Abstract
Personalized recommender systems improve access to relevant items and information by making personalized suggestions based on previous items of an individual user’s likes and dislikes. Most recommender systems use collaborative filtering or content-based filtering to predict new items of interest for a user. This approach has the advantage of being able to recommend previously un-rated items to users with unique interests and to provide explanations for personalized recommendations. We describe a personalized movie recommender system that utilizes WebBot, hybrid 2-way filtering, and a machine-learning algorithm for web page and movie poster’s extraction. And we validate our personalized movie recommender system through hybrid 2-way filtering with extracted information in on-line experiments.
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Jung, KY., Park, DH., Lee, JH. (2004). Personalized Movie Recommender System through Hybrid 2-Way Filtering with Extracted Information. In: Christiansen, H., Hacid, MS., Andreasen, T., Larsen, H.L. (eds) Flexible Query Answering Systems. FQAS 2004. Lecture Notes in Computer Science(), vol 3055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25957-2_37
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DOI: https://doi.org/10.1007/978-3-540-25957-2_37
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