  We address the problem of active online assortment optimization problem with
preference feedback, which is a framework for modeling user choices and
subsetwise utility maximization. The framework is useful in various real-world
applications including ad placement, online retail, recommender systems,
fine-tuning language models, amongst many. The problem, although has been
studied in the past, lacks an intuitive and practical solution approach with
simultaneously efficient algorithm and optimal regret guarantee. E.g.,
popularly used assortment selection algorithms often require the presence of a
`strong reference' which is always included in the choice sets, further they
are also designed to offer the same assortments repeatedly until the reference
item gets selected -- all such requirements are quite unrealistic for practical
applications. In this paper, we designed efficient algorithms for the problem
of regret minimization in assortment selection with \emph{Plackett Luce} (PL)
based user choices. We designed a novel concentration guarantee for estimating
the score parameters of the PL model using `\emph{Pairwise Rank-Breaking}',
which builds the foundation of our proposed algorithms. Moreover, our methods
are practical, provably optimal, and devoid of the aforementioned limitations
of the existing methods. Empirical evaluations corroborate our findings and
outperform the existing baselines.
