A non-intrusive movie recommendation system

Several recommendation systems have been developed to support the user in choosing an interesting movie from multimedia repositories. The widely utilized collaborative-filtering systems focus on the analysis of user profiles or user ratings of the items. However, these systems decrease their performance at the start-up phase and due to privacy issues, when a user hides most of his personal data. On the other hand, content-based recommendation systems compare movie features to suggest similar multimedia contents; these systems are based on less invasive observations, however they find some difficulties to supply tailored suggestions. In this paper, we propose a plot-based recommendation system, which is based upon an evaluation of similarity among the plot of a video that was watched by the user and a large amount of plots that is stored in a movie database. Since it is independent from the number of user ratings, it is able to propose famous and beloved movies as well as old or unheard movies/programs that are still strongly related to the content of the video the user has watched. We experimented different methodologies to compare natural language descriptions of movies (plots) and evaluated the Latent Semantic Analysis (LSA) to be the superior one in supporting the selection of similar plots. In order to increase the efficiency of LSA, different models have been experimented and in the end, a recommendation system that is able to compare about two hundred thousands movie plots in less than a minute has been developed

Mind-body interventions for vasomotor symptoms in healthy menopausal women and breast cancer survivors. A systematic review

Mind–body therapies are commonly recommended to treat vasomotor symptoms, such as hot flushes and night sweats (HFNS). The purpose of this systematic review was to evaluate the available evidence to date for the efficacy of different mind–body therapies to alleviate HFNS in healthy menopausal women and breast cancer survivors. Randomized controlled trials (RCTs) were identified using seven electronic search engines, direct searches of specific journals and backwards searches through reference lists of related publications. Outcome measures included HFNS frequency and/or severity or self-reported problem rating at post-treatment. The methodological quality of all studies was systematically assessed using predefined criteria. Twenty-six RCTs met the inclusion criteria. Interventions included yoga (n = 5), hypnosis (n = 3), mindfulness (n = 2), relaxation (n = 7), paced breathing (n = 4), reflexology (n = 1) and cognitive behavioural therapy (CBT) (n = 4). Findings were consistent for the effectiveness of CBT and relaxation therapies for alleviating troublesome vasomotor symptoms. For the remaining interventions, although some trials indicated beneficial effects (within groups) at post-treatment and/or follow up, between group findings were mixed and overall, methodological differences across studies failed to provide convincing supporting evidence. Collectively, findings suggest that interventions that include breathing and relaxation techniques, as well as CBT, can be beneficial for alleviating vasomotor symptoms. Additional large, methodologically rigorous trials are needed to establish the efficacy of interventions on vasomotor symptoms, examine long-term outcomes and understand how they work

User Semantic Preferences for Collaborative Recommendations

International audiencePersonalized recommender systems provide relevant items to users from huge catalogue. Collaborative filtering (CF) and content-based (CB) filter- ing are the most widely used techniques in personalized recommender systems. CF uses only the user-rating data to make predictions, while CB filtering relies on semantic information of items for recommendation. In this paper we present a new approach taking into account the semantic information of items in a CF system. Many works have addressed this problem by proposing hybrid solu- tions. In this paper, we present another hybridization technique that predicts us- ers "preferences for items based on their inferred preferences for semantic in- formation. With this aim, we propose a new approach to build user semantic profile to model users‟ preferences for semantic information of items. Then, we use this model in a user-based CF algorithm to calculate the similarity between users. We apply our approach to real data, the MoviesLens dataset, and com- pare our results to standards user-based and item-based CF algorithms

Link Augmentation: A Context-Based Approach to Support Adaptive Hypermedia

In today’s adaptive hypermedia systems, adaptivity is provided based on accumulative data gained from observing the user. User modelling, the capturing of information about the user such as their knowledge, tasks, attitudes, interests etc., is only a small part of the global context in which the user is working. At Southampton University we have formed a model of one particular aspect of context that can be applied in different ways to the problem of linking in context. This paper describes how that context model has been used to provide link augmentation. Link augmentation is an existing open hypermedia technique, which has a direct application in adaptive hypermedia systems. This paper presents a technique for cross-domain adaptive navigational support by combining link augmentation with a model of the user’s spatial context

Content-based Dimensionality Reduction for Recommender Systems

Abstract. Recommender Systems are gaining widespread acceptance in e-commerce applications to confront the information overload problem. Collaborative Filtering (CF) is a successful recommendation technique, which is based on past ratings of users with similar preferences. In contrast, Content-based Filtering (CB) exploits information solely derived from document or item features (e.g. terms or attributes). CF has been combined with CB to improve the accuracy of recommendations. A major drawback in most of these hybrid approaches was that these two techniques were executed independently. In this paper, we construct a feature profile of a user based on both collaborative and content features. We apply Latent Semantic Indexing (LSI) to reveal the dominant features of a user. We provide recommendations according to this dimensionally-reduced feature profile. We perform experimental comparison of the proposed method against well-known CF, CB and hybrid algorithms. Our results show significant improvements in terms of providing accurate recommendations.