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

CoupledCF: Learning explicit and implicit user-item couplings in recommendation for deep collaborative filtering

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users/items as independent while ignoring the rich couplings within and between users and items, leading to limited performance improvement. In reality, users/items are related with various couplings existing within and between users and items, which may better explain how and why a user has personalized preference on an item. This work builds on non-IID learning to propose a neural user-item coupling learning for collaborative filtering, called CoupledCF. CoupledCF jointly learns explicit and implicit couplings within/between users and items w.r.t. user/item attributes and deep features for deep CF recommendation. Empirical results on two real-world large datasets show that CoupledCF significantly outperforms two latest neural recommenders: neural matrix factorization and Google's Wide&Deep network

Dynamics of hadron strong production and decay

We generalize results of lattice QCD to determine the spin-dependent symmetries and factorization properties of meson production in OZI allowed processes. This explains some conjectures previously made in the literature about axial meson decays and gives predictions for exclusive decays of vector charmonia, including ways of establishing the structure of Y(4260) and Y(4325) from their S-wave decays. Factorization gives a selection rule which forbids $e^+e^- \to D^* D_2$ near threshold with the tensor meson in helicity 2. The relations among amplitudes for double charmonia production \e^+e^-\to \psi\chi_{0,1,2} are expected to differ from the analagous relations among light flavour production such as \e^+e^-\to \omega f_{0,1,2}.Comment: 13 pages; journal versio