Markov Determinantal Point Processes

TL;DR

This paper introduces Markov Determinantal Point Processes (M-DPPs), a novel sequential model that ensures diversity within and across time in subset selection, with applications in news article recommendation.

Contribution

The paper proposes a new Markov DPP model that maintains diversity over time and provides an efficient sampling and learning method for sequential subset selection.

Findings

M-DPP maintains DPP margins over time.

Efficient sampling procedure for M-DPP.

Application to news article recommendation.

Abstract

A determinantal point process (DPP) is a random process useful for modeling the combinatorial problem of subset selection. In particular, DPPs encourage a random subset Y to contain a diverse set of items selected from a base set Y. For example, we might use a DPP to display a set of news headlines that are relevant to a user's interests while covering a variety of topics. Suppose, however, that we are asked to sequentially select multiple diverse sets of items, for example, displaying new headlines day-by-day. We might want these sets to be diverse not just individually but also through time, offering headlines today that are unlike the ones shown yesterday. In this paper, we construct a Markov DPP (M-DPP) that models a sequence of random sets {Yt}. The proposed M-DPP defines a stationary process that maintains DPP margins. Crucially, the induced union process Zt = Yt u Yt-1 is also marginally DPP-distributed. Jointly, these properties imply that the sequence of random sets are encouraged to be diverse both at a given time step as well as across time steps. We describe an exact, efficient sampling procedure, and a method for incrementally learning a quality measure over items in the base set Y based on external preferences. We apply the M-DPP to the task of sequentially displaying diverse and relevant news articles to a user with topic preferences.