Recommend at Opportune Moments
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 99 === We propose an approach to adapt the existing item-based (movie-based) collaborative filtering algorithm based on the timestamps of the ratings to recommend movies to users at opportune moments. Over the last few years, researchers focused recommendation problems...
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| Format: | Others |
| Language: | en_US |
| Published: |
2011
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| Online Access: | http://ndltd.ncl.edu.tw/handle/98514988624337623640 |
| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 99 === We propose an approach to adapt the existing item-based (movie-based) collaborative filtering algorithm based on the timestamps of the 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 that a user tends 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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