Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach

User opinions expressed in the form of ratings can influence an individual's view of an item. However, the true quality of an item is often obfuscated by user biases, and it is not obvious from the observed ratings the importance different users place on different aspects of an item. We propose a probabilistic modeling of the observed aspect ratings to infer (i) each user's aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect ratings as ordered discrete data and encode the dependency between different aspects by using a latent Gaussian structure. We handle the Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully Bayesian inference. On two real world datasets, we demonstrate the predictive ability of our model and its effectiveness in learning explainable user biases to provide insights towards a more reliable product quality estimation.Comment: Accepted for publication in IJCAI 201

Weighted Random Walk Sampling for Multi-Relational Recommendation

In the information overloaded web, personalized recommender systems are essential tools to help users find most relevant information. The most heavily-used recommendation frameworks assume user interactions that are characterized by a single relation. However, for many tasks, such as recommendation in social networks, user-item interactions must be modeled as a complex network of multiple relations, not only a single relation. Recently research on multi-relational factorization and hybrid recommender models has shown that using extended meta-paths to capture additional information about both users and items in the network can enhance the accuracy of recommendations in such networks. Most of this work is focused on unweighted heterogeneous networks, and to apply these techniques, weighted relations must be simplified into binary ones. However, information associated with weighted edges, such as user ratings, which may be crucial for recommendation, are lost in such binarization. In this paper, we explore a random walk sampling method in which the frequency of edge sampling is a function of edge weight, and apply this generate extended meta-paths in weighted heterogeneous networks. With this sampling technique, we demonstrate improved performance on multiple data sets both in terms of recommendation accuracy and model generation efficiency

Hierarchical latent factors for preference data

In this work we propose a probabilistic hierarchical generative approach for users' preference data, which is designed to overcome the limitation of current methodologies in Recommender Systems and thus to meet both prediction and recommendation accuracy. The Bayesian Hierarchical User Community Model (BH-UCM) focuses both on modeling the popularity of items and the distribution over item ratings. An extensive evaluation over two popular benchmark datasets shows that the combined modeling of item popularity and rating provides a powerful framework both for rating prediction and for the generation of accurate recommendation lists. Copyright (c) 2012 - Edizioni Libreria Progetto and the authors

Balancing prediction and recommendation accuracy: Hierarchical latent factors for preference data

Recent works in Recommender Systems (RS) have in- vestigated the relationships between the prediction ac- curacy, i.e. the ability of a RS to minimize a cost func- Tion (for instance the RMSE measure) in estimating users' preferences, and the accuracy of the recommenda- Tion list provided to users. State-of-the-art recommen- dation algorithms, which focus on the minimization of RMSE, have shown to achieve weak results from the rec- ommendation accuracy perspective, and vice versa. In this work we present a novel Bayesian probabilistic hi- erarchical approach for users' preference data, which is designed to overcome the limitation of current method- ologies and thus to meet both prediction and recommen- dation accuracy. According to the generative semantics of this technique, each user is modeled as a random mix- Ture over latent factors, which identify users community interests. Each individual user community is then mod- eled as a mixture of topics, which capture the prefer- ences of the members on a set of items. We provide two different formalization of the basic hierarchical model: BH-Forced focuses on rating prediction, while BH-Free models both the popularity of items and the distribu- Tion over item ratings. The combined modeling of item popularity and rating provides a powerful framework for the generation of highly accurate recommendations. An extensive evaluation over two popular benchmark datasets reveals the effectiveness and the quality of the proposed algorithms, showing that BH-Free realizes the most satisfactory compromise between prediction and recommendation accuracy with respect to several state- of-the-art competitors. Copyright © 2012 by the Society for Industrial and Applied Mathematics