Recommender Systems (RSs) are typically used to support users in nding web content of their interest. We consider here an alternative scenario: to support human decision making in the physical world. In particular, we focus on Internet of Things (IoT), where, for instance, users exploration of a sensor enabled city can be tracked and the knowledge of their choices (visit to points of interest, POIs) can be used to generate recommendations for not yet visited POIs. We leverage two distinct components: a generalised user behavioural model and a complementary recommender system; here recommendations can deviate from the usual approach to directly use the learned behaviour model and suggest the most likely actions the user will take next. We also propose techniques for simulating user behaviour and analysing the collective dynamics of a population of users. Moreover, we tackle the lack of data produced by interactions of users with IoT augmented areas, by designing a simulator that can be used to collect user preferences and monitor their decisions.
RSs are techniques and tools that support human decision making by identifying
items or actions relevant to a user [
We believe that these solutions tend to generate recommendations that lack
novelty and therefore tend to be not interesting for the user (especially the
rst one, since it su ers from the cold-start problem). We therefore propose a
recommendation technique that leverages a more general and explainable user
behavioural model, which is learnt by observing users that are \similar" to the
target one, and decouples behaviour learning from recommendations generation,
so that, recommendations can deviate from the strategy to recommend the most
likely next action of the user. We see this as a prerequisite to design novel
and more compelling recommendation strategies to the user. In order to learn
the generalised user behaviour (choice policy) we exploit the observation of the
choices of a population that acts in physical areas. Observations are acquired
via Internet of Things (IoT) technologies, i.e., sensors networks that can register
users' activities (e.g., movements) and environment conditions (e.g., weather)
[
In order to learn the generalised user behaviour, we have used Inverse
Reinforcement Learning (IRL)[
IRL is used by us to learn the sequential decision making behaviour of a group of users by leveraging observations of their actions sequences, i.e., without knowing the users' reward (e.g. rating feedback). For instance, in the POI visit scenario the observations are the temporally ordered attraction-visit actions. Moreover, IRL assumes that the user's reward (utility) for a state (e.g., visiting a POI) is a linear function r = #T ' (reward function), where ' is the state feature vector and # is the (unknown) user preference vector for the state features. State features can describe the location or the visit context of the user, e.g., the crowdedness level of the POI. From the reward function r, by assuming that a user chooses actions that maximise her reward, it can be derived the user (optimal) action-selection policy, i.e., the policy that given the current user's state tells her to act so that her expected utility is maximized.
IRL typically learns the user reward function from a set of observed user's actions sequences. Since in many cases user observed actions are few, and noisy, a better model of the user behaviour can be learned by grouping (e.g., clustering) similar users (e.g., the users sharing the same visit goals) and then by learning user behaviour from grouped observations (generalised user behaviour of the group).
We have implemented this approach to behaviour learning in two case studies
in the tourism domain: the rst one in an indoor environment (i.e., a museum)
[
After the generalised user behaviour model of a group (group action-selection policy) is learnt, action recommendations for a user can be generated by considering the observed sequence of her actions, as follows. If there are just a few observations (e.g., the sequence of actions performed by the user so far), then the generalised user behaviour (predictive model) of the group the user belongs to is used to suggest the optimal action that this user should do after the last visited POI. Conversely, in case there are more action observations for a user, recommendations can be generated by aggregating the group generalised user behaviour with an individual user preference model (built from the user observations). In practice, the two actions' rankings derived from the generalised user behaviour and the user preference model, are aggregated and the suggested action is the top one in this ranking. For instance, if a commuter is understood to like arts because he mostly visited museums (individual user preference model) and a close-by exhibition is estimated to be an optimal choice for group members, then the system could recommend it to her.
We are currently studying an approach to group users' actions sequences according to a common \semantic" structure that motivates the resulting bundles. Grouping is done by rst representing action sequences according their state features and then by performing clustering (i.e., topic modelling). Groups formed by following the described approach are matching distinguished alternative tourist types and their di erences can be further illustrated by the diverse reward functions that are learnt for the groups.
The proposed techniques are going to be implemented in a mobile application that exploits an IoT infrastructure to support tourists' decision making by enabling them to discover new places or shops in an Italian alpine valley.
As mentioned above, since testing of IoT solutions is expensive and suitable
datasets are missing, in order to bootstrap the application, we have developed
a simulation tool that allows individuals or groups to experience a simulated
itinerary and visit to POIs [
www.inf.unibz.it/ damassimo/video/IoT demo.mp4
Finally, we intend to use the learnt behavioural model in order to generate synthetic action sequences of a population under conditions of interest (e.g., the opening of a new road or of a new mall). In this way the global system dynamics and the recommender performance can be evaluated even in novel IoT con gurations, further reducing the testing costs.
The research described in this paper is supported by the project Suggesto Market Space, funded by the Autonomous Province of Trento with Ectrl Solutions and Fondazione Bruno Kessler.