Glasgow University at TRECVID 2006

In the first part of this paper we describe our experiments in the automatic and interactive search tasks of TRECVID 2006. We submitted five fully automatic runs, including a text baseline, two runs based on visual features, and two runs that combine textual and visual features in a graph model. For the interactive search, we have implemented a new video search interface with relevance feedback facilities, based on both textual and visual features. The second part is concerned with our approach to the high-level feature extraction task, based on textual information extracted from speech recogniser and machine translation outputs. They were aligned with shots and associated with high-level feature references. A list of significant words was created for each feature, and it was in turn utilised for identification of a feature during the evaluation

Relation-aware collaborative autoencoder for personalized multiple facet selection

Collaborative-based personalization has been one of the most successful techniques used in building personalization for recommender systems and facet selection. The technique predicts users’ interests based on the preferences of similar people or items. The prediction is usually made on one single group of users or items/facets. However, multiple facet selection creates a different challenge where the prediction needs to be based on the similarity among different groups of users and facets. In conventional collaborative approach, user–facet representation is created from the concatenation of user preferences on each facet. This creates a spared representation which affects the accuracy of the personalized model. It is essential to develop a more suitable representation that effectively represents the collaborative preferences given across multiple facets and a predictive model to estimate the possible preferences across those groups. Multiple facets appear to be correlated to each other and this can be useful for associating the existing preferences. None of the previous works has addressed the issue due to the association of facet relationships. Hence, this paper aims to examine the effectiveness of a new approach that utilizes multiple-facet relationships to associate the collaborative interests across different facets. This study proposes a new collaborative-based personalization model for multiple facet selection, called Relation-aware Collaborative Autoencoder (RCAE) Model. A new embedding methodology was introduced for incorporating multiple facet relationships into user–facet interaction. Evaluations based on four real-world datasets demonstrated that the proposed model utilizing facet relationships has achieved significant improvement over the conventional collaborative approach

An exploration of user–facet interaction in collaborative-based personalized multiple facet selection

The huge amount of irrelevant and unimportant information have led to the need of using personalization in selecting the information which is relevant to searchers’ interest. Personalized faceted search has been a potential tool to support searchers to retrieve appropriate information effectively by navigating a list of selected multiple facets or categories based on the search results. To develop an effective personalized faceted search, the selection of relevant multiple facets is an important mechanism. Collaborative-based personalization was introduced for facet selection. Recently, Artificial Neural Network (ANN) has been reported that it performs better than other state-of-the-art Collaborative Filtering techniques for predicting single facet. However, analyzing the collaborative interests for multiple facets has not been studied. It is challenging if the interaction of the users on multiple facets is based on the information associated with the preferences of similar users over a group of multiple facets. This paper proposes an ANN-based facet predictive model that makes use of the collaborative-based personalization concept for multiple facet selection. The architecture of the proposed model is based on two suitable interaction schemes, the Early interaction and the Late interaction schemes. Based on experimental results, the performance was evaluated in terms of prediction accuracy and computation time. The results showed that the proposed model based on an effective interaction scheme obtained significant improvement on the prediction of personal interests on multiple facets

Collaborative filtering for personalised facet selection

An overwhelming number of facet values causes difficulties in providing an efficient search filter in dynamic facet search. It requires effort and time from the searchers to examine the list in order to select their interested facets. Personalised facet selection provides a list of relevant facet which is related to the user's interests. However, personalisation may not be possible to determine a user's current interest from the user's profile or the user's history search only. In some cases, due to insufficient information to identify users' current interests, the need of associating community opinions with personal interests is necessary. This study aims to investigate the incorporation of a collaborative approach to personalise facet selection. Collaborative Filtering is employed to address the issue of limited profile information and the approach has been widely used in recommender systems. Experiments were conducted on a benchmark Movie dataset using user ratings as the representation of user preferences and evaluated by rating prediction accuracy and computational time. The results show that Collaborative Filtering should improve the performance of personalised facet selection

Deep autoencoder on personalized facet selection

Information overloading leads to the need for an efficient search tool to eliminate a considerable amount of irrelevant or unimportant data and present the contents in an easy-browsing form. Personalized faceted search has been one of the potential tools to provide a hierarchical list of facets or categories that helps searchers to organize the information of the search results. Facet selection is one of the important steps to pursue a good faceted search. Collaborative-based personalization was introduced to facet selection. Previous studies have been performed on the use of Collaborative Filtering techniques for personalized facet selection. However, none of the study has investigated Artificial neural network techniques on personalized facet selection. Therefore, this study aims to investigate the possible use of deep Autoencoder on the prediction of facet interests. Autoencoder model was applied to address the association of collaborative interest in facets. The experiments were conducted on 100K and 1M rating records of Movielen dataset. Rating score was used to represent the explicit feedback on facet interests. The performance was reported by comparing the proposed technique and the state-of-the-art model-based Collaborative Filtering techniques in terms of prediction accuracy and computational time. The results showed that the proposed Autoencoder-based model achieved better performance and it was able to significantly improve the prediction of personal facet interests