Thanks Lance, For your golden words " Algorithms are seductive to programmers". Can't tell you how many papers we have read to find out if we "fit into" any of them.
In response to your question, Yes, we do maintain a history of who (users) likes what (items) in the form of their ratings. That would go in as input to the algorithm to make the recommendation. That was what I meant when I wrote "Based on the historical rating information, we need the list of best matches from the temporally available items". So we are looking to use the historical "taste" data to make recommendations on the new items. Bala > Date: Sat, 20 Aug 2011 17:39:57 -0700 > Subject: Re: Recommending items with temporal restrictions > From: [email protected] > To: [email protected] > > Do you keep history of user & item actions? Even if an item is old, it > has a correlation with users who are still active. User A,B,C buy X. X > goes out of date. Users A,B,C buy Y. Y goes out of date. Users A,B buy > Z. Should you recommend Z to C? > > Algorithms are seductive to programmers. Start simple, understand your > data and use a feedback loop to watch your responses. After this, you > can pick&choose algorithms based on what makes sense in your context. > > On Sat, Aug 20, 2011 at 2:30 AM, Danny Bickson <[email protected]> > wrote: > > 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 > > > > > > -- > Lance Norskog > [email protected]
