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From Danny Bickson <>
Subject Re: Recommending items with temporal restrictions
Date Sat, 20 Aug 2011 09:30:14 GMT
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.
C) Yahoo! Music Recommendations: Modeling Music Ratings with Temporal
Dynamics and Item Taxonomy. Gideon Dror, Noam Koenigstein and Yehuda
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,


> Hi,
> My team is working on building a recommendation system to recommend items for the following
use cases:1. Based on User similarity (using
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

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