Reference managers like Zotero or Mendeley allow researchers to categorize papers into hierarchical categories called collections. The User navigates through a listing of these collections when filing a new item. The retrieval time grows something like the logarithm of the number of collections; looking for a collection can quickly become a nuisance.

A top level view of a collection hierarchy

Fig 1. A top level list of collections in the Zotero reference manager

One way to reduce navigation time is to allow users to search collections by name. A complementary solution is to provide a view of the currently "hot" collections. The user may interact with a list of suggested collections, or receive relevant completions when typing into a collections search bar.

This raises a basic learning problem:

Let $K = K_1, \ \dots, \ K_m$ be the sequence of collections the user has visited (possibly augmented with visit times). Let $H_m$ be a set of $n$ collections the user is likely to visit next. How can we construct $H_m$?

A technique that does this this might exploit a few important features of this domain:

  • Project Clusters: Users jump between collections relevant to their current projects
  • Collection Aging: Users tend to focus on new collections, and forget older ones
  • Retrieval Cost: There's a tangible cost to the user (time, distraction) when navigating collections; this applies to the reduced view (the technique might keep $n$ as small as possible)

Two ideas so far

LIFO Cache

Reduce $K_{m-1}, K_{m-2},\ \dots$ into the first $n$ unique entries which do not match $K_m$.

This heuristic is very simple to implement and requires no learning. It encompasses clusters and aging given suitably large $n$. But with large $n$ it incurs a retrieval cost of its own.

Markov Chain/Hidden Markov Model

Use $K$ to train a MC or HMM. Build $H_m$ using estimates from this model.

The simplest version is an order $k\ $ MC transition matrix built using k-gram statistics of $K_i$. This might be sensitive to project clusters, but I don't think it will recognize new collections without a hard coded aging heuristic.

I'm not clear on how HMMs would be trained here, and I'm not very taken with the $k$-gram MC approach. My next task is to read about MCs/HMMs in context of suggestion systems.

Other Models

I am brand new to suggestion systems. Reading leads are quite welcome!

I would be especially excited about unsupervised techniques, and neural network techniques I could train on a GPU. Apart from improving Zotero, I would like this problem to give me an opportunity to learn about cutting edge techniques.

Valuable Goals

An ideal technique would cast light on questions like

  1. How should we measure the performance of this kind of system? (I suspect cache miss rate is a good metric, as well as the ratio between cache miss rate and cache size)
  2. How should we translate these human-centric performance metrics into human independent objective functions for learning?
  3. How much better than LIFO can we theoretically expect to do with a more sophisticated technique (say, in terms of cache size for a given cache miss rate)?
  4. How can a technique learn patterns like clusters and aging without hand tuned objectives?

I am interested in learning theory and building an implementation, so resources with publicly available code would be be preferable. Apart from potentially being overkill for the problem, I would not mind if the final model depends on a GPU.

Please forgive me for the long and vague question. I wish I had done more reading before posing this question, but I feel a bit stuck! I hope to get unstuck with some good reading resources. Thanks!



You must log in to answer this question.

Browse other questions tagged .