However, it seems to be undermined by a significant share of workers that are primarily interested in producing quick generic answers rather than correct ones in order to optimise their time-efficiency and, in turn, earn more money. Recently, we have seen numerous sophisticated schemes of identifying such workers. Those, however, often require additional resources or introduce artificial limitations to the task. In this work, we take a different approach by investigating means of a priori making crowdsourced tasks more resistant against cheaters

Distance matters! Cumulative proximity expansions for ranking documents

In the information retrieval process, functions that rank documents according to their estimated relevance to a query typically regard query terms as being independent. However, it is often the joint presence of query terms that is of interest to the user, which is overlooked when matching independent terms. One feature that can be used to express the relatedness of co-occurring terms is their proximity in text. In past research, models that are trained on the proximity information in a collection have performed better than models that are not estimated on data. We analyzed how co-occurring query terms can be used to estimate the relevance of documents based on their distance in text, which is used to extend a unigram ranking function with a proximity model that accumulates the scores of all occurring term combinations. This proximity model is more practical than existing models, since it does not require any co-occurrence statistics, it obviates the need to tune additional parameters, and has a retrieval speed close to competing models. We show that this approach is more robust than existing models, on both Web and newswire corpora, and on average performs equal or better than existing proximity models across collections