Exploring the Semantic Gap for Movie Recommendations

In the last years, there has been much attention given to the semantic gap problem in multimedia retrieval systems. Much effort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from multimedia content, as low-level features are not considered useful because they deal primarily with representing the perceived content rather than the semantics of it. In this paper, we explore a different point of view by leveraging the gap between low-level and high-level features. We experiment with a recent approach for movie recommendation that extract low-level Mise-en-Scéne features from multimedia content and combine it with high-level features provided by the wisdom of the crowd. To this end, we first performed an offline performance assessment by implementing a pure content-based recommender system with three different versions of the same algorithm, respectively based on (i) conventional movie attributes, (ii) mise-en-scene features, and (iii) a hybrid method that interleaves recommendations based on movie attributes and mise-en-scene features. In a second study, we designed an empirical study involving 100 subjects and collected data regarding the quality perceived by the users. Results from both studies show that the introduction of mise-en-scéne features in conjunction with traditional movie attributes improves both offline and online quality of recommendations

Personalized Video Recommendation Using Rich Contents from Videos

Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods

Analyzing Nico Nico Douga Videos by Using Users’ Interests and the Distribution of Comments

Nico Nico Douga (NND) is one of the most famous social media platforms for sharing videos in Japan. There are 18 million videos posted to NND, for which users search by using keyword search and related video recommendation. However, it is difficult for the users to find interesting videos because videos generally are associated with only short texts and a few tags. In this paper, we present a method for analyzing videos in NND by using the distribution of time-synchronized comments. Our method regards a video as the set of the users’ comments on it and enables the clustering of videos based on the users’ shared interests. In our experiment, we applied the proposed method to videos posted to NND, and evaluated our method by quantitatively comparing it with existing text-based methods and by qualitatively performing the subjective evaluation of clustering results. In the result of the quantitative evaluation, the proposed method showed a higher score of normalized mutual information than the existing methods when category metadata were used as correct results. The experimental results of the qualitative evaluation showed that the proposed method was as good as or better than the existing text-based and image-based methods