presented a per-query algorithmic approach that leverages fundamental retrieval principles such as pseudo-feedback-based relevance modeling. 24 That is, the weak ranker observes the documents most highly ranked by the strong ranker, uses them to induce a model of relevance for the query, and uses this model for ranking. We demonstrated the merits of our approach in improving the search effectiveness of the weak ranker using TREC datae— a suite of datasets used for evaluation of retrieval methods.

Strategic Users

The previous sections emphasized the need to account for the incentives of strategic parties when devising major data science applications such as search (ranking), recommendation, and prediction (specifically, regression). The strategic parties were associated with stakeholders such as content providers or prediction experts. The users of the systems were not strategic; they were the ones posting a query, asking for recommendation, or interested in prediction. In a sense, the users were the products the strategic parties were aiming to attract or to serve better than their competitors. However, users may have their own incentives that might cause them to potentially not follow recommendations by the system or to provide dishonest inputs to the system. Next, we briefly describe one of our recent works that addresses such aspects.

Incentive-compatible explore and
exploit. Recommendation systems
based on the interleaving of explora-
tion and exploitation paradigm have
a two-way relationship with their
customers—on the one hand they
provide recommendations while on
the other hand they use customers as
their source of information. This re-
ality leads to an important challenge:
a user might not accept a recommen-
dation if he/she believes the recom-
mendation is done to benefit explo-
ration rather than be the optimal
one given current information. The
challenge is to devise a system that
will behave close to optimal, but will
be also incentive compatible, that is,
e https://trec.nist.gov/
We have recently addressed this
challenge in designing recommenda-
tion systems, specifically for social
networks, where users can (partly)
observe each other. 1,f In particular, we
investigated the conditions on the so-
cial network that allow for asymptoti-
cally optimal outcomes. Our results
show that for reasonable networks,
where each user can observe many of
the other users, but where still most
users cannot see most of the other
users, an incentive compatible and
approximately optimal recommenda-
tion system does exist.

The literature on incentivizing exploration relevant to our study can be viewed as an extension of the celebrated multi-armed bandit problems8 to deal with settings where exploration can only be done by self motivated myopic agents and a central planner must incentivize exploration. The tension between the objective of the mediator (the recommendation engine) and the individual agents in a multi-armed bandit context was first introduced in Kremer et al., 22 who study this in a very simple setting. They identify an incentive compatible scheme with which a central mediator with commitment power can asymptotically steer the users toward taking the optimal action. This exciting news has been extended in Mansour et al. 26, 27 to several more elaborate bandit settings and to additional optimization criteria such as regret minimization. Whereas both of these papers account for agents’ incentives and in particular the misalignment of incentives of the agents and the mediator they ignore other societal aspects. In particular, these papers make an implicit assumption that agents cannot see nor communicate with any other agent. Needless to say, the assumption that agents have no knowledge of each other, and cannot see the actions chosen by their neighbors, is unrealistic. Che and Horner9 also study mechanisms for social learning through a mediator. Similar to Kremer et al., 22 they assume agents are short lived and do not observe each other and the mechanism f The pioneering work on this challenge assumed no user communication. 9. 22

A recommender
system should be
able to deal in
a fair way with
strategic content
providers,
each of which
aims at maximizing
its exposure.