Abstract
We propose an approach to adapt the existing item-based (movie-based) collaborative filtering algorithm based on the timestamp of ratings to recommend movies to users at opportune moments. Over the last few years, researchers focused recommendation problems on rating scores mostly. They analyzed users’ previous rating scores and predicted those unknown rating scores. However, we found rating scores are not the only problem we have to concern about. When to recommend movies to users is also important for a recommender system since users’ shopping habits vary from person to person. To recommend movies to users at opportune moments, we analyzed the rating distribution of each movie by the timestamps and found a user tending to watch similar movies at similar moments. Several experiments have been conducted on MovieLens Data Sets. The system is evaluated by different recommendation lists during a specific period of time - tspecific, and the experimental results show the usefulness of our system.
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Su, CC., Cheng, PJ. (2011). Recommend at Opportune Moments. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds) Information Retrieval Technology. AIRS 2011. Lecture Notes in Computer Science, vol 7097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25631-8_21
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DOI: https://doi.org/10.1007/978-3-642-25631-8_21
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