Input online review data and related bias in recommender systems

A majority of extant literature on recommender systems assume the input data as a given to generate recommendations. Both implicit and/or explicit data are used as input in these systems. The existence of various challenges in using such input data including those associated with strategic source manipulations, sparse matrix, state data, among others, are sometimes acknowledged. While such input data are also known to be rife with various forms of bias, to our knowledge no explicit attempt is made to correct or compensate for them in recommender systems. We consider a specific type of bias that is introduced in online product reviews due to the sequence in which these reviews are written. We model several scenarios in this context and study their properties

Knowledge-based decision support system for scheduling in a flexible flow system

Decision Support Systems (DSSs) are necessary resources for most complex decision making situations. The environment under which the DSSs function are, for the most part, evolving and, hence the DSSs should have the capability to adapt themselves as per the changes in the characteristics of the environment. An adaptive DSS that can function in a dynamic environment is proposed in this thesis, incorporating simulation modeling and inductive learning, to improve the overall performance of the system.We develop an adaptive DSS incorporating learning in this thesis. We also attempt to refine the learned knowledge as is stored in the knowledge-base, thus reducing the effects of noise in the training examples on learning. The proposed framework is illustrated by scheduling a flexible flow system (FFS). Example of an application environment representing an FFS is the surface mount technology (SMT) facility used in printed circuit board (PCB) manufacturing. Scheduling in a PCB assembly facility involves decisions to be taken both at part-release and dispatching at machines stages. We develop a bi-level DSS to accomodate these interactions. The performance of the resulting system is shown to improve over systems using just one best heuristic for scheduling.U of I OnlyETDs are only available to UIUC Users without author permissio

Adaptive Framework for Collisions in RFID Tag Identification

Radio Frequency Identification (RFID) is promising, as a technique, to enable tracking of essential information about objects as they pass through supply chains. Information thus tracked can be utilised to efficiently operate the supply chain. Effective management of the supply chain translates to huge competitive advantage for the firms involved. Among several issues that impede seamless integration of RFID tags in a supply chain, one of the problems encountered while reading RFID tags is that of collision, which occurs when multiple tags transmit data to the same receiver slot. Data loss due to collision necessitates re-transmission of lost data. We consider this problem when Framed Slotted ALOHA protocol is used. Using machine learning, we adaptively configure the number of slots per frame to reduce the number of collisions while improving throughput.RFID, collision, framed slotted ALOHA protocol

Production, Manufacturing and Logistics Knowledge-based framework for automated dynamic

Supply chain management has gained renewed interest among researchers in recent years. This is primarily due to the availability of timely information across the various stages of the supply chain, and therefore the need to effectively utilize the information for improved performance. Although information plays a major role in effective functioning of supply chains, there is a paucity of studies that deal specifically with the dynamics of supply chains and how data collected in these systems can be used to improve their performance. In this paper I develop a framework, with machine learning, for automated supply chain configuration. Supply chain configuration used to be mostly a one-shot problem. Once a supply chain is configured, researchers and practitioners were more interested in means to improve performance given that initial configuration. However, recent developments in e-commerce applications and faster communication over the Internet in general necessitates dynamic (re)configuration of supply chains over time to take advantage of better configurations. Using examples, I show performance improvements of the proposed adaptive supply chain configuration framework over static configurations