a collaborative situation-aware scheme for mobile service recommendation
DESCRIPTION
Nowadays mobile devices, such as smartphones, are becoming increasingly popular and they offer a wide range of mobile applications, also called mobile apps, suitable for different situations. This abundance of applications makes the research over them difficult and time consuming. Context-aware resource recommendation for ubiquitous devices is aimed at proactively pushing personalized suggestions to mobile users, presenting them unseen mobile apps. Typically, the recommendation is based on recognizing the current situation of the user and suggesting them the appropriate resources for those situations. We believe that recommendation schemes can emerge from users’ collective behavior. An emergent behavior or emergent property can appear when a number of simple entities (agents) operate in an environment, forming more complex behaviors as a collective. In our case the entities are represented by mobile users who provide positional data through Global Positioning System (GPS) provided by almost all modern smartphones. The recognition task is performed by exploiting contextual information and preferably without using any explicit input from the user. Thus, in this thesis we present a collaborative multi-agent scheme for social events detection in which a stigmergic paradigm and fuzzy representations are employed to cope with the approximation typical of implicit and aggregated information. The multi-agent scheme is structured into three levels of information processing. The first level is based on a stigmergic paradigm, in which marking agents, following the mobile user, leave marks in the environment. The accumulation of such marks enables the second level, a fuzzy information granulation process, managed by event agents, in which relevant events can emerge. Finally, the third level, a fuzzy inference process, managed by situation agents deduces the user situation from the underlying events.TRANSCRIPT
A Collaborative Situation-Aware Scheme for
Mobile Service Recommendation
CandidatoLuigi Massa Gallerano
RelatoriFrancesco MarcelloniBeatrice LazzeriniMario Giovanni C.A. Cimino
Smartphone Market
SmartphoneMarketStrong
Rise
A new report (Juniper Research) forecasts that the number of global smartphone shipments will reach one billion per annum in 2016
Smartphone Apps
Number ofSmartphone
Apps
Official Google Blog: “10 billion apps downloaded and installed as of December 2011”
Apps for each situation
Mobile Recommendation
App Recommendation
Context
Situation-awareness
This Autonomous Perception implies:
ReasoningDecision AdaptationCognitive system Intrinsic uncertainty in data
No Explicit InputFuzzy Logic
Emergent Paradigm
Stigmergy
Multi-Agent Scheme
UserSituation
Situation-awareness
Control to achieve results is distributed over all entities
The collaborative Multi-agent scheme
Marking Level
Fuzzy GranulationLevel
Fuzzy Inference
Level
Marking Agents
Event Agents
Situation Agent
Marks
Event Certainty Degree
Fuzzy Rules
1°
2°
3°
EMERGES
UserSituation
Marking Level
Released every Tm seconds Intensity Spatial decreases (percentage δ per cell) Temporal decay (every Td sec of a percentage α) Superimpose
Movement Grid
Max Intensity User Still
Fuzzy Granulation Level
Grouping Agent
Disjoining Agent
Observes a Neighboring Area Calculates Intensity associated with the area
Computes Membership Function
Marking AgentMarking Agent
Marking Agent
Marking Agent
Fuzzy Inference Level
Collaboration Agent
Marking Agent
Marking Agent
Marking Agent
DiaryDiary
Diary
GroupingDegree
DisjoiningDegree
LastSituation
Fuzzy inference process
The Simulator
Agents representation
Parameters
Run Options
Testing Scenarios Scenario S1: 7 users Scenario S2: 4 users Scenario S3: 3 users
L = 50, 80 time steps
δ = 0.5, α = 0.1 Td = Tm = T = 60 sec
Conclusions and Future Works
Thanks for your attention
Scenario S1 – 7 Users
Ontological View
Repast 2.0
Framework for creating agent-based simulations using the Java language
Repast Engine Context Projections Agents Data Layers
Java Package: cr.agents
Class Diagram
Diary: Deterministic Finite Automaton
Membership Functions
Membership Functions
Fuzzy Sets – Grouping
LOW-MED
MED-HIGH
Fuzzy Sets – Disjoining
LOW-HIGH
Java Package: cr.core
Class Diagram
Sequence Diagram
Java Package: cr.services
Diffuse Service
Merge Service
Java Package: cr.fuzzy
Class Diagram
Sequence Diagram