A decent movie summary is helpful for movie producer to promote the movie as well as audience to capture the theme of the movie before watching the whole movie. Most exiting automatic movie summarization approaches heavily rely on video content only, which may not deliver ideal result due to the semantic gap between computer calculated low-level features and human used high-level understanding. In this paper, we incorporate script into movie analysis and propose a novel character-based movie summarization approach, which is validated by modern film theory that what actually catches audiences' attention is the character [6]. We first segment scenes in the movie by analysis and alignment of script and movie. Then we conduct substory discovery and content attention analysis based on the scene analysis and character interaction features. Given obtained movie structure and content attention value, we calculate movie attraction scores at both shot and scene levels and adopt this as criterion to generate movie summary. The promising experimental results demonstrate that character analysis is effective for movie summarization and movie content understanding.

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While on the go, people are using their phones as a personal concierge discovering what is around and deciding what to do. Mobile phone has become a recommendation terminal customized for individuals. While existing research predominantly focuses on one-step recommendation-recommending the next single activity according to current context, this work moves one step beyond by recommending a series of activities, which is a package of sequential Points of Interest (POIs). The recommended POIs are not only relevant to user context (i.e., current location, time, and check-in), but also personalized to his/her check-in history. We presents a probabilistic approach, which is highly motivated from a large-scale commercial mobile check-in data analysis, to ranking a list of sequential POI categories (e.g., "Japanese food" and "bar") and POIs (e.g., "I love sushi"). The approach enables users to plan consecutive activities on the move. Specifically, the probabilistic recommendation approach estimates the transition probability from one POI to another, conditioned on current context and check-in history in a Markov chain. To alleviate the discritization error and sparsity problem, we further introduce context collaboration and integrate prior information. Experiments on over 100k real-world check-in records and 20k POIs validate the effectiveness of the proposed approach. (a) user info and context (b) rank of sequential POI categories (c) rank of sequential POIs 1 2 3 4 6 5 Package #1 Package #2 Figure 1: The interface of sequential POI recommendation application developed based on the proposed approach. A user named "Emily" checks in a shopping mall at the context of (a), and gets recommendation of (b) sequential POI categories (e.g., cafe→restaurant) and (c) the corresponding packages of sequential POIs when selecting a specific POI category sequence in (b). The recommendation of sequential POI categories and POIs rather a single POI is a metaphor of "plan activities in real life" and thus more natural to mobile users. Users will have much less interactions with the phone to complete their tasks.

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The ever growing number of videos on YouTube makes recommendation an important way to help users explore interesting videos. Similar to general recommender systems, YouTube video recommendation suffers from typical problems like new user, cold-start, data sparsity, etc. In this paper, we propose a unified YouTube video recommendation solution via cross-network collaboration: users' auxiliary information on Twitter are exploited to address the typical problems in single network-based recommendation solutions. The proposed two-stage solution first transfers user preferences from auxiliary network by learning cross-network behavior correlations, and then integrates the transferred preferences with the observed behaviors on target network in an adaptive fashion. Experimental results show that the proposed cross-network collaborative solution achieves superior performance not only in term of accuracy, but also in improving the diversity and novelty of the recommended videos.