FINLAND http://www.cs.jyu.fi/ai/vagan. 2. Department of Artificial Intelligence. Kharkov National University of Radioelectronics. Lenin Avenue 14, 61166 Kharkov.
Bayesian Metanetworks for Mobile Web Content Personalization VAGAN TERZIYAN1, OLEKSANDRA VITKO2 1
Department of Mathematical Information Technology University of Jyvaskyla P.O. Box 35 (Agora), 40014 Jyvaskyla FINLAND http://www.cs.jyu.fi/ai/vagan 2
Department of Artificial Intelligence Kharkov National University of Radioelectronics Lenin Avenue 14, 61166 Kharkov UKRAINE http://www.cs.jyu.fi/ai/oleksandra Abstract: - The problem of profiling and filtering the Web content is important particularly for mobile applications where wireless network traffic and mobile terminal size are limited comparing to the Internet access from the PC. Bayesian networks are known to be good tool for learning user preferences in electronic commerce. However more sophisticated cases, when user preferences are changing according to a context or location changes, require also more sophisticated Bayesian architectures. In this paper Multilevel Bayesian belief networks were proposed for modeling of user preferences in the mobile information environment. Bayesian Metanetwork is a multilevel probabilistic model which can be used for prediction of mobile user’s preferences taking into account special requirements of mobile environment. In this paper we consider several reasonable modifications of Bayesian Metanetwork, appropriate reasoning rules with some discussion about possible implementation. Further research is expected to specify algorithms for learning Bayesian Metanetworks and to define application area more concrete. Keywords: - Probabilistic Reasoning, Bayesian Networks, Profiling, Personalization, Mobile Commerce
1 Introduction Filtering of the Web content is a topical problem in the age of information overload. The trend is to provide a personalized suggestion about items that users will find interesting. Information systems require a user interface that can "intelligently’’ determine the interest of a user to make suggestions. The promising application area for development of new filtering techniques is mobile information systems. The problem of profiling and filtering is urgent particularly for the mobile environment where network traffic and terminal space are limited comparing to the Internet access from the PC. In this paper Multilevel Bayesian belief networks were proposed for modeling of user preferences in mobile information environment. It can be used for prediction of mobile user’s preferences taking into account special requirements of mobile environment. The aim of this work is to present several architectures of Bayesian Metanetworks with appropriate reasoning rules and to discuss the use of such networks for prediction of user preferences in mobile environment.
Section 2 presents the basic ideas of profiling and filtering using probabilistic networks. In Section 3 we evaluate the application area - mobile information environment. In Section 4 we introduce the Bayesian Metanetwork model and give several possible variations of it. The main conclusions and discussions are drawn in Section 5.
2 Background and Related Work 2.1 Filtering and profiling of users These are the topical problems in the age of information overload. Customers want to get really adapted information instead of irrelevant information or even garbage sometimes. Information filtering is the task of splitting a large-volume data stream into substreams according to some selection criterion. We consider profiling just as a data mining process for efficient and automated construction of a presentation of user’s filtering preferences. Personal information that user provides on registration, his behaviour and history are combined to create user’s personal profile. The profile is then used to target certain products or services for a user.
Many filtering techniques have been developed in the last years. Generally filtering methods are divided into two main classes - content-based and collaborative filtering. In modern adaptive systems content-based and collaborative filtering are combined [3]. Dealing with probabilistic relationships when solving the problems of user clustering, profiling and human decision making is required in information systems.
held terminals make such Personal Trusted Devices (PTD) [11] a possible channel for offering personalized services to mobile users, and enables the rapid development of mobile electronic commerce (m-commerce). The emergence of five different types of m-commerce can be identified: banking, Internet e-commerce over wireless access networks, location-based services, ticketing applications, and retail shopping [10]. The public commerce (p-commerce) in the mobile environment was introduced in [9].
