GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying early buyers from purchase data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Introduction to recommender systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
EigenRank: a ranking-oriented approach to collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Tutorial on recent progress in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Ordering innovators and laggards for product categorization and recommendation
Proceedings of the third ACM conference on Recommender systems
Serendipitous recommendations via innovators
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Hi-index | 0.00 |
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.