Feature selection for recommendation of movies

Recommender system is one of the recent applications which help to recommend the items or products based on the user needs. Since the user needs varies from person to person we cannot generalize the recommender system. Movie recommender system also has the same issues since individuals have different expectations while watching a movie and recommendation is not possible based on the annotations given by the other users. To overcome this situation an affective recommender framework is proposed in this work. Using the objectivity of audio-visual descriptor, connotation provides a space to predict the emotional state of the viewers. By extracting the connotative attributes such as audio-visual descriptor and user emotional state the connotative space is created. Then the movies which are nearer to each other in the created connotative space are recommended. Finally, the ability of the framework is assessed by employing the subjective analysis by asking the users to verify the film contents which met their affective requests.

[1]  Riccardo Leonardi,et al.  Affective analysis on patterns of shot types in movies , 2011, 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA).

[2]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Rongrong Ji,et al.  Video indexing and recommendation based on affective analysis of viewers , 2011, MM '11.

[4]  Jane Labadin,et al.  Feature selection based on mutual information , 2015, 2015 9th International Conference on IT in Asia (CITA).

[5]  Riccardo Leonardi,et al.  Affective Recommendation of Movies Based on Selected Connotative Features , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  A. Hanjalic,et al.  Extracting moods from pictures and sounds: towards truly personalized TV , 2006, IEEE Signal Processing Magazine.

[7]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[8]  Ajay Divakaran,et al.  MPEG-7 visual motion descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[9]  Alan Hanjalic,et al.  Extracting Moods from Pictures and Sounds , 2006 .

[10]  Loong Fah Cheong,et al.  Affective understanding in film , 2006, IEEE Trans. Circuits Syst. Video Technol..

[11]  George Tzanetakis,et al.  Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..

[12]  Loong Fah Cheong,et al.  Taxonomy of Directing Semantics for Film Shot Classification , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Ioannis Arapakis,et al.  Theories, methods and current research on emotions in library and information science, information retrieval and human-computer interaction , 2011, Inf. Process. Manag..

[14]  Marko Tkalcic,et al.  Using affective parameters in a content-based recommender system for images , 2010, User Modeling and User-Adapted Interaction.

[15]  Kiyoharu Aizawa,et al.  Affective Audio-Visual Words and Latent Topic Driving Model for Realizing Movie Affective Scene Classification , 2010, IEEE Transactions on Multimedia.