2.2 Use of probabilistic models in filtering and profiling The Bayesian network has proven to be a valuable tool for encoding, learning and reasoning about probabilistic relationships. It is useful for encoding causal relationships and can be easy constructed by the knowledge-based approach. A Bayesian network for a set of variables X = {X1, …, Xn} is a directed acyclic graph with a network structure S that encodes a set of conditional independence assertions about variables in X, and a set P of local probability distributions associated with each variable [5]. Bayesian networks as well as other probabilistic techniques are widely used for prediction of user preferences. Once learned, Bayesian belief networks can support any probabilistic inference task including prediction of user preferences. The foundation for use of Bayesian networks and Markov models for user profiling in the information retrieval was given in [12]. The idea of using probabilistic mixture models as a flexible framework for modelling of user's preferences has been known and used [1]. The works on probabilistic modelbased collaborative filtering introduce a graphical model for probabilistic relationships - an alternative to the Bayesian network - called a dependency network [6]. Kuenzer et. al. [7] present the empirical study of dynamic Bayesian Networks for user modelling by various Markov Models. As the new market for user profiling has appeared in the mobile communication systems, probabilistic models for modeling of user's preferences should take into account all the features of profiling in the mobile Internet.
3 Mobile Environment and Location Awareness Advances in wireless network technology and the continuously increasing number of users of hand
3.1 Features and Constraints of Mobile Environment The mobile environment imposes the set of constraints and requirements on the filtering technique: • restrictions in computational resources of portable device; • restrictions in time of connection (customer pays for every additional second of the connection); • limitations on size of mobile terminal.
3.2 Location-Based Services One of the most distinguishing features of mobile environment is mobility. The mobile network servers and even mobile terminals are able now to determine the position of the terminal precisely. This gives the basis for the new class of services called Location Based Services (LBS) [2]. Combining positional mechanisms with information about location of various objects can develop powerful and flexible personal information services. The aim of the location-aware service is providing a user with information about the objects taking into account spatial relationships between him and the objects. The attribute "location of mobile user" is constantly changing its values. We hardly can say that such an attribute is "predictive" in the full meaning of the word, although it has an influence on user's choice. Better to say that this is a "context" attribute. Location and time of the day form the context in which the decision is making by a mobile user. Filtering and profiling techniques for mobile environment should be specified to take into account the difference between predictive and contextual attributes. Now we converge to building of the model that can process separately predictive variables and context ones in order to make more explicit modeling and prediction of user preferences in mobile information systems.
4 Bayesian Metanetworks 4.1 Preference and context attributes Each profiling and filtering task has a set of variables (attributes) that influence in some way the choice or preference (target attribute) of a customer. When a user chooses the best offering among the others, his decision making highly depends on his actual needs as well as on his mood, health, presence of other people around, location, daytime, etc. The typical task in learning Bayesian networks from data is model selection [5]. Each attribute in ordinary Bayesian network has the same status, so they are just combined in possible modelscandidates to encode possible conditional dependencies. Taking into account the features of mobile information systems (Section 3) we suggest to distinguish several classes of attributes: Class 1. Target attributes. Possible members: best offering, type of goods, relevant information, user’s cluster (user group to which he belongs). Characteristics: ordinary target nodes of Bayesian network. Class 2. Predictive attributes. Possible members: personal data about user (age, occupation, gender, etc.), observations of user’s behavior (what he/she has bought before). Characteristics: ordinary nodes of Bayesian network, can form any structure. Class 3. Context attributes. Possible members: user’s current location, current time, current user’s mood. Mobile user’s coordinates together with the knowledge of the surrounding area can produce several location variables (e.g. distance to the nearest hotel, settlement scale – city, town or village, etc.). Depending on user's location different sets of predictive attributes will form the user's preference. Characteristics: these attributes are conditionally independent of predictive variable. But they can be conditionally dependent of other context variables. They influence the dependencies in the predictive model, influence relevance of predictive attributes. Class 4. Metacontext attributes. Possible members: parameters that define relevance of location variables, time variables. Characteristics: these attributes are conditionally independent of predictive and context variables. They influence the dependencies in the contextual model, influence relevance of context attributes. Conceptual model of the domain should be build after the domain analysis and available variables should be put to appropriate classes (predictive,
context or even metacontext). There might be the situation in which some attributes belong to predictive class in one case (one context) and to the context class in another case (another context). Parameters, which define class membership, are also metacontext attributes.
