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In recent years, the scientific and technological developments have led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user's personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context, we classify and review personalized video summary techniques based on the type of personalized summary, on the criteria, on the video domain, on the source of information, on the time of summarization, and on the machine learning technique. Depending on the type of methodology used by the personalized video summarization techniques for the summary production process, we classify the techniques into five major categories, which are feature-based video summarization, keyframe selection, shot selection-based approach, video summarization using trajectory analysis, and personalized video summarization using clustering. We also compare personalized video summarization methods and present 37 datasets used to evaluate personalized video summarization methods. Finally, we analyze opportunities and challenges in the field and suggest innovative research lines. [ABSTRACT FROM AUTHOR]

In the era of internet access, recommender systems try to alleviate the difficulty consumers face while trying to find items (e.g. services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of content-based recommender systems, and not only in one part of it. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a content-based recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of content-based recommender systems according to their ability to efficiently handle well-known drawbacks. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel hybrid recommender system for the tourism domain, which combines (a) a Bayesian preferences elicitation component which operates by asking the user to rate generic images (corresponding to generic types of POIs) in order to build a user model and (b) a novel content-based (CB) recommendations component. The second component can in fact itself be considered a hybrid among two different CB algorithms, each exploiting one of two semantic similarity measures: a hierarchy-based and a non-hierarchy based one. The latter is the recently introduced Weighted Extended Jaccard Similarity (WEJS). We note that WEJS is employed for the first time within a recommender algorithm. We incorporate our algorithm within a real, already available at Google Play, tour-planning mobile application for short-term visitors of the popular touristic destination of Agios Nikolaos, Crete, Greece, and evaluate our approach via extensive simulations conducted on a real-world dataset constructed for the needs of the aforementioned mobile application. Our experiments verify that our algorithms result in effective personalized recommendations of touristic points of interest, while our final hybrid algorithm outperforms our exclusively content-based recommender algorithms in terms of recommendations accuracy. Specifically, when comparing the performance of several hybrid recommender system variants, we are able to come up with a "winner": the most preferable variant of our hybrid recommender algorithm is one using a 〈four elicitation slates, six shown images per slate〉 pair as input to its Bayesian elicitation component. This variant combines increased precision performance with a lightweight preferences elicitation process. [ABSTRACT FROM AUTHOR]

In the era of internet access, recommender systems try to alleviate the difficulty that consumers face while trying to find items (e.g., services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of collaborative filtering recommend systems. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a collaborative filtering recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of collaborative filtering recommender systems according to their ability to efficiently handle well-known drawbacks. [ABSTRACT FROM AUTHOR]

We propose a Dual Hybrid Recommender System based on SCoR, the Synthetic Coordinate Recommendation system, and the Random Forest method. By combining user ratings and user/item features, SCoR is initially employed to provide a recommendation which is fed into the Random Forest. The two systems are initially combined by splitting the training set into two "equivalent" parts, one of which is used to train SCoR while the other is used to train the Random Forest. This initial approach does not exhibit good performance due to reduced training. The resulted drawback is alleviated by the proposed dual training system which, using an innovative splitting method, exploits the entire training set for SCoR and the Random Forest, resulting to two recommender systems that are subsequently efficiently combined. Experimental results demonstrate the high performance of the proposed system on the Movielens datasets. [ABSTRACT FROM AUTHOR]

Recommender systems try to predict the preferences of users for specific items, based on an analysis of previous consumer preferences. In this paper, we propose SCoR, a Synthetic Coordinate based Recommendation system which is shown to outperform the most popular algorithmic techniques in the field, approaches like matrix factorization and collaborative filtering. SCoR assigns synthetic coordinates to nodes (users and items), so that the distance between a user and an item provides an accurate prediction of the user’s preference for that item. The proposed framework has several benefits. It is parameter free, thus requiring no fine tuning to achieve high performance, and is more resistance to the cold-start problem compared to other algorithms. Furthermore, it provides important annotations of the dataset, such as the physical detection of users and items with common and unique characteristics as well as the identification of outliers. SCoR is compared against nine other state-of-the-art recommender systems, sever of them based on the well known matrix factorization and two on collaborative filtering. The comparison is performed against four real datasets, including a brief version of the dataset used in the well known Netflix challenge. The extensive experiments prove that SCoR outperforms previous techniques while demonstrating its improved stability and high performance. [ABSTRACT FROM AUTHOR]

We propose a method to improve the prediction performance of recommender systems via a Dual (user anditem) Training Error based Correction approach (DTEC). The proposed method is applied to the Synthetic Coordinate Recommendation system (SCoR) (Papadakis et al., 2017) and to other Ithree state-of-the-art systems. Initially, a recommender system is used Ito provide recommendations for users and items. Subsequently, we introduce a second stage, after initial execution of the recommender system, that improves its predictions taking into account the error in the training set between users and items and their similarity. These corrections can be performed from both user and item viewpoints, and finally a dual system is proposed that efficiently combines both corrections. DTEC computes a model that makes zero the recommendation error in the training set, and then applies it on the test set to improve the rating predictions. The proposed DTEC approach is applicable Ito any model-based recommender system with positive training error, potentially increasing the accuracy of the recommendations. The experimental results demonstrate the efficiency and high performance of DTEC on four well-known, real-world datasets. • DTEC takes into account the error in the training set between users and items. • A dual system efficiently combines the user and item view point corrections. • Improvement of recommendations of the SCoR system. • Applicability of DTEC on other recommender systems to improve recommendations. • High performance results on four well-known, real world datasets. [ABSTRACT FROM AUTHOR]