Jiajin Huang

Using market value functions for targeted marketing data mining

Targeted marketing typically involves the identification of customers or products having potential market values. We propose a linear model for solving this problem by drawing and extending results from information retrieval. It is assumed that each object is represented by values of a finite set of attributes. A market value function, which is a linear combination of utility functions on attribute values, is used to rank objects. Several methods are examined for mining market value functions. The main advantage of the model is that one can rank objects of interest according to their market values, instead of classifying the objects. Both the theoretical and experimental results are reported in this paper. It establishes a basis on which further studies and experimental evaluation can be carried out.

Attribute reduction of rough sets in mining market value functions

The linear model of market value functions is a new method for direct marketing. Just like other methods in direct marketing, attribute reduction is very important to deal with large databases. We apply the algorithm of attribute reduction, which is based on the combination of rough set theory with the boosting algorithm, to the linear model of market value functions. Experimental results compared with the ELSA/ANN model show that the proposed algorithms can be used effectively in the linear model of market value functions.

Relational peculiarity oriented data mining

Peculiarity rules are a new type of interesting rules which can be discovered by searching the relevance among peculiar data. A main task of mining peculiarity rules is the identification of peculiarity. Traditional methods of finding peculiar data are attribute-based approaches. This paper extends peculiarity oriented mining to relational peculiarity oriented mining. Peculiar data are identified on record level, and peculiar rules are mined and explained in a relational mining framework. The results from preliminary experiments show that relational peculiarity oriented mining is very effective.

Cost-sensitive three-way recommendations by learning pair-wise preferences

Recommender systems aim to identify items that a user may like. In this paper, we discuss a three-way decision approach which provides a more meaningful way to recommend items to a user. Besides recommended items and not recommended items, the proposed model adds a set of items that are possibly recommended to users. In the model, we focus on two issues. One is the computation of required thresholds to define the three sets based on the decision-theoretic rough set model. The other is the notion of user preference on the three sets which forms the basis of a ranking strategy, and then a pair-wise preference learning algorithm using gradient descent is adopted for inferring latent vectors for users and items. Working with a sigmoid function of a product of a user and item latent vector, we estimate the probability that the user prefers the item to make recommendations. Experimental results show that the proposed method improves recommendation quality from the cost-sensitive view. The computation of required thresholds to define the three sets based on the decision-theoretic rough set model.The notion of user preference on the three sets which forms the basis of a ranking strategy.A pair-wise learning algorithm to estimate the probability of the user liking the item to make recommendations.

A UNIFIED FRAMEWORK OF TARGETED MARKETING USING CUSTOMER PREFERENCES

One of the fundamental tasks of targeted marketing is to elicit associations between customers and products. Based on the results from information retrieval and utility theory, this article proposes a unified framework of targeted marketing. The customer judgments of products are formally described by preference relations and the connections of customers and products are quantitatively measured by market value functions. Two marketing strategies, known as the customer‐oriented and product‐oriented marketing strategies, are investigated. Four marketing models are introduced and examined. They represent, respectively, the relationships between a group of customers and a group of products, between a group of customers and a single product, between a single customer and a group of products, and between a single customer and a single product. Linear and bilinear market value functions are suggested and studied. The required parameters of a market value function can be estimated by exploring three types of information, namely, customer profiles, product profiles, and transaction data. Experiments on a real‐world data set are performed to demonstrate the effectiveness of the proposed framework.

A Physarum-inspired optimization algorithm for load-shedding problem

Abstract Load-shedding is an intentional reduction approach which can maintain the stability of a microgrid system effectively. Recent studies have shown that a load-shedding problem can be solved by formulating it as a 0/1 knapsack problem (KP). Although approximate solutions of 0/1 KP can be given by ant colony optimization (ACO) algorithms, adopting them requests a delicate consideration of the robustness, convergence rate and premature convergence. This paper proposes a new kind of Physarum-based hybrid optimization algorithm, denoted as PM-ACO, based on the critical paths reserved feature of Physarum-inspired mathematical (PM) model. Through adding additional pheromone to those important items selected by the PM model, PM-ACO improves the selection probability of important items and emerge a positive feedback process to generate optimal solutions. Comparing with other 0/1 KP solving algorithms, our experimental results demonstrate that PM-ACO algorithms have a stronger robustness and a higher convergence rate. Moreover, PM-ACO provides adaptable solutions for the load-shedding problem in a microgrid system.

A Human-Web Interaction Based Trust Model for Trustworthy Web Software Development

Web software systems provide information and service for end users through the Web interface. The interactive process between the user and the software is the process of software systems to perceive the environment and user, to adjust own configuration and provide appropriate services, monitor and eliminate untrustworthy factors, complete own evolvement and achieve trusted result finally. This paper aims to establishing the online trust evolution model and then building the corresponding trustworthy software framework during the human-Web interactive process. Our research will consider the human and software factors together and combine related methodologies and tools for developing trust model in a user-centric way. Our study can provide theoretical and technical support for developing trustworthy Web software systems.

A general framework of targeted marketing

In this paper, inspired by a unified probabilistic model of information retrieval, we propose a general framework of targeted marketing by considering three types of information, namely, the customer profiles, the product profiles, and the transaction databases. The notion of market value functions is introduced, which measure the potential value or profit of marketing a product to a customer. Four sub-models are examined for the estimation of a market value function. Based on market value functions, two targeted marketing strategies, namely, customer-oriented targeted marketing and product-oriented targeted marketing, are suggested. This paper focuses on the conceptual development of the framework. The detailed computation of a market value function and the evaluation of the proposed framework will be reported in another paper.

Adaptive Linear Market Value Functions for Targeted Marketing

This paper presents adaptive linear market value functions to solve the problem of identification of customers having potential market value in targeted marketing. The performance of these methods is compared with some standard data mining methods such as simple Naive Bayes. Experiments on real world data show that the proposed methods are efficient and effective.

A probabilistic inference model for recommender systems

Recommendation is an important application that is employed on the Web. In this paper, we propose a method for recommending items to a user by extending a probabilistic inference model in information retrieval. We regard the user’s preference as the query, an item as a document, and explicit and implicit factors as index terms. Additional information sources can be added to the probabilistic inference model, particularly belief networks. The proposed method also uses the belief network model to recommend items by combining expert information. Experimental results on real-world data sets show that the proposed method can improve recommendation effectiveness.