4.2 Definition and Variations of Bayesian Metanetwork Let's define the Bayesian Metanetwork in a similar way as Semantic Metanetwork was defined [8]. Definition. Bayesian Metanetwork is a set of Bayesian networks, which are put on each other in such a way that elements (nodes or conditional dependencies) of every previous probabilistic network depend on the local probability distributions associated with nodes of the next level network. First Variation of a Bayesian Metanetwork Multilevel Modeling of Conditional Dependencies. Let's consider 2-level Bayesian Metanetwork (Fig. 1). Context variables can be considered as a control higher level to the level of network with predictive variables. Contextual level
Predictive level
Fig. 1. Two-level Bayesian Metanetwork for modeling conditional dependencies
Standard Bayesian inference is applied in Bayesian network of each level. The idea of Bayesian Metanetwork is described in Fig.2, Fig.3. P(A)
P(X) A
X
P(Y|X)
P(B|A) B
Y
P(Y)
P(B) P(P(Y|X)|P(B|A))
Fig. 2. The example of Bayesian Metanetwork. The nodes of the 2nd-level network correspond to the conditional probabilities of the 1st-level network P(B|A) and P(Y|X). The arc in the 2nd-level network corresponds to the conditional probability P(P(Y|X)|P(B|A)).
The inference is given in (1): P(Y) = P(X) × P(P(Y|X) | P(B|A)) × × P(B|A)
(1)
First variation of Bayesian Metanetwork can be implemented in a mobile information system as follows. Mobile user’s profile will have predictive and contextual features. Predictive features – learned or defined user's preferences – will be at the basic predictive network level and hey will be used to predict user behavior to be able to push him carefully selected and wanted filtered products and services. Contextual features will be at the control network level. They will be used to predict the conditional dependencies between preference features of the user's profile (the basic network level) according to the current context. a)
A
X
Q B
Y
S
b)
A
2-level Metanetwork can be easily extended for the multilevel (multicontext) Metanetwork [8]. In principle we can assume that Bayesian Metanetwork can have as many levels as necessary. Fig. 4 shows the example of three-level Bayesian Metanetwork. Second Variation of a Bayesian Metanetwork – Modeling Relevant Features Selection. Feature selection methods try to pick a subset of features that are relevant to the target concept. Each of these methods has its strengths and weaknesses based on data types and domain characteristics. As is well known, there is no single feature selection method that can be applied to all applications. The choice of a feature selection method depends on various data set characteristics: data types, data size, and noise [4]. The Bayesian Metanetwork can be a tool for modeling of relevant features selection. Context variables are considered again as a control higher level to the level of network with predictive variables. Values of context variables influence the relevance of the variables in a predictive model as shown in Fig. 5.
R
Contextual level c)
X
Q B
Y
S
Predictive level
R
Fig. 3. The example of Bayesian Metanetwork. The metanetwork (a) actually consists of two Bayesian networks: (b), 1st-level predictive network, and (c), controlling 2nd-level context network, and nodes of network (c) correspond to the arcs of network (b).
Fig. 5. Two-level Bayesian Metanetwork for modeling relevant features selection
In location based services the main contextual feature will be current mobile user's location. When a user changes his location some conditional dependencies between his preferences probably also change.
We consider relevance as a probability of importance of the variable to the inference of target attribute in the given context. In such definition relevance inherits all properties of a probability as shown in Fig.6. Relevance:
a)
A
X
b)
;
Probability:
X
P(X)
Q B
Y
P(Y|X)
