Incorporating couplings into collaborative filtering

University of Technology Sydney. Faculty of Engineering and Information Technology.Recommender Systems (RS) have been proposed to help users tackle information overload by suggesting potentially interesting items to users. A typical RS usually has a set of users and items with various rating preferences. The key task of RS is to predict an unknown rating or to recommend relevant items to a given user. Many existing recommendation methods such as Collaborative Filtering (CF), Content-based Recommendation, and Hybrid Filtering often assume that users, items and their attributes are identically and independently distributed. In the real world, however, these objects and their attributes are often coupled with each other through explicit or implicit relations. On one hand, users are often connected through social or trust relations, and items are interacted with linkage or citation relations. On the other hand, the attributes of users or items are also more or less coupled with each other. These dependent relations clearly demonstrate that the users, items, and their attributes in RS are not identically and independently distributed (non-IID), which is rarely considered in most existing recommendation methods. The non-IID RS have emerged with the consideration of non-IID characteristics into RS. A main challenge in non-IID RS is to analyse and model the coupling relations between users and between items. In this dissertation, we aim to improve recommendation effectiveness by incorporating the coupling relations into RS. The main contributions of the dissertation are summarized as follows: (1) We propose three novel neighbourhood-based CF methods including coupled user-based CF, coupled item-based CF, and coupled CF. Specifically, we first apply a novel coupled object similarity to compute the coupling relations between users and between items based on their attributes. We then integrate the user and item couplings into the neighbourhood-based CF to produce the proposed methods by inventing new similarity measures. (2) We propose three novel model-based CF methods including coupled user-based matrix factorization (CUMF), coupled item-based matrix factorization (CIMF), and coupled matrix factorization (CMF). CUMF and CIMF respectively integrate the attribute-based user couplings and item couplings into MF, and CMF incorporates the user couplings, item couplings, and the user-item rating matrix together into MF. (3) We propose a two-level matrix factorization recommendation model which integrates the textual semantic couplings between items and the user-item rating matrix together. (4) We conduct experiments to evaluate the effectiveness of incorporating the couplings into non-IID RS

Accurate gradient computations at interfaces using finite element methods

New finite element methods are proposed for elliptic interface problems in one and two dimensions. The main motivation is not only to get an accurate solution but also an accurate first order derivative at the interface (from each side). The key in 1D is to use the idea from \cite{wheeler1974galerkin}. For 2D interface problems, the idea is to introduce a small tube near the interface and introduce the gradient as part of unknowns, which is similar to a mixed finite element method, except only at the interface. Thus the computational cost is just slightly higher than the standard finite element method. We present rigorous one dimensional analysis, which show second order convergence order for both of the solution and the gradient in 1D. For two dimensional problems, we present numerical results and observe second order convergence for the solution, and super-convergence for the gradient at the interface

Pricing Strategies in Dual-channel for Small and Medium-Sized Manufacturers

For the Small and Medium-Sized Manufacturers who are in the weak position in dual-channel marketing, they are often faced with channel price conflict. Therefore, it is necessary for the Small and Medium-Sized Manufacturers to formulate effective pricing strategies. This paper modeled pricing strategies for two scenarios, which involved the manufacturer and retailer make decisions individually and a retailer Stackelberg game. Then we investigated the impacts of digital attribute of product and power structure on the optimal pricing strategies. Besides, we considered the change both of manufacturer and retailer’s profits when the digital attribute of product is heterogeneous and the power structure is difference using computational studies. Our analyses show that, the non-dominant manufacturer decides online direct price according to the dominant retailer’s pricing strategy is a win-win strategy, both manufacturer and retailer are better off, and the dual-channel’s total profits also improve

From field sites to field events

The incorporation of various information and communication technologies (ICTs) in ethnographic methods mandates a reconsideration of the understanding and practice of fieldwork. In this article, we explore how the ‘field site’ may be reimagined in today’s highly mobile, ICT-facilitated world. Based on our research among Indian transnational families and young migrant workers from Malaysia, we argue that the field may be conceived as a collection of ‘field events’ that are co-created by ethnographers, their study participants, and ICTs. As ICTs are increasingly intertwined with people’s lives and thereby feature importantly in ethnographic fieldwork, we encourage ethnographers to carefully consider how these devices and platforms actively shape their ethnographic data as well as their relations with study participants