Interaction-aware Factorization Machines for Recommender Systems

Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named \emph{Interaction-aware Factorization Machine} (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the \emph{feature aspect} and the \emph{field aspect}, to learn flexible interactions on two levels. The feature aspect learns feature interaction importance via an attention network while the field aspect learns the feature interaction effect as a parametric similarity of the feature interaction vector and the corresponding field interaction prototype. IFM introduces more structured control and learns feature interaction importance in a stratified manner, which allows for more leverage in tweaking the interactions on both feature-wise and field-wise levels. Besides, we give a more generalized architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to capture higher-order interactions. To further improve both the performance and efficiency of IFM, a sampling scheme is developed to select interactions based on the field aspect importance. The experimental results from two well-known datasets show the superiority of the proposed models over the state-of-the-art methods

Enhancing Recommendation Interpretability with Tags: A Neural Variational Model

Recommender systems are widely used for assisting consumers finding interested products, and providing suitable explanations for recommendation is particularly important for enhancing consumers’ trust and satisfaction with the system. Tags can be used to annotate different types of items, yet their potential for providing interpretability is not well studied previously. Therefore, it is worthy to study how to leverage tags to enhance recommendation systems in terms of both interpretability and accuracy. This paper proposes a novel model that seamlessly fuse topic model and recommendation model, where the topic model can analyze tags to infer understandable topics, and the recommendation model can conduct accurate and interpretable recommendations based on these topics. We develop variational auto-encoding method to take advantage of neural networks to infer model parameters. Experiments on real-world datasets illustrate that the proposed method can not only achieve great recommendation performance, but also provide interpretability for the recommendation results

Enabling the Analysis of Personality Aspects in Recommender Systems

Existing Recommender Systems mainly focus on exploiting users’ feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI). Personality-based recommender systems have shown a great success to identify similar users based on their personality types. However, there are only a few personality-based recommender systems in the literature which either discover personality explicitly through filling a questionnaire that is a tedious task, or neglect the impact of users’ personal interests and level of knowledge, as a key factor to increase recommendations’ acceptance. Differently, we identifying users’ personality type implicitly with no burden on users and incorporate it along with users’ personal interests and their level of knowledge. Experimental results on a real-world dataset demonstrate the effectiveness of our model, especially in DSW-n-FCI situations

Capturing semantic correlation for item recommendation in tagging systems

The popularity of tagging systems provides a great opportunity to improve the performance of item recommendation. Although existing approaches use topic modeling to mine the semantic information of items by grouping the tags labelled for items, they overlook an important property that tags link users and items as a bridge. Thus these methods cannot deal with the data sparsity without commonly rated items (DS-WO-CRI) problem, limiting their recommendation performance. Towards solving this challenging problem, we propose a novel tag and rating based collaborative filtering (CF) model for item recommendation, which first uses topic modeling to mine the semantic information of tags for each user and for each item respectively, and then incorporates the semantic information into matrix factorization to factorize rating information and to capture the bridging feature of tags and ratings between users and items. As a result, our model captures the semantic correlation between users and items, and is able to greatly improve recommendation performance, especially in DS-WO-CRI situations. Experiments conducted on two popular real-world datasets demonstrate that our proposed model significantly outperforms the conventional CF approach, the state-of-the-art social relation based CF approach, and the state-of-the-art topic modeling based CF approaches in terms of both precision and recall, and it is an effective approach to the DS-WO-CRI problem.7 page(s

Incidence of a semantic correlation model of socioformative data in the evaluation of the curricular planning of a face-to-face classroom subject, 2019.

Se presenta una propuesta denominada “Modelo de correlación semántico de datos socioformativos” (MCS), diseñada para apoyar la evaluación de la planeación curricular (EPC). La EPC se realiza con indicadores derivados de los componentes del currículo como perfil de egreso, plan de estudios, plan de área y plan de asignatura. El MCS, por su parte, procesa los datos obtenidos de un cuestionario sobre percepción socioformativa aplicado previamente a una muestra de la comunidad educativa, lo que permite proyectar un criterio comparativo para los indicadores de la EPC. El mecanismo del MCS está basado en correlaciones semánticas, un método utilizado por el procesamiento del lenguaje natural (PLN) para el cálculo de sentido entre conceptos-palabras. Se propone una investigación aplicada de diseño cuasiexperimental con una muestra de 10 estudiantes asistentes de una asignatura presencial. En el primer momento, los estudiantes evalúan la planeación curricular de la asignatura a partir de un cuestionario compuesto por los indicadores derivados de la EPC. Luego, los estudiantes vuelven a responder al mismo cuestionario, posterior a su participación en una sesión de observación con el MCS. Al final, se contrastan los resultados y se hace una prueba Wilcoxon de muestras relacionadas