Hi, This is not a question specific to Mahout library. I hope you'll be interested.
While recommending to a user, we take his ratings to items, or some implicit ratings like his purchase history, click history, etc. into account. Item based collaborative filtering techniques generally compute item-to-item similarities in a symmetrical way ( sim(item1,item2) = sim(item2,item1). This is the nature of a distance measure). What if we consider user's historical data as a sequence, and want to predict the successor item? For example, in an e-commerce domain, we may want to find the item to buy after buying some other items. For example, if we have a user vector u, where uti is the item that user was interested in time ti, what are the possible values of ucurrent? Considering active user's interest to items at a specific time as states, can we see predicting user's current interest as the unobserved state and the user data as an HMM? I do not know well HMM, do you think that point of view to the problem seems reasonable? Do you have any ideas/suggestions about other solutions if it is not a good way? -- Gökhan Çapan
