  Web recommendation services bear great importance in e-commerce, as they aid
the user in navigating through the items that are most relevant to her needs.
In a typical Web site, long history of previous activities or purchases by the
user is rarely available. Hence in most cases, recommenders propose items that
are similar to the most recent ones viewed in the current user session. The
corresponding task is called session based item-to-item recommendation. For
frequent items, it is easy to present item-to-item recommendations by "people
who viewed this, also viewed" lists. However, most of the items belong to the
long tail, where previous actions are sparsely available. Another difficulty is
the so-called cold start problem, when the item has recently appeared and had
no time yet to accumulate sufficient number of transactions. In order to
recommend a next item in a session in sparse or cold start situations, we also
have to incorporate item similarity models. In this paper we describe a
probabilistic similarity model based on Random Fields to approximate
item-to-item transition probabilities. We give a generative model for the item
interactions based on arbitrary distance measures over the items including
explicit, implicit ratings and external metadata. The model may change in time
to fit better recent events and recommend the next item based on the updated
Fisher Information. Our new model outperforms both simple similarity baseline
methods and recent item-to-item recommenders, under several different
performance metrics and publicly available data sets. We reach significant
gains in particular for recommending a new item following a rare item.
