I advise taking a lot in some of the related papers: A) Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, Jaime G. Carbonell, Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization. In Proceedings of SIAM Data Mining, 2010. B) Yehuda Koren. Collaborative Filtering with Temporal Dynamics. http://research.yahoo.com/files/kdd-fp074-koren.pdf C) Yahoo! Music Recommendations: Modeling Music Ratings with Temporal Dynamics and Item Taxonomy. Gideon Dror, Noam Koenigstein and Yehuda Koren ACM Conference on Recommender Systems (RecSys), 2011
All of the above papers bin ratings into time slots, and have the flexibility to support temporal effects. In other words, the linear model can learn availability of items per time bins and not recommend items that do not exist in a certain time. (I assume that item availability can be mapped to discrete time bins). Hope this helps, DB > > Hi, > My team is working on building a recommendation system to recommend items for > the following use cases:1. Based on User similarity (using > org.apache.mahout.cf.taste.hadoop.item.RecommenderJob as the Base)2. Based on > item similarity > The part where it gets tricky is that we have a temporal restriction on our > items (they are valid only for x days). So in the ideal case, the recommender > should/can use the rating information on all our historical items, but will > never recommend any items that are not temporally available. Based on the > historical rating information, we need the list of best matches from the > temporally available items. > Apart from ideas that involve any pre/post processing activities to filter > temporally invalid item recommendations, we were reaching out to find if > somebody out here has ever dealt with a similar requirement and has found an > easier solution to deal with this edge case. > Any piece of advice, word of caution or streak of brilliance is more than > welcome. > Thanks in advance. > Bala
